1
|
Jeong H, Lim H, Yoon C, Won J, Lee GY, de la Rosa E, Kirschke JS, Kim B, Kim N, Kim C. Robust Ensemble of Two Different Multimodal Approaches to Segment 3D Ischemic Stroke Segmentation Using Brain Tumor Representation Among Multiple Center Datasets. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024:10.1007/s10278-024-01099-6. [PMID: 38693333 DOI: 10.1007/s10278-024-01099-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/23/2023] [Revised: 03/18/2024] [Accepted: 03/22/2024] [Indexed: 05/03/2024]
Abstract
Ischemic stroke segmentation at an acute stage is vital in assessing the severity of patients' impairment and guiding therapeutic decision-making for reperfusion. Although many deep learning studies have shown attractive performance in medical segmentation, it is difficult to use these models trained on public data with private hospitals' datasets. Here, we demonstrate an ensemble model that employs two different multimodal approaches for generalization, a more effective way to perform on external datasets. First, after we jointly train a segmentation model on diffusion-weighted imaging (DWI) and apparent diffusion coefficient (ADC) MR modalities, the model is inferred on the DWI images. Second, a channel-wise segmentation model is trained by concatenating the DWI and ADC images as input, and then is inferred using both MR modalities. Before training with ischemic stroke data, we utilized BraTS 2021, a public brain tumor dataset, for transfer learning. An extensive ablation study evaluates which strategy learns better representations for ischemic stroke segmentation. In our study, nnU-Net well-known for robustness is selected as our baseline model. Our proposed method is evaluated on three different datasets: the Asan Medical Center (AMC) I and II, and the 2022 Ischemic Stroke Lesion Segmentation (ISLES). Our experiments are widely validated over a large, multi-center, and multi-scanner dataset with a huge amount of 846 scans. Not only stroke lesion models can benefit from transfer learning using brain tumor data, but combining the MR modalities using different training schemes also highly improves segmentation performance. The method achieved a top-1 ranking in the ongoing ISLES'22 challenge and performed particularly well on lesion-wise metrics of interest to neuroradiologists, achieving a Dice coefficient of 78.69% and a lesion-wise F1 score of 82.46%. Also, the method was relatively robust on the AMC I (Dice, 60.35%; lesion-wise F1, 68.30%) and II (Dice; 74.12%; lesion-wise F1, 67.53%) datasets in different settings. The high segmentation accuracy of our proposed method could improve radiologists' ability to detect ischemic stroke lesions in MRI images. Our model weights and inference code are available on https://github.com/MDOpx/ISLES22-model-inference .
Collapse
Affiliation(s)
- Hyunsu Jeong
- Graduate School of Artificial Intelligence (GSAI), Department of Electrical Engineering, Medical Science and Engineering, and Medical Device Innovation Center, Convergence IT Engineering, Mechanical Engineering, Pohang University of Science and Technology (POSTECH), Pohang, South Korea
| | - Hyunseok Lim
- Graduate School of Artificial Intelligence (GSAI), Department of Electrical Engineering, Medical Science and Engineering, and Medical Device Innovation Center, Convergence IT Engineering, Mechanical Engineering, Pohang University of Science and Technology (POSTECH), Pohang, South Korea
| | - Chiho Yoon
- Graduate School of Artificial Intelligence (GSAI), Department of Electrical Engineering, Medical Science and Engineering, and Medical Device Innovation Center, Convergence IT Engineering, Mechanical Engineering, Pohang University of Science and Technology (POSTECH), Pohang, South Korea
| | - Jongjun Won
- Department of Medical Science, Asan Medical Center, Asan Medical Institute of Convergence Science and Technology, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Grace Yoojin Lee
- Department of Medical Science, Asan Medical Center, Asan Medical Institute of Convergence Science and Technology, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Ezequiel de la Rosa
- icometrix, Leuven, Belgium
- Department of Informatics, Technical University of Munich, Neuroradiology Munich, Germany
| | - Jan S Kirschke
- Department of Informatics, Technical University of Munich, Neuroradiology Munich, Germany
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum Rechtsder Isar, Technical University of Munich, Munich, Germany
| | - Bumjoon Kim
- Department of Biomedical Engineering, Convergence Medicine, Radiology, Neurology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea.
| | - Namkug Kim
- Department of Biomedical Engineering, Convergence Medicine, Radiology, Neurology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea.
| | - Chulhong Kim
- Graduate School of Artificial Intelligence (GSAI), Department of Electrical Engineering, Medical Science and Engineering, and Medical Device Innovation Center, Convergence IT Engineering, Mechanical Engineering, Pohang University of Science and Technology (POSTECH), Pohang, South Korea.
| |
Collapse
|
2
|
Zvolanek KM, Moore JE, Jarvis K, Moum SJ, Bright MG. Macrovascular blood flow and microvascular cerebrovascular reactivity are regionally coupled in adolescence. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.26.590312. [PMID: 38746187 PMCID: PMC11092525 DOI: 10.1101/2024.04.26.590312] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/16/2024]
Abstract
Cerebrovascular imaging assessments are particularly challenging in adolescent cohorts, where not all modalities are appropriate, and rapid brain maturation alters hemodynamics at both macro- and microvascular scales. In a preliminary sample of healthy adolescents (n=12, 8-25 years), we investigated relationships between 4D flow MRI-derived blood velocity and blood flow in bilateral anterior, middle, and posterior cerebral arteries and BOLD cerebrovascular reactivity in associated vascular territories. As hypothesized, higher velocities in large arteries are associated with an earlier response to a vasodilatory stimulus (cerebrovascular reactivity delay) in the downstream territory. Higher blood flow through these arteries is associated with a larger BOLD response to a vasodilatory stimulus (cerebrovascular reactivity amplitude) in the associated territory. These trends are consistent in a case study of adult moyamoya disease. In our small adolescent cohort, macrovascular-microvascular relationships for velocity/delay and flow/CVR change with age, though underlying mechanisms are unclear. Our work emphasizes the need to better characterize this key stage of human brain development, when cerebrovascular hemodynamics are changing, and standard imaging methods offer limited insight into these processes. We provide important normative data for future comparisons in pathology, where combining macro- and microvascular assessments may better help us prevent, stratify, and treat cerebrovascular disease.
Collapse
|
3
|
Biesbroek JM, Coenen M, DeCarli C, Fletcher EM, Maillard PM, Barkhof F, Barnes J, Benke T, Chen CPLH, Dal‐Bianco P, Dewenter A, Duering M, Enzinger C, Ewers M, Exalto LG, Franzmeier N, Hilal S, Hofer E, Koek HL, Maier AB, McCreary CR, Papma JM, Paterson RW, Pijnenburg YAL, Rubinski A, Schmidt R, Schott JM, Slattery CF, Smith EE, Sudre CH, Steketee RME, Teunissen CE, van den Berg E, van der Flier WM, Venketasubramanian N, Venkatraghavan V, Vernooij MW, Wolters FJ, Xin X, Kuijf HJ, Biessels GJ. Amyloid pathology and vascular risk are associated with distinct patterns of cerebral white matter hyperintensities: A multicenter study in 3132 memory clinic patients. Alzheimers Dement 2024; 20:2980-2989. [PMID: 38477469 PMCID: PMC11032573 DOI: 10.1002/alz.13765] [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: 10/30/2023] [Revised: 01/30/2024] [Accepted: 01/31/2024] [Indexed: 03/14/2024]
Abstract
INTRODUCTION White matter hyperintensities (WMH) are associated with key dementia etiologies, in particular arteriolosclerosis and amyloid pathology. We aimed to identify WMH locations associated with vascular risk or cerebral amyloid-β1-42 (Aβ42)-positive status. METHODS Individual patient data (n = 3,132; mean age 71.5 ± 9 years; 49.3% female) from 11 memory clinic cohorts were harmonized. WMH volumes in 28 regions were related to a vascular risk compound score (VRCS) and Aß42 status (based on cerebrospinal fluid or amyloid positron emission tomography), correcting for age, sex, study site, and total WMH volume. RESULTS VRCS was associated with WMH in anterior/superior corona radiata (B = 0.034/0.038, p < 0.001), external capsule (B = 0.052, p < 0.001), and middle cerebellar peduncle (B = 0.067, p < 0.001), and Aß42-positive status with WMH in posterior thalamic radiation (B = 0.097, p < 0.001) and splenium (B = 0.103, p < 0.001). DISCUSSION Vascular risk factors and Aß42 pathology have distinct signature WMH patterns. This regional vulnerability may incite future studies into how arteriolosclerosis and Aß42 pathology affect the brain's white matter. HIGHLIGHTS Key dementia etiologies may be associated with specific patterns of white matter hyperintensities (WMH). We related WMH locations to vascular risk and cerebral Aβ42 status in 11 memory clinic cohorts. Aβ42 positive status was associated with posterior WMH in splenium and posterior thalamic radiation. Vascular risk was associated with anterior and infratentorial WMH. Amyloid pathology and vascular risk have distinct signature WMH patterns.
Collapse
|
4
|
Richerson WT, Schmit BD, Wolfgram DF. Longitudinal changes in diffusion tensor imaging in hemodialysis patients. Hemodial Int 2024; 28:178-187. [PMID: 38351365 PMCID: PMC11014772 DOI: 10.1111/hdi.13133] [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: 09/21/2023] [Revised: 11/14/2023] [Accepted: 01/24/2024] [Indexed: 03/27/2024]
Abstract
INTRODUCTION Hemodialysis patients have increased white matter and gray matter pathology in the brain relative to controls based on MRI. Diffusion tensor imaging is useful in detecting differences between hemodialysis and controls but has not identified the expected longitudinal decline in hemodialysis patients. In this study we implemented specialized post-processing techniques to reduce noise to detect longitudinal changes in diffusion tensor imaging parameters and evaluated for any association with changes in cognition. METHODS We collected anatomical and diffusion MRIs as well as cognitive testing from in-center hemodialysis patients at baseline and 1 year later. Gray matter thickness, white matter volume, and white matter diffusion tensor imaging parameters were measured to identify longitudinal changes. We analyzed the diffusion tensor imaging parameters by averaging the whole white matter and using a pothole analysis. Eighteen hemodialysis patients were included in the longitudinal analysis and 15 controls were used for the pothole analysis. We used the NIH Toolbox Cognition Battery to assess cognitive performance over the same time frame. FINDINGS Over the course of a year on hemodialysis, we found a decrease in white matter fractional anisotropy across the entire white matter (p < 0.01), and an increase in the number of white matter fractional anisotropy voxels below pothole threshold (p = 0.03). We did not find any relationship between changes in whole brain structural parameters and cognitive performance. DISCUSSION By employing noise reducing techniques, we were able to detect longitudinal changes in diffusion tensor imaging parameters in hemodialysis patients. The fractional anisotropy declines over the year indicate significant decreases in white matter health. However, we did not find that declines in fractional anisotropy was associated with declines in cognitive performance.
Collapse
Affiliation(s)
- Wesley T Richerson
- Department of Biomedical Engineering, Marquette University and Medical College of Wisconsin, Milwaukee, Wisconsin, USA
| | - Brian D Schmit
- Department of Biomedical Engineering, Marquette University and Medical College of Wisconsin, Milwaukee, Wisconsin, USA
| | - Dawn F Wolfgram
- Department of Medicine, Medical College of Wisconsin, Milwaukee, Wisconsin, USA
- Zablocki Veterans Affairs Medical Center, Milwaukee, Wisconsin, USA
| |
Collapse
|
5
|
Tubi MA, Wheeler K, Matsiyevskiy E, Hapenney M, Mack WJ, Chui HC, King K, Thompson PM, Braskie MN. White matter hyperintensity volume modifies the association between CSF vascular inflammatory biomarkers and regional FDG-PET along the Alzheimer's disease continuum. Neurobiol Aging 2023; 132:1-12. [PMID: 37708739 PMCID: PMC10843575 DOI: 10.1016/j.neurobiolaging.2023.08.002] [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: 02/03/2023] [Revised: 07/28/2023] [Accepted: 08/06/2023] [Indexed: 09/16/2023]
Abstract
In older adults with abnormal levels of Alzheimer's disease neuropathology, lower cerebrospinal fluid (CSF) vascular endothelial growth factor (VEGF) levels are associated with lower [¹⁸F]-fluorodeoxyglucose positron emission tomography (FDG-PET) signal, but whether this association is (1) specific to VEGF or broadly driven by vascular inflammation, or (2) modified by vascular risk (e.g., white matter hyperintensities [WMHs]) remains unknown. To address this and build upon our past work, we evaluated whether 5 CSF vascular inflammation biomarkers (vascular cell adhesion molecule 1, VEGF, C-reactive protein, fibrinogen, and von Willebrand factor)-previously associated with CSF amyloid levels-were related to FDG-PET signal and whether WMH volume modified these associations in 158 Alzheimer's Disease Neuroimaging Initiative participants (55-90 years old, 39 cognitively normal, 80 mild cognitive impairment, 39 Alzheimer's disease). We defined regions both by cortical boundary and by the 3 major vascular territories: anterior, middle, and posterior cerebral arteries. We found that WMH volume had interactive effects with CSF biomarkers (VEGF and C-reactive protein) on FDG-PET throughout the cortex in both vascular territories and conventionally defined regions of interest.
Collapse
Affiliation(s)
- Meral A Tubi
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA
| | - Koral Wheeler
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA
| | - Elizabeth Matsiyevskiy
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA
| | - Matthew Hapenney
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA
| | - Wendy J Mack
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Helena C Chui
- Department of Neurology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Kevin King
- Department of Neuroradiology, Barrow Neurological Institute, Phoenix, AZ, USA
| | - Paul M Thompson
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA
| | - Meredith N Braskie
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA.
| |
Collapse
|
6
|
Dang Y, He Y, Zheng D, Wang X, Chen J, Zhou Y. Heritability of cerebral blood flow in adolescent and young adult twins: an arterial spin labeling perfusion imaging study. Cereb Cortex 2023; 33:10624-10633. [PMID: 37615361 DOI: 10.1093/cercor/bhad310] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2023] [Revised: 08/04/2023] [Accepted: 08/06/2023] [Indexed: 08/25/2023] Open
Abstract
Blood perfusion is a fundamental physiological property of all organs and is closely linked to brain metabolism. Genetic factors were reported to have important influences on cerebral blood flow. However, the profile of genetic contributions to cerebral blood flow in adolescents or young adults was underexplored. In this study, we recruited a sample of 65 pairs of same-sex adolescent or young adult twins undergoing resting arterial spin labeling imaging to conduct heritability analyses. Our findings indicate that genetic factors modestly affect cerebral blood flow in adolescents or young adults in the territories of left anterior cerebral artery and right posterior cerebral artery, with the primary contribution being to the frontal regions, cingulate gyrus, and striatum, suggesting a profile of genetic contributions to specific brain regions. Notably, the regions in the left hemisphere demonstrate the highest heritability in most regions examined. These results expand our knowledge of the genetic basis of cerebral blood flow in the developing brain and emphasize the importance of regional analysis in understanding the heritability of cerebral blood flow. Such insights may contribute to our understanding of the underlying genetic mechanism of brain functions and altered cerebral blood flow observed in youths with brain disorders.
Collapse
Affiliation(s)
- Yi Dang
- Magnetic Resonance Imaging Research Center, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China
| | - Yuwen He
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing 100101, China
- Center for Cognitive and Brain Sciences, University of Macau, Macao SAR 999078, China
- Department of Public Health and Medicinal Administration, Faculty of Health Sciences, University of Macau, Macao SAR 999078, China
| | - Dang Zheng
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing 100101, China
- China National Children's Center, Beijing 100035, China
| | - Xiaoming Wang
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing 100101, China
| | - Jie Chen
- CAS Key Laboratory of Mental Health, Institute of Psychology, Beijing 100101, China
| | - Yuan Zhou
- Magnetic Resonance Imaging Research Center, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing 100101, China
- Department of Psychology, University of the Chinese Academy of Sciences, Beijing 100101, China
- The National Clinical Research Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing 100029, China
| |
Collapse
|
7
|
Ingwersen T, Olma MC, Schlemm E, Mayer C, Cheng B, Tütüncü S, Kirchhof P, Veltkamp R, Röther J, Laufs U, Nabavi DG, Ntaios G, Endres M, Haeusler KG, Thomalla G. Independent external validation of a stroke recurrence score in patients with embolic stroke of undetermined source. Neurol Res Pract 2023; 5:51. [PMID: 37794453 PMCID: PMC10552210 DOI: 10.1186/s42466-023-00279-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Accepted: 08/24/2023] [Indexed: 10/06/2023] Open
Abstract
BACKGROUND Embolic stroke of undetermined source (ESUS) accounts for a substantial proportion of ischaemic strokes. A stroke recurrence score has been shown to predict the risk of recurrent stroke in patients with ESUS based on a combination of clinical and imaging features. This study aimed to externally validate the performance of the ESUS recurrence score using data from a randomized controlled trial. METHODS The validation dataset consisted of eligible stroke patients with available magnetic resonance imaging (MRI) data enrolled in the PreDAFIS sub-study of the MonDAFIS study. The score was calculated using three variables: age (1 point per decade after 35 years), presence of white matter hyperintensities (2 points), and multiterritorial ischaemic stroke (3 points). Patients were assigned to risk groups as described in the original publication. The model was evaluated using standard discrimination and calibration methods. RESULTS Of the 1054 patients, 241 (22.9%) were classified as ESUS. Owing to insufficient MRI quality, three patients were excluded, leaving 238 patients (median age 65.5 years [IQR 20.75], 39% female) for analysis. Of these, 30 (13%) patients experienced recurrent ischaemic stroke or transient ischemic attack (TIA) during a follow-up period of 383 patient-years, corresponding to an incidence rate of 7.8 per 100 patient-years (95% CI 5.3-11.2). Patients with an ESUS recurrence score value of ≥ 7 had a 2.46 (hazard ratio (HR), 95% CI 1.02-5.93) times higher risk of stroke recurrence than patients with a score of 0-4. The cumulative probability of stroke recurrence in the low-(0-4), intermediate-(5-6), and high-risk group (≥ 7) was 9%, 13%, and 23%, respectively (log-rank test, χ2 = 4.2, p = 0.1). CONCLUSIONS This external validation of a published scoring system supports a threshold of ≥ 7 for identifying ESUS patients at high-risk of stroke recurrence. However, further adjustments may be required to improve the model's performance in independent cohorts. The use of risk scores may be helpful in guiding extended diagnostics and further trials on secondary prevention in patients with ESUS. TRIAL REGISTRATION Clinical Trials, NCT02204267. Registered 30 July 2014, https://clinicaltrials.gov/ct2/show/NCT02204267 .
Collapse
Affiliation(s)
- Thies Ingwersen
- Department of Neurology, University Medical Centre Hamburg-Eppendorf (UKE), Hamburg, Germany.
| | - Manuel C Olma
- Center for Stroke Research Berlin, Charité Universitätsmedizin Berlin, Berlin, Germany
- Berlin Institute of Health, BIH, Berlin, Germany
| | - Eckhard Schlemm
- Department of Neurology, University Medical Centre Hamburg-Eppendorf (UKE), Hamburg, Germany
| | - Carola Mayer
- Department of Neurology, University Medical Centre Hamburg-Eppendorf (UKE), Hamburg, Germany
| | - Bastian Cheng
- Department of Neurology, University Medical Centre Hamburg-Eppendorf (UKE), Hamburg, Germany
| | - Serdar Tütüncü
- Center for Stroke Research Berlin, Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Paulus Kirchhof
- Department of Cardiology, University Heart and Vascular Center Hamburg, Hamburg, Germany
- Institute of Cardiovascular Sciences, University of Birmingham, Birmingham, UK
- Partner Site Hamburg/Kiel/Lübeck, German Centre for Cardiovascular Research, Hamburg, Germany
| | - Roland Veltkamp
- Department of Neurology, Alfried Krupp Hospital, Essen, Germany
- Department of Brain Sciences, Imperial College London, London, UK
| | - Joachim Röther
- Department of Neurology, Asklepios Hospital Altona, Hamburg, Germany
| | - Ulrich Laufs
- Department of Cardiology, University Hospital Leipzig, Hamburg, Germany
| | - Darius G Nabavi
- Department of Neurology, Vivantes Hospital Neukölln, Berlin, Germany
| | - George Ntaios
- Department of Internal Medicine, School of Health Sciences, University of Thessaly, Larissa, Greece
| | - Matthias Endres
- Center for Stroke Research Berlin, Charité Universitätsmedizin Berlin, Berlin, Germany
- Berlin Institute of Health, BIH, Berlin, Germany
- Partner Site Hamburg/Kiel/Lübeck, German Centre for Cardiovascular Research, Hamburg, Germany
- Partner Site Berlin, German Centre for Neurodegenerative Diseases (DZNE), Berlin, Germany
- Excellence Cluster NeuroCure, Berlin, Germany
- Department of Neurology with Experimental Neurology, Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Karl Georg Haeusler
- Department of Neurology, Universitätsklinikum Würzburg (UKW), Würzburg, Germany
| | - Götz Thomalla
- Department of Neurology, University Medical Centre Hamburg-Eppendorf (UKE), Hamburg, Germany
| |
Collapse
|
8
|
Botz J, Lohner V, Schirmer MD. Spatial patterns of white matter hyperintensities: a systematic review. Front Aging Neurosci 2023; 15:1165324. [PMID: 37251801 PMCID: PMC10214839 DOI: 10.3389/fnagi.2023.1165324] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Accepted: 04/24/2023] [Indexed: 05/31/2023] Open
Abstract
Background White matter hyperintensities are an important marker of cerebral small vessel disease. This disease burden is commonly described as hyperintense areas in the cerebral white matter, as seen on T2-weighted fluid attenuated inversion recovery magnetic resonance imaging data. Studies have demonstrated associations with various cognitive impairments, neurological diseases, and neuropathologies, as well as clinical and risk factors, such as age, sex, and hypertension. Due to their heterogeneous appearance in location and size, studies have started to investigate spatial distributions and patterns, beyond summarizing this cerebrovascular disease burden in a single metric-its volume. Here, we review the evidence of association of white matter hyperintensity spatial patterns with its risk factors and clinical diagnoses. Design/methods We performed a systematic review in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) Statement. We used the standards for reporting vascular changes on neuroimaging criteria to construct a search string for literature search on PubMed. Studies written in English from the earliest records available until January 31st, 2023, were eligible for inclusion if they reported on spatial patterns of white matter hyperintensities of presumed vascular origin. Results A total of 380 studies were identified by the initial literature search, of which 41 studies satisfied the inclusion criteria. These studies included cohorts based on mild cognitive impairment (15/41), Alzheimer's disease (14/41), Dementia (5/41), Parkinson's disease (3/41), and subjective cognitive decline (2/41). Additionally, 6 of 41 studies investigated cognitively normal, older cohorts, two of which were population-based, or other clinical findings such as acute ischemic stroke or reduced cardiac output. Cohorts ranged from 32 to 882 patients/participants [median cohort size 191.5 and 51.6% female (range: 17.9-81.3%)]. The studies included in this review have identified spatial heterogeneity of WMHs with various impairments, diseases, and pathologies as well as with sex and (cerebro)vascular risk factors. Conclusion The results show that studying white matter hyperintensities on a more granular level might give a deeper understanding of the underlying neuropathology and their effects. This motivates further studies examining the spatial patterns of white matter hyperintensities.
Collapse
Affiliation(s)
- Jonas Botz
- Computational Neuroradiology, Department of Neuroradiology, University Hospital Bonn, Bonn, Germany
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin, Germany
| | - Valerie Lohner
- Cardiovascular Epidemiology of Aging, Department of Cardiology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Markus D. Schirmer
- Computational Neuroradiology, Department of Neuroradiology, University Hospital Bonn, Bonn, Germany
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
| |
Collapse
|
9
|
Hernandez Petzsche MR, de la Rosa E, Hanning U, Wiest R, Valenzuela W, Reyes M, Meyer M, Liew SL, Kofler F, Ezhov I, Robben D, Hutton A, Friedrich T, Zarth T, Bürkle J, Baran TA, Menze B, Broocks G, Meyer L, Zimmer C, Boeckh-Behrens T, Berndt M, Ikenberg B, Wiestler B, Kirschke JS. ISLES 2022: A multi-center magnetic resonance imaging stroke lesion segmentation dataset. Sci Data 2022; 9:762. [PMID: 36496501 PMCID: PMC9741583 DOI: 10.1038/s41597-022-01875-5] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2022] [Accepted: 11/28/2022] [Indexed: 12/13/2022] Open
Abstract
Magnetic resonance imaging (MRI) is an important imaging modality in stroke. Computer based automated medical image processing is increasingly finding its way into clinical routine. The Ischemic Stroke Lesion Segmentation (ISLES) challenge is a continuous effort to develop and identify benchmark methods for acute and sub-acute ischemic stroke lesion segmentation. Here we introduce an expert-annotated, multicenter MRI dataset for segmentation of acute to subacute stroke lesions ( https://doi.org/10.5281/zenodo.7153326 ). This dataset comprises 400 multi-vendor MRI cases with high variability in stroke lesion size, quantity and location. It is split into a training dataset of n = 250 and a test dataset of n = 150. All training data is publicly available. The test dataset will be used for model validation only and will not be released to the public. This dataset serves as the foundation of the ISLES 2022 challenge ( https://www.isles-challenge.org/ ) with the goal of finding algorithmic methods to enable the development and benchmarking of automatic, robust and accurate segmentation methods for ischemic stroke.
Collapse
Affiliation(s)
- Moritz R. Hernandez Petzsche
- grid.6936.a0000000123222966Department of Diagnostic and Interventional Neuroradiology, Klinikum rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany
| | - Ezequiel de la Rosa
- grid.435381.8icometrix, Leuven, Belgium ,grid.6936.a0000000123222966Department of Informatics, Technical University of Munich, Munich, Germany
| | - Uta Hanning
- grid.13648.380000 0001 2180 3484Department of Diagnostic and Interventional Neuroradiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Roland Wiest
- grid.5734.50000 0001 0726 5157Institute of Diagnostic and Interventional Neuroradiology, University of Bern, Bern, Switzerland
| | - Waldo Valenzuela
- grid.5734.50000 0001 0726 5157Institute of Diagnostic and Interventional Neuroradiology, University of Bern, Bern, Switzerland
| | - Mauricio Reyes
- grid.5734.50000 0001 0726 5157ARTORG Center for Biomedical Engineering Research, Univ. of Bern, Bern, Switzerland
| | | | - Sook-Lei Liew
- grid.42505.360000 0001 2156 6853Chan Division of Occupational Science and Occupational Therapy, University of Southern California, Los Angeles, CA USA
| | - Florian Kofler
- grid.6936.a0000000123222966Department of Diagnostic and Interventional Neuroradiology, Klinikum rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany ,grid.6936.a0000000123222966Department of Informatics, Technical University of Munich, Munich, Germany ,grid.6936.a0000000123222966TranslaTUM – Central Institute for Translational Cancer Research, Technical University of Munich, Munich, Germany ,Helmholtz AI, Helmholtz Zentrum Munich, Munich, Germany
| | - Ivan Ezhov
- grid.6936.a0000000123222966Department of Informatics, Technical University of Munich, Munich, Germany ,grid.6936.a0000000123222966TranslaTUM – Central Institute for Translational Cancer Research, Technical University of Munich, Munich, Germany
| | | | - Alexandre Hutton
- grid.42505.360000 0001 2156 6853Chan Division of Occupational Science and Occupational Therapy, University of Southern California, Los Angeles, CA USA
| | - Tassilo Friedrich
- grid.6936.a0000000123222966Department of Diagnostic and Interventional Neuroradiology, Klinikum rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany
| | - Teresa Zarth
- grid.6936.a0000000123222966Department of Diagnostic and Interventional Neuroradiology, Klinikum rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany
| | - Johannes Bürkle
- grid.6936.a0000000123222966Department of Diagnostic and Interventional Neuroradiology, Klinikum rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany
| | - The Anh Baran
- grid.6936.a0000000123222966Department of Diagnostic and Interventional Neuroradiology, Klinikum rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany
| | - Björn Menze
- grid.6936.a0000000123222966Department of Informatics, Technical University of Munich, Munich, Germany ,grid.7400.30000 0004 1937 0650Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland
| | - Gabriel Broocks
- grid.13648.380000 0001 2180 3484Department of Diagnostic and Interventional Neuroradiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Lukas Meyer
- grid.13648.380000 0001 2180 3484Department of Diagnostic and Interventional Neuroradiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Claus Zimmer
- grid.6936.a0000000123222966Department of Diagnostic and Interventional Neuroradiology, Klinikum rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany
| | - Tobias Boeckh-Behrens
- grid.6936.a0000000123222966Department of Diagnostic and Interventional Neuroradiology, Klinikum rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany
| | - Maria Berndt
- grid.6936.a0000000123222966Department of Diagnostic and Interventional Neuroradiology, Klinikum rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany
| | - Benno Ikenberg
- grid.6936.a0000000123222966Department of Neurology, Klinikum rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany
| | - Benedikt Wiestler
- grid.6936.a0000000123222966Department of Diagnostic and Interventional Neuroradiology, Klinikum rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany ,grid.6936.a0000000123222966TranslaTUM – Central Institute for Translational Cancer Research, Technical University of Munich, Munich, Germany
| | - Jan S. Kirschke
- grid.6936.a0000000123222966Department of Diagnostic and Interventional Neuroradiology, Klinikum rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany ,grid.6936.a0000000123222966TranslaTUM – Central Institute for Translational Cancer Research, Technical University of Munich, Munich, Germany
| |
Collapse
|
10
|
Phuah CL, Chen Y, Strain JF, Yechoor N, Laurido-Soto OJ, Ances BM, Lee JM. Association of Data-Driven White Matter Hyperintensity Spatial Signatures With Distinct Cerebral Small Vessel Disease Etiologies. Neurology 2022; 99:e2535-e2547. [PMID: 36123127 PMCID: PMC9754646 DOI: 10.1212/wnl.0000000000201186] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Accepted: 07/15/2022] [Indexed: 11/15/2022] Open
Abstract
BACKGROUND AND OBJECTIVES Topographical distribution of white matter hyperintensities (WMH) are hypothesized to vary by cerebrovascular risk factors. We used an unbiased pattern discovery approach to identify distinct WMH spatial patterns and investigate their association with different WMH etiologies. METHODS We performed a cross-sectional study on participants of the Alzheimer's Disease Neuroimaging Initiative (ADNI) to identify spatially distinct WMH distribution patterns using voxel-based spectral clustering analysis of aligned WMH probability maps. We included all participants from the ADNI Grand Opportunity/ADNI 2 study with available baseline 2D-FLAIR MRI scans, without history of stroke or presence of infarction on imaging. We evaluated the associations of these WMH spatial patterns with vascular risk factors, amyloid-β PET, and imaging biomarkers of cerebral amyloid angiopathy (CAA), characterizing different forms of cerebral small vessel disease (CSVD) using multivariable regression. We also used linear regression models to investigate whether WMH spatial distribution influenced cognitive impairment. RESULTS We analyzed MRI scans of 1,046 ADNI participants with mixed vascular and amyloid-related risk factors (mean age 72.9, 47.7% female, 31.4% hypertensive, 48.3% with abnormal amyloid PET). We observed unbiased partitioning of WMH into 5 unique spatial patterns: deep frontal, periventricular, juxtacortical, parietal, and posterior. Juxtacortical WMH were independently associated with probable CAA, deep frontal WMH were associated with risk factors for arteriolosclerosis (hypertension and diabetes), and parietal WMH were associated with brain amyloid accumulation, consistent with an Alzheimer disease (AD) phenotype. Juxtacortical, deep frontal, and parietal WMH spatial patterns were associated with cognitive impairment. Periventricular and posterior WMH spatial patterns were unrelated to any disease phenotype or cognitive decline. DISCUSSION Data-driven WMH spatial patterns reflect discrete underlying etiologies including arteriolosclerosis, CAA, AD, and normal aging. Global measures of WMH volume may miss important spatial distinctions. WMH spatial signatures may serve as etiology-specific imaging markers, helping to resolve WMH heterogeneity, identify the dominant underlying pathologic process, and improve prediction of clinical-relevant trajectories that influence cognitive decline.
Collapse
Affiliation(s)
- Chia-Ling Phuah
- From the Department of Neurology (C.-L.P., Y.C., J.F.S., N.Y., O.J.L.-S., B.M.A., J.-M.L.), Washington University School of Medicine & Barnes-Jewish Hospital, St. Louis, MO; NeuroGenomics and Informatics Center (C.-L.P.), Washington University School of Medicine, St. Louis, MO; Mallinckrodt Institute of Radiology (J.-M.L.), Washington University School of Medicine, St. Louis, MO; and Department of Biomedical Engineering (J.-M.L.), Washington University School of Medicine, St. Louis, MO
| | - Yasheng Chen
- From the Department of Neurology (C.-L.P., Y.C., J.F.S., N.Y., O.J.L.-S., B.M.A., J.-M.L.), Washington University School of Medicine & Barnes-Jewish Hospital, St. Louis, MO; NeuroGenomics and Informatics Center (C.-L.P.), Washington University School of Medicine, St. Louis, MO; Mallinckrodt Institute of Radiology (J.-M.L.), Washington University School of Medicine, St. Louis, MO; and Department of Biomedical Engineering (J.-M.L.), Washington University School of Medicine, St. Louis, MO
| | - Jeremy F Strain
- From the Department of Neurology (C.-L.P., Y.C., J.F.S., N.Y., O.J.L.-S., B.M.A., J.-M.L.), Washington University School of Medicine & Barnes-Jewish Hospital, St. Louis, MO; NeuroGenomics and Informatics Center (C.-L.P.), Washington University School of Medicine, St. Louis, MO; Mallinckrodt Institute of Radiology (J.-M.L.), Washington University School of Medicine, St. Louis, MO; and Department of Biomedical Engineering (J.-M.L.), Washington University School of Medicine, St. Louis, MO
| | - Nirupama Yechoor
- From the Department of Neurology (C.-L.P., Y.C., J.F.S., N.Y., O.J.L.-S., B.M.A., J.-M.L.), Washington University School of Medicine & Barnes-Jewish Hospital, St. Louis, MO; NeuroGenomics and Informatics Center (C.-L.P.), Washington University School of Medicine, St. Louis, MO; Mallinckrodt Institute of Radiology (J.-M.L.), Washington University School of Medicine, St. Louis, MO; and Department of Biomedical Engineering (J.-M.L.), Washington University School of Medicine, St. Louis, MO
| | - Osvaldo J Laurido-Soto
- From the Department of Neurology (C.-L.P., Y.C., J.F.S., N.Y., O.J.L.-S., B.M.A., J.-M.L.), Washington University School of Medicine & Barnes-Jewish Hospital, St. Louis, MO; NeuroGenomics and Informatics Center (C.-L.P.), Washington University School of Medicine, St. Louis, MO; Mallinckrodt Institute of Radiology (J.-M.L.), Washington University School of Medicine, St. Louis, MO; and Department of Biomedical Engineering (J.-M.L.), Washington University School of Medicine, St. Louis, MO
| | - Beau M Ances
- From the Department of Neurology (C.-L.P., Y.C., J.F.S., N.Y., O.J.L.-S., B.M.A., J.-M.L.), Washington University School of Medicine & Barnes-Jewish Hospital, St. Louis, MO; NeuroGenomics and Informatics Center (C.-L.P.), Washington University School of Medicine, St. Louis, MO; Mallinckrodt Institute of Radiology (J.-M.L.), Washington University School of Medicine, St. Louis, MO; and Department of Biomedical Engineering (J.-M.L.), Washington University School of Medicine, St. Louis, MO
| | - Jin-Moo Lee
- From the Department of Neurology (C.-L.P., Y.C., J.F.S., N.Y., O.J.L.-S., B.M.A., J.-M.L.), Washington University School of Medicine & Barnes-Jewish Hospital, St. Louis, MO; NeuroGenomics and Informatics Center (C.-L.P.), Washington University School of Medicine, St. Louis, MO; Mallinckrodt Institute of Radiology (J.-M.L.), Washington University School of Medicine, St. Louis, MO; and Department of Biomedical Engineering (J.-M.L.), Washington University School of Medicine, St. Louis, MO.
| |
Collapse
|
11
|
van Veluw SJ, Barkhof F, Schirmer MD. White Matter Hyperintensity Spatial Patterns Provide Clues About Underlying Disease: Location Matters! Neurology 2022; 99:1017-1018. [PMID: 36123129 DOI: 10.1212/wnl.0000000000201398] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Accepted: 08/30/2022] [Indexed: 11/15/2022] Open
Affiliation(s)
- Susanne J van Veluw
- From the Department of Neurology (S.J.v.V., M.D.S.), Massachusetts General Hospital/Harvard Medical School, Boston, MA; Department of Radiology and Nuclear Medicine (F.B.), Amsterdam University Medical Center, Amsterdam Neuroscience, Amsterdam, The Netherlands; and Queen Square Institute of Neurology and Centre for Medical Image Computing (F.B.), University College London, UK.
| | - Frederik Barkhof
- From the Department of Neurology (S.J.v.V., M.D.S.), Massachusetts General Hospital/Harvard Medical School, Boston, MA; Department of Radiology and Nuclear Medicine (F.B.), Amsterdam University Medical Center, Amsterdam Neuroscience, Amsterdam, The Netherlands; and Queen Square Institute of Neurology and Centre for Medical Image Computing (F.B.), University College London, UK
| | - Markus D Schirmer
- From the Department of Neurology (S.J.v.V., M.D.S.), Massachusetts General Hospital/Harvard Medical School, Boston, MA; Department of Radiology and Nuclear Medicine (F.B.), Amsterdam University Medical Center, Amsterdam Neuroscience, Amsterdam, The Netherlands; and Queen Square Institute of Neurology and Centre for Medical Image Computing (F.B.), University College London, UK
| |
Collapse
|
12
|
Amemiya S, Takao H, Watanabe Y, Takei N, Ueyama T, Kato S, Miyawaki S, Koizumi S, Abe O, Saito N. Reliability and Sensitivity to Longitudinal CBF Changes in Steno-Occlusive Diseases: ASL Versus 123 I-IMP-SPECT. J Magn Reson Imaging 2022; 55:1723-1732. [PMID: 34780101 DOI: 10.1002/jmri.27996] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2021] [Revised: 11/04/2021] [Accepted: 11/04/2021] [Indexed: 12/31/2022] Open
Abstract
BACKGROUND Noninvasive cerebral blood flow (CBF) monitoring using arterial spin labeling (ASL) magnetic resonance imaging is useful for managing large cerebral artery steno-occlusive diseases. However, knowledge about its measurement characteristics in comparison with reference standard perfusion imaging is limited. PURPOSE To evaluate perfusion in a longitudinal manner in patients with steno-occlusive disease using ASL and compare with single-photon emission computed tomography (SPECT). STUDY TYPE Prospective. POPULATION Moyamoya (n = 10, eight females) and atherosclerotic diseases (n = 2, two males). FIELD STRENGTH/SEQUENCE 3.0 T; gradient-echo three-dimensional T1 -weighted and spin-echo ASL. ASSESSMENT Multi-delay ASL and [123 I]-iodoamphetamine SPECT CBF measurements were performed both before and within 9 days of anterior-circulation revascularization. Reliability and sensitivity to whole-brain voxel-wise CBF changes (ΔCBF) and their postlabeling delay (PLD) dependency with varied PLDs (in milliseconds) of 1000, 2333, and 3666 were examined. STATISTICAL TESTS Reliability and sensitivity to ΔCBF were examined using within-subject standard deviation (Sw) and intraclass correlation coefficients (ICCs). For statistical comparisons, standard deviation of longitudinal ΔCBF within the hemisphere contralateral to surgery, and the ratio between it and average ΔCBF within the ipsilateral regions of interest were subjected to paired t tests, respectively. P < 0.05 was considered statistically significant. RESULTS ASL test-retest time interval was 31 ± 18 days. Test-retest reliability was significantly lower for SPECT (0.16 ± 0.02) than ASL (0.13 ± 0.04). Sensitivity to postoperative changes was significantly higher for ASL (2.71 ± 2.79) than SPECT (0.27 ± 0.62). Test-retest reliability was significantly higher for a PLD of 2333 (0.13 ± 0.04) than 3666 (0.19 ± 0.05), and sensitivity to ΔCBF was significantly higher for PLDs of 1000 (2.53 ± 2.50) and 2333 than 3666 (0.79 ± 1.88). ICC maps also showed higher reliability for ASL than SPECT. DATA CONCLUSION Higher test-retest reliability led to better ASL sensitivity than SPECT for postoperative ΔCBF. ASL test-retest reliability and sensitivity to ΔCBF were higher with a PLD of 2333. LEVEL OF EVIDENCE 1 TECHNICAL EFFICACY: Stage 2.
Collapse
Affiliation(s)
- Shiori Amemiya
- Department of Radiology, Graduate School of Medicine, University of Tokyo, Tokyo, Japan
| | - Hidemasa Takao
- Department of Radiology, Graduate School of Medicine, University of Tokyo, Tokyo, Japan
| | - Yusuke Watanabe
- Department of Radiology, Graduate School of Medicine, University of Tokyo, Tokyo, Japan
| | - Naoyuki Takei
- MR Applications and Workflow, GE Healthcare, Tokyo, Japan
| | - Tsuyoshi Ueyama
- Department of Radiology, The University of Tokyo Hospital, Tokyo, Japan
| | - Seiji Kato
- Department of Radiology, The University of Tokyo Hospital, Tokyo, Japan
| | - Satoru Miyawaki
- Department of Neurosurgery, Graduate School of Medicine, University of Tokyo, Tokyo, Japan
| | - Satoshi Koizumi
- Department of Neurosurgery, Graduate School of Medicine, University of Tokyo, Tokyo, Japan
| | - Osamu Abe
- Department of Radiology, Graduate School of Medicine, University of Tokyo, Tokyo, Japan
| | - Nobuhito Saito
- Department of Neurosurgery, Graduate School of Medicine, University of Tokyo, Tokyo, Japan
| |
Collapse
|
13
|
Tavazzi E, Bergsland N, Pirastru A, Pelizzari L, Cazzoli M, Saibene FL, Navarro JS, Farina E, Comanducci A, Cecconi P, Baglio F. Brain plasticity after rehabilitation in a severe case of artery of Percheron stroke assessed with multimodal MR imaging. Neurocase 2022; 28:194-198. [PMID: 35465838 DOI: 10.1080/13554794.2022.2062249] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
Artery of Percheron (AOP) stroke is a rare event. We describe an AOP stroke involving both thalami and the midbrain, resulting in a multifunctional clinical impairment. Intensive inpatient multidisciplinary rehabilitation favored the recovery of motor deficits, together with the improvement of cognitive dysfunctions. MRI assessment in the chronic post-stroke phase showed structural and functional reorganization in response to the extended thalamic tissue damage and absence of revascularization. Thalamo-cortical networks involving frontal and prefrontal regions, as well as parietal areas were disrupted, whereas increased functional thalamo-occipital connectivity was found. This report sheds light on brain reorganization following AOP stroke after rehabilitation..
Collapse
Affiliation(s)
- E Tavazzi
- IRCCS, Fondazione Don Carlo Gnocchi ONLUS, Milan, Italy.,Department of Neurology, Buffalo Neuroimaging Analysis Center, School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY, United States
| | - N Bergsland
- IRCCS, Fondazione Don Carlo Gnocchi ONLUS, Milan, Italy.,Department of Neurology, Buffalo Neuroimaging Analysis Center, School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY, United States
| | - A Pirastru
- IRCCS, Fondazione Don Carlo Gnocchi ONLUS, Milan, Italy
| | - L Pelizzari
- IRCCS, Fondazione Don Carlo Gnocchi ONLUS, Milan, Italy
| | - M Cazzoli
- IRCCS, Fondazione Don Carlo Gnocchi ONLUS, Milan, Italy
| | - F L Saibene
- IRCCS, Fondazione Don Carlo Gnocchi ONLUS, Milan, Italy
| | - J S Navarro
- IRCCS, Fondazione Don Carlo Gnocchi ONLUS, Milan, Italy
| | - E Farina
- IRCCS, Fondazione Don Carlo Gnocchi ONLUS, Milan, Italy
| | - A Comanducci
- IRCCS, Fondazione Don Carlo Gnocchi ONLUS, Milan, Italy
| | - P Cecconi
- IRCCS, Fondazione Don Carlo Gnocchi ONLUS, Milan, Italy
| | - F Baglio
- IRCCS, Fondazione Don Carlo Gnocchi ONLUS, Milan, Italy
| |
Collapse
|
14
|
Jiménez-Balado J, Corlier F, Habeck C, Stern Y, Eich T. Effects of white matter hyperintensities distribution and clustering on late-life cognitive impairment. Sci Rep 2022; 12:1955. [PMID: 35121804 PMCID: PMC8816933 DOI: 10.1038/s41598-022-06019-8] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2021] [Accepted: 01/20/2022] [Indexed: 11/29/2022] Open
Abstract
White matter hyperintensities (WMH) are a key hallmark of subclinical cerebrovascular disease and are known to impair cognition. Here, we parcellated WMH using a novel system that segments WMH based on both lobar regions and distance from the ventricles, dividing the brain into a coordinate system composed of 36 distinct parcels (‘bullseye’ parcellation), and then investigated the effect of distribution on cognition using two different analytic approaches. Data from a well characterized sample of healthy older adults (58 to 84 years) who were free of dementia were included. Cognition was evaluated using 12 computerized tasks, factored onto 4 indices representing episodic memory, speed of processing, fluid reasoning and vocabulary. We first assessed the distribution of WMH according to the bullseye parcellation and tested the relationship between WMH parcellations and performance across the four cognitive domains. Then, we used a data-driven approach to derive latent variables within the WMH distribution, and tested the relation between these latent components and cognitive function. We observed that different, well-defined cognitive constructs mapped to specific WMH distributions. Speed of processing was correlated with WMH in the frontal lobe, while in the case of episodic memory, the relationship was more ubiquitous, involving most of the parcellations. A principal components analysis revealed that the 36 bullseye regions factored onto 3 latent components representing the natural aggrupation of WMH: fronto-parietal periventricular (WMH principally in the frontal and parietal lobes and basal ganglia, especially in the periventricular region); occipital; and temporal and juxtacortical WMH (involving WMH in the temporal lobe, and at the juxtacortical region from frontal and parietal lobes). We found that fronto-parietal periventricular and temporal & juxtacortical WMH were independently associated with speed of processing and episodic memory, respectively. These results indicate that different cognitive impairment phenotypes might present with specific WMH distributions. Additionally, our study encourages future research to consider WMH classifications using parcellations systems other than periventricular and deep localizations.
Collapse
Affiliation(s)
- Joan Jiménez-Balado
- Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA, USA
| | - Fabian Corlier
- Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA, USA
| | - Christian Habeck
- Department of Neurology, Taub Institute for Research on Alzheimer's Disease and the Aging Brain, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY, USA
| | - Yaakov Stern
- Department of Neurology, Taub Institute for Research on Alzheimer's Disease and the Aging Brain, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY, USA
| | - Teal Eich
- Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA, USA.
| |
Collapse
|
15
|
Altermatt A, Sinnecker T, Aeschbacher S, Springer A, Coslovsky M, Beer J, Moschovitis G, Auricchio A, Fischer U, Aubert CE, Kühne M, Conen D, Osswald S, Bonati LH, Wuerfel J. Right Hemispheric Predominance of Brain Infarcts in Atrial Fibrillation: A Lesion Mapping Analysis. J Stroke 2022; 24:156-159. [PMID: 35135070 PMCID: PMC8829476 DOI: 10.5853/jos.2021.03531] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2021] [Accepted: 01/11/2022] [Indexed: 11/23/2022] Open
Affiliation(s)
- Anna Altermatt
- Medical Image Analysis Center (MIAC AG), Basel, Switzerland
- Department of Biomedical Engineering, University of Basel, Basel, Switzerland
| | - Tim Sinnecker
- Medical Image Analysis Center (MIAC AG), Basel, Switzerland
- Department of Neurology, University Hospital Basel, Basel, Switzerland
| | - Stefanie Aeschbacher
- Cardiology Division, Department of Medicine, University Hospital Basel, University of Basel, Basel, Switzerland
- Cardiovascular Research Institute Basel, University Hospital Basel, University of Basel, Basel, Switzerland
| | - Anne Springer
- Cardiology Division, Department of Medicine, University Hospital Basel, University of Basel, Basel, Switzerland
- Cardiovascular Research Institute Basel, University Hospital Basel, University of Basel, Basel, Switzerland
| | - Michael Coslovsky
- Cardiology Division, Department of Medicine, University Hospital Basel, University of Basel, Basel, Switzerland
- Cardiovascular Research Institute Basel, University Hospital Basel, University of Basel, Basel, Switzerland
- Clinical Trial Unit, Department of Clinical Research, University Hospital Basel, University of Basel, Basel, Switzerland
| | - Juerg Beer
- Department of Medicine, Baden Cantonal Hospital, Baden, Switzerland
| | - Giorgio Moschovitis
- Division of Cardiology, Department of Medicine, Ente Ospedaliero Cantonale (EOC), Regional Hospital of Lugano, Lugano, Switzerland
| | - Angelo Auricchio
- Division of Cardiology, Fondazione Cardiocentro Ticino, Lugano, Switzerland
| | - Urs Fischer
- Department of Neurology, Inselspital, University Hospital of Bern, University of Bern, Bern, Switzerland
| | - Carole E. Aubert
- Department of General Internal Medicine, Inselspital, University Hospital of Bern, Bern, Switzerland
- Institute of Primary Health Care (BIHAM), University of Bern, Bern, Switzerland
- Center for Clinical Management Research, Veterans Affairs Ann Arbor Healthcare System, Ann Arbor, MI, USA
- Institute for Healthcare Policy and Innovation, University of Michigan, Ann Arbor, MI, USA
| | - Michael Kühne
- Cardiology Division, Department of Medicine, University Hospital Basel, University of Basel, Basel, Switzerland
- Cardiovascular Research Institute Basel, University Hospital Basel, University of Basel, Basel, Switzerland
| | - David Conen
- Population Health Research Institute, McMaster University, Hamilton, ON, Canada
| | - Stefan Osswald
- Cardiology Division, Department of Medicine, University Hospital Basel, University of Basel, Basel, Switzerland
- Cardiovascular Research Institute Basel, University Hospital Basel, University of Basel, Basel, Switzerland
| | - Leo H. Bonati
- Department of Neurology, University Hospital Basel, Basel, Switzerland
- Correspondence: Leo H. Bonati Department of Neurology, University Hospital Basel, CH-4031 Basel, Switzerland Tel: +41-61-265-2525 Fax: +41-61-265-2020 E-mail:
| | - Jens Wuerfel
- Medical Image Analysis Center (MIAC AG), Basel, Switzerland
- Department of Biomedical Engineering, University of Basel, Basel, Switzerland
- NeuroCure Research Center, Charité University Medicine Berlin, Berlin, Germany
| | | |
Collapse
|
16
|
Poublanc J, Shafi R, Sobczyk O, Sam K, Mandell DM, Venkatraghavan L, Duffin J, Fisher JA, Mikulis DJ. Normal BOLD Response to a Step CO 2 Stimulus After Correction for Partial Volume Averaging. Front Physiol 2021; 12:639360. [PMID: 34194335 PMCID: PMC8236700 DOI: 10.3389/fphys.2021.639360] [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: 12/08/2020] [Accepted: 04/26/2021] [Indexed: 11/26/2022] Open
Abstract
Cerebrovascular reactivity (CVR) is defined as the change in cerebral blood flow induced by a change in a vasoactive stimulus. CVR using BOLD MRI in combination with changes in end-tidal CO2 is a very useful method for assessing vascular performance. In recent years, this technique has benefited from an advanced gas delivery method where end-tidal CO2 can be targeted, measured very precisely, and validated against arterial blood gas sampling (Ito et al., 2008). This has enabled more precise comparison of an individual patient against a normative atlas of healthy subjects. However, expected control ranges for CVR metrics have not been reported in the literature. In this work, we calculate and report the range of control values for the magnitude (mCVR), the steady state amplitude (ssCVR), and the speed (TAU) of the BOLD response to a standard step stimulus, as well as the time delay (TD) as observed in a cohort of 45 healthy controls. These CVR metrics maps were corrected for partial volume averaging for brain tissue types using a linear regression method to enable more accurate quantitation of CVR metrics. In brief, this method uses adjacent voxel CVR metrics in combination with their tissue composition to write the corresponding set of linear equations for estimating CVR metrics of gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF). After partial volume correction, mCVR and ssCVR increase as expected in gray matter, respectively, by 25 and 19%, and decrease as expected in white matter by 33 and 13%. In contrast, TAU and TD decrease in gray matter by 33 and 13%. TAU increase in white matter by 24%, but TD surprisingly decreased by 9%. This correction enables more accurate voxel-wise tissue composition providing greater precision when reporting gray and white matter CVR values.
Collapse
Affiliation(s)
- Julien Poublanc
- Joint Department of Medical Imaging and the Functional Neuroimaging Laboratory, University Health Network, Toronto, ON, Canada
| | - Reema Shafi
- Joint Department of Medical Imaging and the Functional Neuroimaging Laboratory, University Health Network, Toronto, ON, Canada
| | - Olivia Sobczyk
- Joint Department of Medical Imaging and the Functional Neuroimaging Laboratory, University Health Network, Toronto, ON, Canada.,Department of Anesthesia and Pain Management, University Health Network, Toronto, ON, Canada
| | - Kevin Sam
- Department of Radiology and Radiological Sciences, Johns Hopkins University, United States
| | - Daniel M Mandell
- Joint Department of Medical Imaging and the Functional Neuroimaging Laboratory, University Health Network, Toronto, ON, Canada
| | | | - James Duffin
- Department of Anesthesia and Pain Management, University Health Network, Toronto, ON, Canada.,Department of Physiology, University of Toronto, Toronto, ON, Canada
| | - Joseph A Fisher
- Department of Anesthesia and Pain Management, University Health Network, Toronto, ON, Canada.,Department of Physiology, University of Toronto, Toronto, ON, Canada.,Institute of Medical Science, University of Toronto, Toronto, ON, Canada
| | - David J Mikulis
- Joint Department of Medical Imaging and the Functional Neuroimaging Laboratory, University Health Network, Toronto, ON, Canada.,Institute of Medical Science, University of Toronto, Toronto, ON, Canada
| |
Collapse
|
17
|
Bonkhoff AK, Schirmer MD, Bretzner M, Hong S, Regenhardt RW, Brudfors M, Donahue KL, Nardin MJ, Dalca AV, Giese AK, Etherton MR, Hancock BL, Mocking SJT, McIntosh EC, Attia J, Benavente OR, Bevan S, Cole JW, Donatti A, Griessenauer CJ, Heitsch L, Holmegaard L, Jood K, Jimenez-Conde J, Kittner SJ, Lemmens R, Levi CR, McDonough CW, Meschia JF, Phuah CL, Rolfs A, Ropele S, Rosand J, Roquer J, Rundek T, Sacco RL, Schmidt R, Sharma P, Slowik A, Söderholm M, Sousa A, Stanne TM, Strbian D, Tatlisumak T, Thijs V, Vagal A, Wasselius J, Woo D, Zand R, McArdle PF, Worrall BB, Jern C, Lindgren AG, Maguire J, Bzdok D, Wu O, Rost NS. Outcome after acute ischemic stroke is linked to sex-specific lesion patterns. Nat Commun 2021; 12:3289. [PMID: 34078897 PMCID: PMC8172535 DOI: 10.1038/s41467-021-23492-3] [Citation(s) in RCA: 36] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2020] [Accepted: 04/30/2021] [Indexed: 01/31/2023] Open
Abstract
Acute ischemic stroke affects men and women differently. In particular, women are often reported to experience higher acute stroke severity than men. We derived a low-dimensional representation of anatomical stroke lesions and designed a Bayesian hierarchical modeling framework tailored to estimate possible sex differences in lesion patterns linked to acute stroke severity (National Institute of Health Stroke Scale). This framework was developed in 555 patients (38% female). Findings were validated in an independent cohort (n = 503, 41% female). Here, we show brain lesions in regions subserving motor and language functions help explain stroke severity in both men and women, however more widespread lesion patterns are relevant in female patients. Higher stroke severity in women, but not men, is associated with left hemisphere lesions in the vicinity of the posterior circulation. Our results suggest there are sex-specific functional cerebral asymmetries that may be important for future investigations of sex-stratified approaches to management of acute ischemic stroke.
Collapse
Affiliation(s)
- Anna K Bonkhoff
- J. Philip Kistler Stroke Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
| | - Markus D Schirmer
- J. Philip Kistler Stroke Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Clinic for Neuroradiology, University Hospital Bonn, Bonn, Germany
| | - Martin Bretzner
- J. Philip Kistler Stroke Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Univ. Lille, Inserm, CHU Lille, U1171 - LilNCog (JPARC) - Lille Neurosciences & Cognition, F-59000, Lille, France
| | - Sungmin Hong
- J. Philip Kistler Stroke Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Robert W Regenhardt
- J. Philip Kistler Stroke Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Mikael Brudfors
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Kathleen L Donahue
- J. Philip Kistler Stroke Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Marco J Nardin
- J. Philip Kistler Stroke Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Adrian V Dalca
- Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology, Boston, MA, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA
| | - Anne-Katrin Giese
- Department of Neurology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Mark R Etherton
- J. Philip Kistler Stroke Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Brandon L Hancock
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA
| | - Steven J T Mocking
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA
| | - Elissa C McIntosh
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA
| | - John Attia
- Hunter Medical Research Institute, Newcastle, NSW, Australia
- School of Medicine and Public Health, University of Newcastle, Newcastle, NSW, Australia
| | - Oscar R Benavente
- Department of Medicine, Division of Neurology, University of British Columbia, Vancouver, BC, Canada
| | - Stephen Bevan
- School of Life Sciences, University of Lincoln, Lincoln, UK
| | - John W Cole
- Department of Neurology, University of Maryland School of Medicine and Veterans Affairs Maryland Health Care System, Baltimore, MD, USA
| | - Amanda Donatti
- School of Medical Sciences, University of Campinas (UNICAMP) and the Brazilian Institute of Neuroscience and Neurotechnology (BRAINN), Campinas, Sao Paulo, Brazil
| | - Christoph J Griessenauer
- Department of Neurosurgery, Geisinger, Danville, PA, USA
- Research Institute of Neurointervention, Paracelsus Medical University, Salzburg, Austria
| | - Laura Heitsch
- Department of Emergency Medicine, Washington University School of Medicine, St Louis, MO, USA
- Department of Neurology, Washington University School of Medicine & Barnes-Jewish Hospital, St Louis, MO, USA
| | - Lukas Holmegaard
- Department of Clinical Neuroscience, Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- Department of Neurology, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Katarina Jood
- Department of Clinical Neuroscience, Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- Department of Neurology, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Jordi Jimenez-Conde
- Department of Neurology, Neurovascular Research Group (NEUVAS), IMIM-Hospital del Mar (Institut Hospital del Mar d'Investigacions Mèdiques), Universitat Autonoma de Barcelona, Barcelona, Spain
| | - Steven J Kittner
- Department of Neurology, University of Maryland School of Medicine and Veterans Affairs Maryland Health Care System, Baltimore, MD, USA
| | - Robin Lemmens
- KU Leuven - University of Leuven, Department of Neurosciences, Experimental Neurology and Leuven Research Institute for Neuroscience and Disease (LIND), Leuven, Belgium
- VIB, Vesalius Research Center, Laboratory of Neurobiology, University Hospitals Leuven, Department of Neurology, Leuven, Belgium
| | - Christopher R Levi
- School of Medicine and Public Health, University of Newcastle, Newcastle, NSW, Australia
- Department of Neurology, John Hunter Hospital, Newcastle, NSW, Australia
| | - Caitrin W McDonough
- Department of Pharmacotherapy and Translational Research and Center for Pharmacogenomics, University of Florida, Gainesville, FL, USA
| | | | - Chia-Ling Phuah
- Department of Neurology, Washington University School of Medicine & Barnes-Jewish Hospital, St Louis, MO, USA
| | | | - Stefan Ropele
- Department of Neurology, Clinical Division of Neurogeriatrics, Medical University Graz, Graz, Austria
| | - Jonathan Rosand
- J. Philip Kistler Stroke Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Jaume Roquer
- Department of Neurology, Neurovascular Research Group (NEUVAS), IMIM-Hospital del Mar (Institut Hospital del Mar d'Investigacions Mèdiques), Universitat Autonoma de Barcelona, Barcelona, Spain
| | - Tatjana Rundek
- Department of Neurology and Evelyn F. McKnight Brain Institute, Miller School of Medicine, University of Miami, Miami, FL, USA
| | - Ralph L Sacco
- Department of Neurology and Evelyn F. McKnight Brain Institute, Miller School of Medicine, University of Miami, Miami, FL, USA
| | - Reinhold Schmidt
- Department of Neurology, Clinical Division of Neurogeriatrics, Medical University Graz, Graz, Austria
| | - Pankaj Sharma
- Institute of Cardiovascular Research, Royal Holloway University of London (ICR2UL), Egham, UK
- St Peter's and Ashford Hospitals, Egham, UK
| | - Agnieszka Slowik
- Department of Neurology, Jagiellonian University Medical College, Krakow, Poland
| | - Martin Söderholm
- Department of clinical sciences Malmö, Lund University, Lund, Sweden
- Department of Neurology, Skåne University Hospital, Lund and Malmö, Sweden
| | - Alessandro Sousa
- School of Medical Sciences, University of Campinas (UNICAMP) and the Brazilian Institute of Neuroscience and Neurotechnology (BRAINN), Campinas, Sao Paulo, Brazil
| | - Tara M Stanne
- Department of Laboratory Medicine, Institute of Biomedicine, the Sahlgrenska Academy at University of Gothenburg, Gothenburg, Sweden
| | - Daniel Strbian
- Department of Neurology, Helsinki University Hospital and University of Helsinki, Helsinki, Finland
| | - Turgut Tatlisumak
- Department of Clinical Neuroscience, Institute of Neuroscience and Physiology, Sahlgrenska Academy at University of Gothenburg, Gothenburg, Sweden
- Department of Neurology, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Vincent Thijs
- Stroke Division, Florey Institute of Neuroscience and Mental Health, Heidelberg, Australia
- Department of Neurology, Austin Health, Heidelberg, Australia
| | - Achala Vagal
- Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Johan Wasselius
- Department of Clinical Sciences Lund, Radiology, Lund University, Lund, Sweden
- Department of Radiology, Neuroradiology, Skåne University Hospital, Lund, Sweden
| | - Daniel Woo
- Department of Neurology and Rehabilitation Medicine, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Ramin Zand
- Department of Neurology, Geisinger, Danville, PA, USA
| | - Patrick F McArdle
- Division of Endocrinology, Diabetes and Nutrition, Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Bradford B Worrall
- Departments of Neurology and Public Health Sciences, University of Virginia, Charlottesville, VA, USA
| | - Christina Jern
- Department of Laboratory Medicine, Institute of Biomedicine, the Sahlgrenska Academy at University of Gothenburg, Gothenburg, Sweden
- Department of Clinical Genetics and Genomics, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Arne G Lindgren
- Department of Neurology, Skåne University Hospital, Lund, Sweden
- Department of Clinical Sciences Lund, Neurology, Lund University, Lund, Sweden
| | - Jane Maguire
- University of Technology Sydney, Sydney, NSW, Australia
| | - Danilo Bzdok
- Department of Biomedical Engineering, McConnell Brain Imaging Centre, Montreal Neurological Institute, Faculty of Medicine, School of Computer Science, McGill University, Montreal, QC, Canada
- Mila - Quebec Artificial Intelligence Institute, Montreal, QC, Canada
| | - Ona Wu
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA
| | - Natalia S Rost
- J. Philip Kistler Stroke Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| |
Collapse
|
18
|
Zhang J, You Q, Shu J, Gang Q, Jin H, Yu M, Sun W, Zhang W, Huang Y. GJA1 Gene Polymorphisms and Topographic Distribution of Cranial MRI Lesions in Cerebral Small Vessel Disease. Front Neurol 2020; 11:583974. [PMID: 33324328 PMCID: PMC7723976 DOI: 10.3389/fneur.2020.583974] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2020] [Accepted: 11/02/2020] [Indexed: 01/10/2023] Open
Abstract
Vascular endothelial cell (EC) and blood–brain barrier (BBB) dysfunction is the core pathogenesis of cerebral small vessel disease (CSVD). Moreover, animal experiments have shown the importance of connexin (Cx)-43 in EC and BBB function. In this study, we recruited 200 patients diagnosed with sporadic CSVD. Initially, we examined imaging scores of white matter hyperintensities (WMH), lacunar infarction (LI), and cerebral microbleeds (CMB). Additionally, we performed next-generation sequencing of the GJA1 gene (Cx43 coding gene) to examine correlation between these single-nucleotide polymorphisms and the burden and distribution of CSVD. Fourteen target loci were chosen. Of these, 13 loci (92.9%) contributed toward risk for cerebellar LI, one locus (7.1%) was shown to be a protective factor for lobar CMB after FDR adjustment. In conclusion, single-nucleotide polymorphisms in the GJA1 gene appear to affect the distribution but not severity of CSVD.
Collapse
Affiliation(s)
- Jing Zhang
- Department of Neurology, Peking University First Hospital, Beijing, China
| | - Qian You
- Department of Neurology, Peking University First Hospital, Beijing, China
| | - Junlong Shu
- Department of Neurology, Peking University First Hospital, Beijing, China
| | - Qiang Gang
- Department of Neurology, Peking University First Hospital, Beijing, China
| | - Haiqiang Jin
- Department of Neurology, Peking University First Hospital, Beijing, China
| | - Meng Yu
- Department of Neurology, Peking University First Hospital, Beijing, China
| | - Wei Sun
- Department of Neurology, Peking University First Hospital, Beijing, China
| | - Wei Zhang
- Department of Neurology, Peking University First Hospital, Beijing, China
| | - Yining Huang
- Department of Neurology, Peking University First Hospital, Beijing, China
| |
Collapse
|
19
|
Xiong L, Charidimou A, Pasi M, Boulouis G, Pongpitakmetha T, Schirmer MD, Singh S, Benson E, Gurol EM, Rosand J, Greenberg SM, Biffi A, Viswanathan A. Predictors for Late Post-Intracerebral Hemorrhage Dementia in Patients with Probable Cerebral Amyloid Angiopathy. J Alzheimers Dis 2020; 71:435-442. [PMID: 31403947 DOI: 10.3233/jad-190346] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
Abstract
BACKGROUND AND OBJECTIVE Cerebral amyloid angiopathy (CAA) accounts for the majority of lobar intracerebral hemorrhage (ICH); however, the risk factors for dementia conversion after ICH occurrence in CAA patients are unknown, especially in the long-term period after ICH. Therefore, we aimed to unravel the predictors for late post-ICH dementia (6 months after ICH event) in probable CAA patients. METHODS From a large consecutive MRI prospective cohort of spontaneous ICH (2006-2017), we identified probable CAA patients (modified Boston criteria) without dementia 6 months post-ICH. Cognitive outcome during follow-up was determined based on the information from standardized clinical visit notes. We used Cox regression analysis to investigate the association between baseline demographic characteristics, past medical history, MRI biomarkers, and late post-ICH dementia conversion (dementia occurred after 6 months). RESULTS Among 97 non-demented lobar ICH patients with probable CAA, 25 patients (25.8%) developed dementia during a median follow-up time of 2.5 years (IQR 1.5-3.8 years). Pre-existing mild cognitive impairment, increased white matter hyperintensities (WMH) burden, the presence of disseminated cortical superficial siderosis (cSS), and higher total small vessel disease score for CAA were all independent predictors for late dementia conversion. CONCLUSION In probable CAA patients presenting with lobar ICH, high WMH burden and presence of disseminated cSS are useful neuroimaging biomarkers for dementia risk stratification. These findings have implications for clinical practice and future trial design.
Collapse
Affiliation(s)
- Li Xiong
- Department of Neurology, Massachusetts General Hospital Stroke Research Center, Harvard Medical School, Boston, MA, USA
| | - Andreas Charidimou
- Department of Neurology, Massachusetts General Hospital Stroke Research Center, Harvard Medical School, Boston, MA, USA
| | - Marco Pasi
- Department of Neurology, Massachusetts General Hospital Stroke Research Center, Harvard Medical School, Boston, MA, USA
| | - Gregoire Boulouis
- Centre Hospitalier Sainte-Anne, Université Paris Descartes, Paris, France
| | - Thanakit Pongpitakmetha
- Department of Neurology, Massachusetts General Hospital Stroke Research Center, Harvard Medical School, Boston, MA, USA.,Department of Pharmacology, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
| | - Markus D Schirmer
- Department of Neurology, Massachusetts General Hospital Stroke Research Center, Harvard Medical School, Boston, MA, USA.,Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology, Boston, MA, USA.,Department of Population Health Sciences, German Centre for Neurodegenerative Diseases (DZNE), Germany
| | - Sanjula Singh
- Department of Neurology, Massachusetts General Hospital Stroke Research Center, Harvard Medical School, Boston, MA, USA
| | - Emily Benson
- Department of Neurology, Massachusetts General Hospital Stroke Research Center, Harvard Medical School, Boston, MA, USA
| | - Edip M Gurol
- Department of Neurology, Massachusetts General Hospital Stroke Research Center, Harvard Medical School, Boston, MA, USA
| | - Jonathan Rosand
- Department of Neurology, Massachusetts General Hospital Stroke Research Center, Harvard Medical School, Boston, MA, USA
| | - Steven M Greenberg
- Department of Neurology, Massachusetts General Hospital Stroke Research Center, Harvard Medical School, Boston, MA, USA
| | - Alessandro Biffi
- Department of Neurology, Massachusetts General Hospital Stroke Research Center, Harvard Medical School, Boston, MA, USA
| | - Anand Viswanathan
- Department of Neurology, Massachusetts General Hospital Stroke Research Center, Harvard Medical School, Boston, MA, USA
| |
Collapse
|
20
|
The INECO Frontal Screening for the Evaluation of Executive Dysfunction in Cerebral Small Vessel Disease: Evidence from Quantitative MRI in a CADASIL Cohort from Colombia. J Int Neuropsychol Soc 2020; 26:1006-1018. [PMID: 32487276 DOI: 10.1017/s1355617720000533] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
OBJECTIVES Executive dysfunction is a predominant cognitive symptom in cerebral small vessel disease (SVD). The Institute of Cognitive Neurology Frontal Screening (IFS) is a well-validated screening tool allowing the rapid assessment of multiple components of executive function in Spanish-speaking individuals. In this study, we examined performance on the IFS in subjects with cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL), an inherited condition leading to the early onset of SVD. We further explored associations between performance on the IFS and magnetic resonance imaging (MRI) markers of SVD. METHODS We recruited 24 asymptomatic CADASIL subjects and 23 noncarriers from Colombia. All subjects underwent a research MRI and a neuropsychological evaluation, including the IFS. Structural MRI markers of SVD were quantified in each subject, together with an SVD Sum Score representing the overall burden of cerebrovascular alterations. General linear model, correlation, and receiver operating characteristic curve analyses were used to explore group differences on the IFS and relationships with MRI markers of SVD. RESULTS CADASIL subjects had a significantly reduced performance on the IFS Total Score. Performance on the IFS correlated with all quantified markers of SVD, except for brain atrophy and perivascular spaces enlargement. Finally, while the IFS Total Score was not able to accurately discriminate between carriers and noncarriers, it showed adequate sensitivity and specificity in detecting the presence of multiple MRI markers of SVD. CONCLUSIONS These results suggest that the IFS may be a useful screening tool to assess executive function and disease severity in the context of SVD.
Collapse
|
21
|
Dubost F, Bruijne MD, Nardin M, Dalca AV, Donahue KL, Giese AK, Etherton MR, Wu O, Groot MD, Niessen W, Vernooij M, Rost NS, Schirmer MD. Multi-atlas image registration of clinical data with automated quality assessment using ventricle segmentation. Med Image Anal 2020; 63:101698. [PMID: 32339896 PMCID: PMC7275913 DOI: 10.1016/j.media.2020.101698] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2019] [Revised: 12/03/2019] [Accepted: 04/06/2020] [Indexed: 02/08/2023]
Abstract
Registration is a core component of many imaging pipelines. In case of clinical scans, with lower resolution and sometimes substantial motion artifacts, registration can produce poor results. Visual assessment of registration quality in large clinical datasets is inefficient. In this work, we propose to automatically assess the quality of registration to an atlas in clinical FLAIR MRI scans of the brain. The method consists of automatically segmenting the ventricles of a given scan using a neural network, and comparing the segmentation to the atlas ventricles propagated to image space. We used the proposed method to improve clinical image registration to a general atlas by computing multiple registrations - one directly to the general atlas and others via different age-specific atlases - and then selecting the registration that yielded the highest ventricle overlap. Finally, as an example application of the complete pipeline, a voxelwise map of white matter hyperintensity burden was computed using only the scans with registration quality above a predefined threshold. Methods were evaluated in a single-site dataset of more than 1000 scans, as well as a multi-center dataset comprising 142 clinical scans from 12 sites. The automated ventricle segmentation reached a Dice coefficient with manual annotations of 0.89 in the single-site dataset, and 0.83 in the multi-center dataset. Registration via age-specific atlases could improve ventricle overlap compared to a direct registration to the general atlas (Dice similarity coefficient increase up to 0.15). Experiments also showed that selecting scans with the registration quality assessment method could improve the quality of average maps of white matter hyperintensity burden, instead of using all scans for the computation of the white matter hyperintensity map. In this work, we demonstrated the utility of an automated tool for assessing image registration quality in clinical scans. This image quality assessment step could ultimately assist in the translation of automated neuroimaging pipelines to the clinic.
Collapse
Affiliation(s)
- Florian Dubost
- J. Philip Kistler Stroke Research Center, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, USA; Biomedical Imaging Group Rotterdam, Department of Radiology and Nuclear Medicine, Erasmus MC - University Medical Center Rotterdam, The Netherlands.
| | - Marleen de Bruijne
- Biomedical Imaging Group Rotterdam, Department of Radiology and Nuclear Medicine, Erasmus MC - University Medical Center Rotterdam, The Netherlands; Department of Computer Science, University of Copenhagen, Copenhagen, Denmark
| | - Marco Nardin
- J. Philip Kistler Stroke Research Center, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, USA
| | - Adrian V Dalca
- Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology, Cambridge, USA
| | - Kathleen L Donahue
- J. Philip Kistler Stroke Research Center, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, USA
| | - Anne-Katrin Giese
- J. Philip Kistler Stroke Research Center, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, USA
| | - Mark R Etherton
- J. Philip Kistler Stroke Research Center, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, USA
| | - Ona Wu
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA
| | - Marius de Groot
- Biomedical Imaging Group Rotterdam, Department of Radiology and Nuclear Medicine, Erasmus MC - University Medical Center Rotterdam, The Netherlands; Department of Epidemiology, Erasmus MC - University Medical Center Rotterdam, the Netherlands
| | - Wiro Niessen
- Biomedical Imaging Group Rotterdam, Department of Radiology and Nuclear Medicine, Erasmus MC - University Medical Center Rotterdam, The Netherlands; Department of Imaging Physics, Faculty of Applied Science, TU Delft, Delft, The Netherlands
| | - Meike Vernooij
- Department of Radiology and Nuclear Medicine, Erasmus MC - University Medical Center Rotterdam, the Netherlands; Department of Epidemiology, Erasmus MC - University Medical Center Rotterdam, the Netherlands
| | - Natalia S Rost
- J. Philip Kistler Stroke Research Center, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, USA
| | - Markus D Schirmer
- J. Philip Kistler Stroke Research Center, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, USA; Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology, Cambridge, USA; Department of Population Health Sciences, German Centre for Neurodegenerative Diseases (DZNE), Germany.
| |
Collapse
|
22
|
Etherton MR, Wu O, Giese AK, Rost NS. Normal-appearing white matter microstructural injury is associated with white matter hyperintensity burden in acute ischemic stroke. Int J Stroke 2019; 16:184-191. [PMID: 31847795 DOI: 10.1177/1747493019895707] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
BACKGROUND White matter hyperintensity of presumed vascular origin is a risk factor for poor stroke outcomes. In patients with acute ischemic stroke, however, the in vivo mechanisms of white matter microstructural injury are less clear. AIMS To characterize the directional diffusivity components in normal-appearing white matter and white matter hyperintensity in acute ischemic stroke patients. METHODS A retrospective analysis was performed on a cohort of patients with acute ischemic stroke and brain magnetic resonance imaging with diffusion tensor imaging sequences acquired within 48 h of admission. White matter hyperintensity volume was measured in a semi-automated manner. Median fractional anisotropy, mean diffusivity, radial diffusivity, and axial diffusivity values were calculated within normal-appearing white matter and white matter hyperintensity in the hemisphere contralateral to the acute infarct. Linear regression analysis was performed to evaluate predictors of white matter hyperintensity volume and normal-appearing white matter diffusivity metrics. RESULTS In 319 patients, mean age was 64.9 ± 15.9 years. White matter hyperintensity volume was 6.33 cm3 (interquartile range 3.0-12.6 cm3). Axial and radial diffusivity were significantly increased in white matter hyperintensity compared to normal-appearing white matter. In multivariable linear regression, age (β = 0.20, P = 0.003) and normal-appearing white matter axial diffusivity (β = 37.9, P < 0.001) were independently associated with white matter hyperintensity volume. Subsequent analysis demonstrated that increasing age (β = 0.004, P < 0.001) and admission diastolic blood pressure (β = 0.001, P = 0.02) were independent predictors of normal-appearing white matter axial diffusivity in multivariable linear regression. CONCLUSIONS Normal-appearing white matter axial diffusivity increases with age and is an independent predictor of white matter hyperintensity volume in acute ischemic stroke.
Collapse
Affiliation(s)
- Mark R Etherton
- Department of Neurology, J. Philip Kistler Stroke Research Center, 2348Massachusetts General Hospital and Harvard Medical School, Boston, USA
| | - Ona Wu
- Department of Neurology, J. Philip Kistler Stroke Research Center, 2348Massachusetts General Hospital and Harvard Medical School, Boston, USA.,Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, 2348Massachusetts General Hospital, Boston, USA
| | - Anne-Katrin Giese
- Department of Neurology, J. Philip Kistler Stroke Research Center, 2348Massachusetts General Hospital and Harvard Medical School, Boston, USA
| | - Natalia S Rost
- Department of Neurology, J. Philip Kistler Stroke Research Center, 2348Massachusetts General Hospital and Harvard Medical School, Boston, USA
| |
Collapse
|
23
|
Sartoretti E, Sartoretti T, Wyss M, Becker AS, Schwenk Á, van Smoorenburg L, Najafi A, Binkert C, Thoeny HC, Zhou J, Jiang S, Graf N, Czell D, Sartoretti-Schefer S, Reischauer C. Amide Proton Transfer Weighted Imaging Shows Differences in Multiple Sclerosis Lesions and White Matter Hyperintensities of Presumed Vascular Origin. Front Neurol 2019; 10:1307. [PMID: 31920930 PMCID: PMC6914856 DOI: 10.3389/fneur.2019.01307] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2019] [Accepted: 11/26/2019] [Indexed: 01/14/2023] Open
Abstract
Objectives: To assess the ability of 3D amide proton transfer weighted (APTw) imaging based on magnetization transfer analysis to discriminate between multiple sclerosis lesions (MSL) and white matter hyperintensities of presumed vascular origin (WMH) and to compare APTw signal intensity of healthy white matter (healthy WM) with APTw signal intensity of MSL and WHM. Materials and Methods: A total of 27 patients (16 female, 11 males, mean age 39.6 years) with multiple sclerosis, 35 patients (17 females, 18 males, mean age 66.6 years) with small vessel disease (SVD) and 20 healthy young volunteers (9 females, 11 males, mean age 29 years) were included in the MSL, the WMH, and the healthy WM group. MSL and WMH were segmented on fluid attenuated inversion recovery (FLAIR) images underlaid onto APTw images. Histogram parameters (mean, median, 10th, 25th, 75th, 90th percentile) were calculated. Mean APTw signal intensity values in healthy WM were defined by "Region of interest" (ROI) measurements. Wilcoxon rank sum tests and receiver operating characteristics (ROC) curve analyses of clustered data were applied. Results: All histogram parameters except the 75 and 90th percentile were significantly different between MSL and WMH (p = 0.018-p = 0.034). MSL presented with higher median values in all parameters. The histogram parameters offered only low diagnostic performance in discriminating between MSL and WMH. The 10th percentile yielded the highest diagnostic performance with an AUC of 0.6245 (95% CI: [0.532, 0.717]). Mean APTw signal intensity values of MSL were significantly higher than mean values of healthy WM (p = 0.005). The mean values of WMH did not differ significantly from the values of healthy WM (p = 0.345). Conclusions: We found significant differences in APTw signal intensity, based on straightforward magnetization transfer analysis, between MSL and WMH and between MSL and healthy WM. Low AUC values from ROC analyses, however, suggest that it may be challenging to determine type of lesion with APTw imaging. More advanced analysis of the APT CEST signal may be helpful for further differentiation of MSL and WMH.
Collapse
Affiliation(s)
| | - Thomas Sartoretti
- Laboratory of Translational Nutrition Biology, Department of Health Sciences and Technology, ETH Zurich, Schwerzenbach, Switzerland
| | - Michael Wyss
- Institute of Radiology, Kantonsspital Winterthur, Winterthur, Switzerland.,Philips Healthsystems, Zurich, Switzerland
| | - Anton S Becker
- Laboratory of Translational Nutrition Biology, Department of Health Sciences and Technology, ETH Zurich, Schwerzenbach, Switzerland.,Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich, Zurich, Switzerland.,Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, United States
| | - Árpád Schwenk
- Institute of Radiology, Kantonsspital Winterthur, Winterthur, Switzerland
| | | | - Arash Najafi
- Institute of Radiology, Kantonsspital Winterthur, Winterthur, Switzerland
| | - Christoph Binkert
- Institute of Radiology, Kantonsspital Winterthur, Winterthur, Switzerland
| | - Harriet C Thoeny
- Department of Medicine, University of Fribourg, Fribourg, Switzerland.,Department of Radiology, HFR Fribourg-Hôpital Cantonal, Fribourg, Switzerland
| | - Jinyuan Zhou
- Division of MR Research, Department of Radiology, Johns Hopkins University, Baltimore, MD, United States
| | - Shanshan Jiang
- Division of MR Research, Department of Radiology, Johns Hopkins University, Baltimore, MD, United States
| | | | - David Czell
- Department of Neurology, Spital Linth, Uznach, Switzerland
| | | | - Carolin Reischauer
- Department of Medicine, University of Fribourg, Fribourg, Switzerland.,Department of Radiology, HFR Fribourg-Hôpital Cantonal, Fribourg, Switzerland
| |
Collapse
|
24
|
Schirmer MD, Ktena SI, Nardin MJ, Donahue KL, Giese AK, Etherton MR, Wu O, Rost NS. Rich-Club Organization: An Important Determinant of Functional Outcome After Acute Ischemic Stroke. Front Neurol 2019; 10:956. [PMID: 31551913 PMCID: PMC6748157 DOI: 10.3389/fneur.2019.00956] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2019] [Accepted: 08/20/2019] [Indexed: 12/21/2022] Open
Abstract
Objective: To determine whether the rich-club organization, essential for information transport in the human connectome, is an important biomarker of functional outcome after acute ischemic stroke (AIS). Methods: Consecutive AIS patients (N = 344) with acute brain magnetic resonance imaging (MRI) (<48 h) were eligible for this study. Each patient underwent a clinical MRI protocol, which included diffusion weighted imaging (DWI). All DWIs were registered to a template on which rich-club regions have been defined. Using manual outlines of stroke lesions, we automatically counted the number of affected rich-club regions and assessed its effect on the National Institute of Health Stroke Scale (NIHSS) and modified Rankin Scale (mRS; obtained at 90 days post-stroke) scores through ordinal regression. Results: Of 344 patients (median age 65, inter-quartile range 54-76 years) with a median DWI lesion volume (DWIv) of 3cc, 64% were male. We established that an increase in number of rich-club regions affected by a stroke increases the odds of poor stroke outcome, measured by NIHSS (OR: 1.77, 95%CI 1.41-2.21) and mRS (OR: 1.38, 95%CI 1.11-1.73). Additionally, we demonstrated that the OR exceeds traditional markers, such as DWIv (ORNIHSS 1.08, 95%CI 1.06-1.11; ORmRS 1.05, 95%CI 1.03-1.07) and age (ORNIHSS 1.03, 95%CI 1.01-1.05; ORmRS 1.05, 95%CI 1.03-1.07). Conclusion: In this proof-of-concept study, the number of rich-club nodes affected by a stroke lesion presents a translational biomarker of stroke outcome, which can be readily assessed using standard clinical AIS imaging protocols and considered in functional outcome prediction models beyond traditional factors.
Collapse
Affiliation(s)
- Markus D Schirmer
- Department of Neurology, J. Philip Kistler Stroke Research Center, Harvard Medical School, Boston, MA, United States.,Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology, Cambridge, MA, United States.,Department of Population Health Sciences, German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
| | - Sofia Ira Ktena
- Biomedical Image Analysis Group, Imperial College London, London, United Kingdom
| | - Marco J Nardin
- Department of Neurology, J. Philip Kistler Stroke Research Center, Harvard Medical School, Boston, MA, United States
| | - Kathleen L Donahue
- Department of Neurology, J. Philip Kistler Stroke Research Center, Harvard Medical School, Boston, MA, United States
| | - Anne-Katrin Giese
- Department of Neurology, J. Philip Kistler Stroke Research Center, Harvard Medical School, Boston, MA, United States
| | - Mark R Etherton
- Department of Neurology, J. Philip Kistler Stroke Research Center, Harvard Medical School, Boston, MA, United States
| | - Ona Wu
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA, United States
| | - Natalia S Rost
- Department of Neurology, J. Philip Kistler Stroke Research Center, Harvard Medical School, Boston, MA, United States
| |
Collapse
|
25
|
Schirmer MD, Dalca AV, Sridharan R, Giese AK, Donahue KL, Nardin MJ, Mocking SJT, McIntosh EC, Frid P, Wasselius J, Cole JW, Holmegaard L, Jern C, Jimenez-Conde J, Lemmens R, Lindgren AG, Meschia JF, Roquer J, Rundek T, Sacco RL, Schmidt R, Sharma P, Slowik A, Thijs V, Woo D, Vagal A, Xu H, Kittner SJ, McArdle PF, Mitchell BD, Rosand J, Worrall BB, Wu O, Golland P, Rost NS. White matter hyperintensity quantification in large-scale clinical acute ischemic stroke cohorts - The MRI-GENIE study. NEUROIMAGE-CLINICAL 2019; 23:101884. [PMID: 31200151 PMCID: PMC6562316 DOI: 10.1016/j.nicl.2019.101884] [Citation(s) in RCA: 40] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/15/2019] [Revised: 05/02/2019] [Accepted: 05/25/2019] [Indexed: 11/26/2022]
Abstract
White matter hyperintensity (WMH) burden is a critically important cerebrovascular phenotype linked to prediction of diagnosis and prognosis of diseases, such as acute ischemic stroke (AIS). However, current approaches to its quantification on clinical MRI often rely on time intensive manual delineation of the disease on T2 fluid attenuated inverse recovery (FLAIR), which hinders high-throughput analyses such as genetic discovery. In this work, we present a fully automated pipeline for quantification of WMH in clinical large-scale studies of AIS. The pipeline incorporates automated brain extraction, intensity normalization and WMH segmentation using spatial priors. We first propose a brain extraction algorithm based on a fully convolutional deep learning architecture, specifically designed for clinical FLAIR images. We demonstrate that our method for brain extraction outperforms two commonly used and publicly available methods on clinical quality images in a set of 144 subject scans across 12 acquisition centers, based on dice coefficient (median 0.95; inter-quartile range 0.94–0.95; p < 0.01) and Pearson correlation of total brain volume (r = 0.90). Subsequently, we apply it to the large-scale clinical multi-site MRI-GENIE study (N = 2783) and identify a decrease in total brain volume of −2.4 cc/year. Additionally, we show that the resulting total brain volumes can successfully be used for quality control of image preprocessing. Finally, we obtain WMH volumes by building on an existing automatic WMH segmentation algorithm that delineates and distinguishes between different cerebrovascular pathologies. The learning method mimics expert knowledge of the spatial distribution of the WMH burden using a convolutional auto-encoder. This enables successful computation of WMH volumes of 2533 clinical AIS patients. We utilize these results to demonstrate the increase of WMH burden with age (0.950 cc/year) and show that single site estimates can be biased by the number of subjects recruited. Fully automated high-throughput white matter hyperintensity segmentation pipeline. Methodology designed for and applied to international clinical multi-site data. Calculation of disease burden in 2533 acute ischemic stroke patients. Total brain volume change with age (−2.4 cc/year) used in automated quality control. Increase of white matter hyperintensity burden of 0.95 cc/year.
Collapse
Affiliation(s)
- Markus D Schirmer
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Computer Science and Artificial Intelligence Lab, MIT, USA; Department of Population Health Sciences, German Centre for Neurodegenerative Diseases (DZNE), Germany.
| | - Adrian V Dalca
- Computer Science and Artificial Intelligence Lab, MIT, USA; Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA
| | | | - Anne-Katrin Giese
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Kathleen L Donahue
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Marco J Nardin
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Steven J T Mocking
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA
| | - Elissa C McIntosh
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA
| | - Petrea Frid
- Department of Clinical Sciences Lund, Neurology, Lund University, Lund, Sweden
| | - Johan Wasselius
- Department of Clinical Sciences Lund, Radiology, Lund University, Lund, Sweden; Department of Radiology, Neuroradiology, Skåne University Hospital, Malmö, Sweden
| | - John W Cole
- Department of Neurology, University of Maryland School of Medicine and Veterans Affairs Maryland Health Care System, Baltimore, MD, USA
| | - Lukas Holmegaard
- Institute of Neuroscience and Physiology, the Sahlgrenska Academy at University of Gothenburg, Gothenburg, Sweden
| | - Christina Jern
- Institute of Biomedicine, the Sahlgrenska Academy at University of Gothenburg, Gothenburg, Sweden
| | - Jordi Jimenez-Conde
- Department of Neurology, Neurovascular Research Group (NEUVAS), IMIM-Hospital del Mar (Institut Hospital del Mar d'Investigacions Mèdiques), Universitat Autonoma de Barcelona, Barcelona, Spain
| | - Robin Lemmens
- Department of Neurosciences, Experimental Neurology and Leuven Research Institute for Neuroscience and Disease (LIND), KU Leuven - University of Leuven, Leuven, Belgium; VIB, Vesalius Research Center, Laboratory of Neurobiology, Department of Neurology, University Hospitals Leuven, Leuven, Belgium
| | - Arne G Lindgren
- Department of Clinical Sciences Lund, Neurology, Lund University, Lund, Sweden; Department of Neurology and Rehabilitation Medicine, Skåne University Hospital, Lund, Sweden
| | | | - Jaume Roquer
- Department of Neurology, Neurovascular Research Group (NEUVAS), IMIM-Hospital del Mar (Institut Hospital del Mar d'Investigacions Mèdiques), Universitat Autonoma de Barcelona, Barcelona, Spain
| | - Tatjana Rundek
- Department of Neurology, Miller School of Medicine, University of Miami, Miami, FL, USA
| | - Ralph L Sacco
- Department of Neurology, Miller School of Medicine, University of Miami, Miami, FL, USA
| | - Reinhold Schmidt
- Department of Neurology, Clinical Division of Neurogeriatrics, Medical University Graz, Graz, Austria
| | - Pankaj Sharma
- Institute of Cardiovascular Research, St Peter's and Ashford Hospitals, Royal Holloway University of London (ICR2UL), Egham, UK
| | - Agnieszka Slowik
- Department of Neurology, Jagiellonian University Medical College, Krakow, Poland
| | - Vincent Thijs
- Stroke Division, Australia and Department of Neurology, Austin Health, Florey Institute of Neuroscience and Mental Health, Heidelberg, Australia
| | - Daniel Woo
- Department of Neurology and Rehabilitation Medicine, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Achala Vagal
- Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Huichun Xu
- Division of Endocrinology, Diabetes and Nutrition, Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Steven J Kittner
- Department of Neurology, University of Maryland School of Medicine and Veterans Affairs Maryland Health Care System, Baltimore, MD, USA
| | - Patrick F McArdle
- Division of Endocrinology, Diabetes and Nutrition, Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Braxton D Mitchell
- Division of Endocrinology, Diabetes and Nutrition, Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Jonathan Rosand
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA; Center for Human Genetic Research, Massachusetts General Hospital, Boston, MA, USA
| | - Bradford B Worrall
- Departments of Neurology and Public Health Sciences, University of Virginia, Charlottesville, VA, USA
| | - Ona Wu
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA
| | - Polina Golland
- Computer Science and Artificial Intelligence Lab, MIT, USA
| | - Natalia S Rost
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | | |
Collapse
|