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da Silva PHR, de Leeuw FE, Zotin MCZ, Neto OMP, Leoni RF, Tuladhar AM. Cortical Thickness and Brain Connectivity Mediate the Relation Between White Matter Hyperintensity and Information Processing Speed in Cerebral Small Vessel Disease. Brain Topogr 2023:10.1007/s10548-023-00973-w. [PMID: 37273021 DOI: 10.1007/s10548-023-00973-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2023] [Accepted: 05/26/2023] [Indexed: 06/06/2023]
Abstract
White matter hyperintensities of presumed vascular origin (WMH) are the most common imaging feature of cerebral small vessel disease (cSVD) and are associated with cognitive impairment, especially information processing speed (IPS) deficits. However, it is unclear how WMH can directly impact IPS or whether the cortical thickness and brain connectivity mediate such association. In this study, it was evaluated the possible mediating roles of cortical thickness and brain (structural and functional) connectivity on the relationship between WMH (also considering its topography distribution) and IPS in 389 patients with cSVD from the RUN-DMC (Radboud University Nijmegen Diffusion tensor and Magnetic resonance imaging Cohort) database. Significant (p < 0.05 after multiple comparisons correction) associations of WMH volume and topography with cortical thickness, brain connectivity, and IPS performance in cSVD individuals were found. Additionally, cortical thickness and brain structural and functional connectivity were shown to mediate the association of WMH volume and location with IPS scores. More specifically, frontal cortical thickness, functional sensorimotor network, and posterior thalamic radiation tract were the essential mediators of WMH and IPS in this clinical group. This study provided insight into the mechanisms underlying the clinical relevance of white matter hyperintensities in information processing speed deficits in cSVD through cortical thinning and network disruptions.
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Affiliation(s)
| | - Frank-Erik de Leeuw
- Department of Neurology, Donders Center for Medical Neuroscience, Radboud University Nijmegen Medical Centre, Nijmegen, The Netherlands
| | - Maria Clara Zanon Zotin
- Department of Neurology, J. Philip Kistler Stroke Research Center, MGH, Boston, MA, USA
- Department of Medical Imaging, Hematology, and Clinical Oncology, Ribeirão Preto Medical School, Ribeirão Preto, Brazil
| | - Octavio Marques Pontes Neto
- Department of Neurosciences and Behavioural Sciences, Hospital das Clínicas-Ribeirão Preto Medical School, University of São Paulo, Ribeirão Preto, Brazil
| | | | - Anil M Tuladhar
- Department of Neurology, Donders Center for Medical Neuroscience, Radboud University Nijmegen Medical Centre, Nijmegen, The Netherlands
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2
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Tozer DJ, Pflanz CP, Markus HS. Reproducibility of regional structural and functional MRI networks in cerebral small vessel disease compared to age matched and stroke-free controls. CEREBRAL CIRCULATION - COGNITION AND BEHAVIOR 2023; 4:100167. [PMID: 37397269 PMCID: PMC10313873 DOI: 10.1016/j.cccb.2023.100167] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Revised: 05/11/2023] [Accepted: 05/12/2023] [Indexed: 07/04/2023]
Abstract
Abnormalities in structural and functional MRI connectivity measures have been reported in cerebral small vessel disease (SVD). Previous research has shown that whole-brain structural connectivity was highly reproducible in SVD patients, while whole-brain functional connectivity showed low reproducibility. It remains unclear whether the lower reproducibility of functional networks reported in SVD is due to selective disruption of reproducibility in specific networks or is generalised in patients with SVD. In this case-control study 15 SVD and 10 age-matched control participants were imaged twice with diffusion tensor imaging and resting state fMRI. Structural and functional connectivity matrices were constructed from this data and the default mode, fronto-parietal, limbic, salience, somatomotor and visual networks were extracted and the average connectivity between connections calculated and used to determine their reproducibility. Regional structural networks were more reproducible than functional networks, all structural networks showed ICC values ≥0.64 (except the salience network in SVD). The functional networks showed greater reproducibility in the controls compared to SVD with ICC values >0.7 for control participants and <=0.5 for the SVD group. The default mode network showed the greatest reproducibility for both control and SVD groups. Reproducibility of functional networks was affected by disease status with lower reproducibility in SVD compared with controls.
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Affiliation(s)
- Daniel J. Tozer
- Corresponding author at: University of Cambridge, Department of Clinical Neurosciences, Neurology Unit, R3, Box 83, Cambridge Biomedical Campus, Cambridge CB2 0QQ, United Kingdom.
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3
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Tay J, Düring M, van Leijsen EMC, Bergkamp MI, Norris DG, de Leeuw FE, Markus HS, Tuladhar AM. Network structure-function coupling and neurocognition in cerebral small vessel disease. Neuroimage Clin 2023; 38:103421. [PMID: 37141644 PMCID: PMC10176072 DOI: 10.1016/j.nicl.2023.103421] [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: 11/03/2022] [Revised: 03/23/2023] [Accepted: 04/24/2023] [Indexed: 05/06/2023]
Abstract
BACKGROUND Cerebral small vessel disease is a leading cause of cognitive decline and vascular dementia. Small vessel disease pathology changes structural brain networks, but its impact on functional networks remains poorly understood. Structural and functional networks are closely coupled in healthy individuals, and decoupling is associated with clinical symptoms in other neurological conditions. We tested the hypothesis that structural-functional network coupling is related to neurocognitive outcomes in 262 small vessel disease patients. METHODS Participants underwent multimodal magnetic resonance imaging and cognitive assessment in 2011 and 2015. Structural connectivity networks were reconstructed using probabilistic diffusion tractography, while functional connectivity networks were estimated from resting-state functional magnetic resonance imaging. Structural and functional networks were then correlated to calculate a measure of structural-functional network coupling for each participant. RESULTS Lower whole-brain coupling was associated with reduced processing speed and greater apathy both cross-sectionally and longitudinally. In addition, coupling within the cognitive control network was associated with all cognitive outcomes, suggesting that neurocognitive outcomes in small vessel disease may be related to the functioning of this intrinsic connectivity network. CONCLUSIONS Our work demonstrates the influence of structural-functional connectivity network decoupling in small vessel disease symptomatology. Cognitive control network function may be investigated in future studies.
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Affiliation(s)
- Jonathan Tay
- Stroke Research Group, Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - Marco Düring
- Medical Image Analysis Center (MIAC AG) and qbig, Department of Biomedical Engineering, University of Basel, Basel, Switzerland
| | | | - Mayra I Bergkamp
- Department of Neurology, Donders Center for Medical Neurosciences, Radboud University Nijmegen Medical Centre, Nijmegen, the Netherlands
| | - David G Norris
- Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands
| | - Frank-Erik de Leeuw
- Department of Neurology, Donders Center for Medical Neurosciences, Radboud University Nijmegen Medical Centre, Nijmegen, the Netherlands
| | - Hugh S Markus
- Stroke Research Group, Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - Anil M Tuladhar
- Department of Neurology, Donders Center for Medical Neurosciences, Radboud University Nijmegen Medical Centre, Nijmegen, the Netherlands.
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4
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Long Y, Ouyang X, Yan C, Wu Z, Huang X, Pu W, Cao H, Liu Z, Palaniyappan L. Evaluating test-retest reliability and sex-/age-related effects on temporal clustering coefficient of dynamic functional brain networks. Hum Brain Mapp 2023; 44:2191-2208. [PMID: 36637216 PMCID: PMC10028647 DOI: 10.1002/hbm.26202] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Revised: 11/25/2022] [Accepted: 01/01/2023] [Indexed: 01/14/2023] Open
Abstract
The multilayer dynamic network model has been proposed as an effective method to understand the brain function. In particular, derived from the definition of clustering coefficient in static networks, the temporal clustering coefficient provides a direct measure of the topological stability of dynamic brain networks and shows potential in predicting altered brain functions. However, test-retest reliability and demographic-related effects on this measure remain to be evaluated. Using a data set from the Human Connectome Project (157 male and 180 female healthy adults; 22-37 years old), the present study investigated: (1) the test-retest reliability of temporal clustering coefficient across four repeated resting-state functional magnetic resonance imaging scans as measured by intraclass correlation coefficient (ICC); and (2) sex- and age-related effects on temporal clustering coefficient. The results showed that (1) the temporal clustering coefficient had overall moderate test-retest reliability (ICC > 0.40 over a wide range of densities) at both global and subnetwork levels, (2) female subjects showed significantly higher temporal clustering coefficient than males at both global and subnetwork levels, particularly within the default-mode and subcortical regions, and (3) temporal clustering coefficient of the subcortical subnetwork was positively correlated with age in young adults. The results of sex effects were robustly replicated in an independent REST-meta-MDD data set, while the results of age effects were not. Our findings suggest that the temporal clustering coefficient is a relatively reliable and reproducible approach for identifying individual differences in brain function, and provide evidence for demographically related effects on the human brain dynamic connectomes.
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Affiliation(s)
- Yicheng Long
- Department of Psychiatry, and National Clinical Research Center for Mental DisordersThe Second Xiangya Hospital of Central South UniversityChangshaHunanChina
| | - Xuan Ouyang
- Department of Psychiatry, and National Clinical Research Center for Mental DisordersThe Second Xiangya Hospital of Central South UniversityChangshaHunanChina
| | - Chaogan Yan
- CAS Key Laboratory of Behavioral Science, Institute of PsychologyChinese Academy of SciencesBeijingChina
- Department of PsychologyUniversity of Chinese Academy of SciencesBeijingChina
- International Big‐Data Center for Depression Research, Institute of PsychologyChinese Academy of SciencesBeijingChina
| | - Zhipeng Wu
- Department of Psychiatry, and National Clinical Research Center for Mental DisordersThe Second Xiangya Hospital of Central South UniversityChangshaHunanChina
| | - Xiaojun Huang
- Department of PsychiatryJiangxi Provincial People's Hospital, The First Affiliated Hospital of Nanchang Medical CollegeNanchangChina
| | - Weidan Pu
- Medical Psychological InstituteThe Second Xiangya Hospital, Central South UniversityChangshaChina
| | - Hengyi Cao
- Center for Psychiatric NeuroscienceFeinstein Institute for Medical ResearchManhassetNew YorkUSA
- Division of Psychiatry ResearchZucker Hillside HospitalGlen OaksNew YorkUSA
| | - Zhening Liu
- Department of Psychiatry, and National Clinical Research Center for Mental DisordersThe Second Xiangya Hospital of Central South UniversityChangshaHunanChina
| | - Lena Palaniyappan
- Department of PsychiatryUniversity of Western OntarioLondonOntarioCanada
- Robarts Research InstituteUniversity of Western OntarioLondonOntarioCanada
- Lawson Health Research InstituteLondonOntarioCanada
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5
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Drenth N, Foster-Dingley JC, Bertens AS, Rius Ottenheim N, van der Mast RC, Rombouts SARB, van Rooden S, van der Grond J. Functional connectivity in older adults-the effect of cerebral small vessel disease. Brain Commun 2023; 5:fcad126. [PMID: 37168731 PMCID: PMC10165246 DOI: 10.1093/braincomms/fcad126] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2022] [Revised: 02/08/2023] [Accepted: 04/17/2023] [Indexed: 05/13/2023] Open
Abstract
Ageing is associated with functional reorganization that is mainly characterized by declining functional connectivity due to general neurodegeneration and increasing incidence of disease. Functional connectivity has been studied across the lifespan; however, there is a paucity of research within the older groups (≥75 years) where neurodegeneration and disease prevalence are at its highest. In this cross-sectional study, we investigated associations between age and functional connectivity and the influence of cerebral small vessel disease (CSVD)-a common age-related morbidity-in 167 community-dwelling older adults aged 75-91 years (mean = 80.3 ± 3.8). Resting-state functional MRI was used to determine functional connectivity within ten standard networks and calculate the whole-brain graph theoretical measures global efficiency and clustering coefficient. CSVD features included white matter hyperintensities, lacunar infarcts, cerebral microbleeds, and atrophy that were assessed in each individual and a composite score was calculated. Both main and interaction effects (age*CSVD features) on functional connectivity were studied. We found stable levels of functional connectivity across the age range. CSVD was not associated with functional connectivity measures. To conclude, our data show that the functional architecture of the brain is relatively unchanged after 75 years of age and not differentially affected by individual levels of vascular pathology.
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Affiliation(s)
- Nadieh Drenth
- Correspondence to: Nadieh Drenth Department of Radiology Leiden University Medical Center, Albinusdreef 2, 2300 RC Leiden, The Netherlands. E-mail:
| | - Jessica C Foster-Dingley
- Department of Radiology, Leiden University Medical Center, P.O. Box 9600, 2300 RC Leiden, The Netherlands
- Department of Psychiatry, Leiden University Medical Center, P.O. Box 9600, 2300 RC Leiden, The Netherlands
| | - Anne Suzanne Bertens
- Department of Radiology, Leiden University Medical Center, P.O. Box 9600, 2300 RC Leiden, The Netherlands
- Department of Psychiatry, Leiden University Medical Center, P.O. Box 9600, 2300 RC Leiden, The Netherlands
| | - Nathaly Rius Ottenheim
- Department of Psychiatry, Leiden University Medical Center, P.O. Box 9600, 2300 RC Leiden, The Netherlands
| | - Roos C van der Mast
- Department of Psychiatry, Leiden University Medical Center, P.O. Box 9600, 2300 RC Leiden, The Netherlands
- Department of Psychiatry, Collaborative Antwerp Psychiatric Research Institute (CAPRI)–University of Antwerp, Antwerp, Belgium
| | - Serge A R B Rombouts
- Department of Radiology, Leiden University Medical Center, P.O. Box 9600, 2300 RC Leiden, The Netherlands
- Institute of Psychology, Leiden University, P.O. Box 9555, 2300 RB Leiden, The Netherlands
- Leiden Institute for Brain and Cognition, P.O. Box 9600, 2300 RC Leiden, The Netherlands
| | - Sanneke van Rooden
- Department of Radiology, Leiden University Medical Center, P.O. Box 9600, 2300 RC Leiden, The Netherlands
| | - Jeroen van der Grond
- Department of Radiology, Leiden University Medical Center, P.O. Box 9600, 2300 RC Leiden, The Netherlands
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6
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van den Brink H, Kopczak A, Arts T, Onkenhout L, Siero JCW, Zwanenburg JJM, Hein S, Hübner M, Gesierich B, Duering M, Stringer MS, Hendrikse J, Wardlaw JM, Joutel A, Dichgans M, Biessels GJ. CADASIL Affects Multiple Aspects of Cerebral Small Vessel Function on 7T-MRI. Ann Neurol 2023; 93:29-39. [PMID: 36222455 DOI: 10.1002/ana.26527] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2022] [Revised: 10/05/2022] [Accepted: 10/07/2022] [Indexed: 02/05/2023]
Abstract
OBJECTIVE Cerebral small vessel diseases (cSVDs) are a major cause of stroke and dementia. We used cutting-edge 7T-MRI techniques in patients with Cerebral Autosomal Dominant Arteriopathy with Subcortical Infarcts and Leukoencephalopathy (CADASIL), to establish which aspects of cerebral small vessel function are affected by this monogenic form of cSVD. METHODS We recruited 23 CADASIL patients (age 51.1 ± 10.1 years, 52% women) and 13 age- and sex-matched controls (46.1 ± 12.6, 46% women). Small vessel function measures included: basal ganglia and centrum semiovale perforating artery blood flow velocity and pulsatility, vascular reactivity to a visual stimulus in the occipital cortex and reactivity to hypercapnia in the cortex, subcortical gray matter, white matter, and white matter hyperintensities. RESULTS Compared with controls, CADASIL patients showed lower blood flow velocity and higher pulsatility index within perforating arteries of the centrum semiovale (mean difference - 0.09 cm/s, p = 0.03 and 0.20, p = 0.009) and basal ganglia (mean difference - 0.98 cm/s, p = 0.003 and 0.17, p = 0.06). Small vessel reactivity to a short visual stimulus was decreased (blood-oxygen-level dependent [BOLD] mean difference -0.21%, p = 0.04) in patients, while reactivity to hypercapnia was preserved in the cortex, subcortical gray matter, and normal appearing white matter. Among patients, reactivity to hypercapnia was decreased in white matter hyperintensities compared to normal appearing white matter (BOLD mean difference -0.29%, p = 0.02). INTERPRETATION Multiple aspects of cerebral small vessel function on 7T-MRI were abnormal in CADASIL patients, indicative of increased arteriolar stiffness and regional abnormalities in reactivity, locally also in relation to white matter injury. These observations provide novel markers of cSVD for mechanistic and intervention studies. ANN NEUROL 2023;93:29-39.
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Affiliation(s)
- Hilde van den Brink
- Department of Neurology and Neurosurgery, University Medical Center Utrecht Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Anna Kopczak
- Institute for Stroke and Dementia Research, University Hospital, Ludwig-Maximilians-University Munich, Munich, Germany
| | - Tine Arts
- Department of Radiology, Center for Image Sciences, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Laurien Onkenhout
- Department of Neurology and Neurosurgery, University Medical Center Utrecht Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Jeroen C W Siero
- Department of Radiology, Center for Image Sciences, University Medical Center Utrecht, Utrecht, The Netherlands.,Spinoza Centre for Neuroimaging Amsterdam, Amsterdam, The Netherlands
| | - Jaco J M Zwanenburg
- Department of Radiology, Center for Image Sciences, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Sandra Hein
- Institute for Stroke and Dementia Research, University Hospital, Ludwig-Maximilians-University Munich, Munich, Germany
| | - Mathias Hübner
- Institute for Stroke and Dementia Research, University Hospital, Ludwig-Maximilians-University Munich, Munich, Germany
| | - Benno Gesierich
- Institute for Stroke and Dementia Research, University Hospital, Ludwig-Maximilians-University Munich, Munich, Germany.,Medical Image Analysis Center (MIAC AG) and qbig, Department of Biomedical Engineering, University of Basel, Basel, Switzerland
| | - Marco Duering
- Institute for Stroke and Dementia Research, University Hospital, Ludwig-Maximilians-University Munich, Munich, Germany.,Medical Image Analysis Center (MIAC AG) and qbig, Department of Biomedical Engineering, University of Basel, Basel, Switzerland
| | - Michael S Stringer
- Brain Research Imaging Centre, Centre for Clinical Brain Sciences, UK Dementia Research Institute Centre at the University of Edinburgh, Edinburgh, UK
| | - Jeroen Hendrikse
- Department of Radiology, Center for Image Sciences, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Joanna M Wardlaw
- Brain Research Imaging Centre, Centre for Clinical Brain Sciences, UK Dementia Research Institute Centre at the University of Edinburgh, Edinburgh, UK
| | - Anne Joutel
- Institute of Psychiatry and Neurosciences of Paris, Université de Paris, Inserm U1266, Paris, France
| | - Martin Dichgans
- Institute for Stroke and Dementia Research, University Hospital, Ludwig-Maximilians-University Munich, Munich, Germany.,Munich Cluster for Systems Neurology (SyNergy), Munich, Germany.,German Center for Neurodegenerative Disease (DZNE), Munich, Germany
| | - Geert Jan Biessels
- Department of Neurology and Neurosurgery, University Medical Center Utrecht Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands
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7
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Abstract
Cerebral small vessel disease (cSVD) is a major cause of stroke and dementia. This review summarizes recent developments in advanced neuroimaging of cSVD with a focus on clinical and research applications. In the first section, we highlight how advanced structural imaging techniques, including diffusion magnetic resonance imaging (MRI), enable improved detection of tissue damage, including characterization of tissue appearing normal on conventional MRI. These techniques enable progression to be monitored and may be useful as surrogate endpoint in clinical trials. Quantitative MRI, including iron and myelin imaging, provides insights into tissue composition on the molecular level. In the second section, we cover how advanced MRI techniques can demonstrate functional or dynamic abnormalities of the blood vessels, which could be targeted in mechanistic research and early-stage intervention trials. Such techniques include the use of dynamic contrast enhanced MRI to measure blood-brain barrier permeability, and MRI methods to assess cerebrovascular reactivity. In the third section, we discuss how the increased spatial resolution provided by ultrahigh field MRI at 7 T allows imaging of perforating arteries, and flow velocity and pulsatility within them. The advanced MRI techniques we describe are providing novel pathophysiological insights in cSVD and allow improved quantification of disease burden and progression. They have application in clinical trials, both in assessing novel therapeutic mechanisms, and as a sensitive endpoint to assess efficacy of interventions on parenchymal tissue damage. We also discuss challenges of these advanced techniques and suggest future directions for research.
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Affiliation(s)
- Hilde van den Brink
- Department of Neurology and
Neurosurgery, University Medical Center Utrecht Brain Center, Utrecht University,
Utrecht, The Netherlands
| | - Fergus N Doubal
- Centre for Clinical Brain Sciences, UK
Dementia Research Institute, University of Edinburgh, Edinburgh, UK
| | - Marco Duering
- Medical Image Analysis Center (MIAC AG)
and qbig, Department of Biomedical Engineering, University of Basel, Basel,
Switzerland,Marco Duering, Medical Image Analysis
Center (MIAC AG) and qbig, Department of Biomedical Engineering, University of
Basel, Marktgasse 8, Basel, CH-4051, Switzerland.
; @MarcoDuering
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8
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Wang S, Zhang F, Huang P, Hong H, Jiaerken Y, Yu X, Zhang R, Zeng Q, Zhang Y, Kikinis R, Rathi Y, Makris N, Lou M, Pasternak O, Zhang M, O'Donnell LJ. Superficial white matter microstructure affects processing speed in cerebral small vessel disease. Hum Brain Mapp 2022; 43:5310-5325. [PMID: 35822593 PMCID: PMC9812245 DOI: 10.1002/hbm.26004] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2022] [Revised: 06/10/2022] [Accepted: 06/15/2022] [Indexed: 01/15/2023] Open
Abstract
White matter hyperintensities (WMH) are a typical feature of cerebral small vessel disease (CSVD), which contributes to about 50% of dementias worldwide. Microstructural alterations in deep white matter (DWM) have been widely examined in CSVD. However, little is known about abnormalities in superficial white matter (SWM) and their relevance for processing speed, the main cognitive deficit in CSVD. In 141 CSVD patients, processing speed was assessed using Trail Making Test Part A. White matter abnormalities were assessed by WMH burden (volume on T2-FLAIR) and diffusion MRI measures. SWM imaging measures had a large contribution to processing speed, despite a relatively low SWM WMH burden. Across all imaging measures, SWM free water (FW) had the strongest association with processing speed, followed by SWM mean diffusivity (MD). SWM FW was the only marker to significantly increase between two subgroups with the lowest WMH burdens. When comparing two subgroups with the highest WMH burdens, the involvement of WMH in the SWM was accompanied by significant differences in processing speed and white matter microstructure. Mediation analysis revealed that SWM FW fully mediated the association between WMH volume and processing speed, while no mediation effect of MD or DWM FW was observed. Overall, results suggest that the SWM has an important contribution to processing speed, while SWM FW is a sensitive imaging marker associated with cognition in CSVD. This study extends the current understanding of CSVD-related dysfunction and suggests that the SWM, as an understudied region, can be a potential target for monitoring pathophysiological processes.
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Affiliation(s)
- Shuyue Wang
- Department of Radiologythe Second Affiliated Hospital of Zhejiang University School of MedicineChina
- Brigham and Women's Hospital, Harvard Medical SchoolBostonMassachusettsUSA
| | - Fan Zhang
- Brigham and Women's Hospital, Harvard Medical SchoolBostonMassachusettsUSA
| | - Peiyu Huang
- Department of Radiologythe Second Affiliated Hospital of Zhejiang University School of MedicineChina
| | - Hui Hong
- Department of Radiologythe Second Affiliated Hospital of Zhejiang University School of MedicineChina
| | - Yeerfan Jiaerken
- Department of Radiologythe Second Affiliated Hospital of Zhejiang University School of MedicineChina
| | - Xinfeng Yu
- Department of Radiologythe Second Affiliated Hospital of Zhejiang University School of MedicineChina
| | - Ruiting Zhang
- Department of Radiologythe Second Affiliated Hospital of Zhejiang University School of MedicineChina
| | - Qingze Zeng
- Department of Radiologythe Second Affiliated Hospital of Zhejiang University School of MedicineChina
| | - Yao Zhang
- Department of Radiologythe Second Affiliated Hospital of Zhejiang University School of MedicineChina
| | - Ron Kikinis
- Brigham and Women's Hospital, Harvard Medical SchoolBostonMassachusettsUSA
| | - Yogesh Rathi
- Brigham and Women's Hospital, Harvard Medical SchoolBostonMassachusettsUSA
| | - Nikos Makris
- Brigham and Women's Hospital, Harvard Medical SchoolBostonMassachusettsUSA
- Center for Morphometric AnalysisMassachusetts General Hospital, Harvard Medical SchoolBostonMassachusettsUSA
| | - Min Lou
- Department of Neurologythe Second Affiliated Hospital of Zhejiang University School of MedicineChina
| | - Ofer Pasternak
- Brigham and Women's Hospital, Harvard Medical SchoolBostonMassachusettsUSA
| | - Minming Zhang
- Department of Radiologythe Second Affiliated Hospital of Zhejiang University School of MedicineChina
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9
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Ma J, Hua XY, Zheng MX, Wu JJ, Huo BB, Xing XX, Gao X, Zhang H, Xu JG. Brain Metabolic Network Redistribution in Patients with White Matter Hyperintensities on MRI Analyzed with an Individualized Index Derived from 18F-FDG-PET/MRI. Korean J Radiol 2022; 23:986-997. [PMID: 36098344 PMCID: PMC9523232 DOI: 10.3348/kjr.2022.0320] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Revised: 08/01/2022] [Accepted: 08/02/2022] [Indexed: 11/15/2022] Open
Abstract
OBJECTIVE Whether metabolic redistribution occurs in patients with white matter hyperintensities (WMHs) on magnetic resonance imaging (MRI) is unknown. This study aimed 1) to propose a measure of the brain metabolic network for an individual patient and preliminarily apply it to identify impaired metabolic networks in patients with WMHs, and 2) to explore the clinical and imaging features of metabolic redistribution in patients with WMHs. MATERIALS AND METHODS This study included 50 patients with WMHs and 70 healthy controls (HCs) who underwent 18F-fluorodeoxyglucose-positron emission tomography/MRI. Various global property parameters according to graph theory and an individual parameter of brain metabolic network called "individual contribution index" were obtained. Parameter values were compared between the WMH and HC groups. The performance of the parameters in discriminating between the two groups was assessed using the area under the receiver operating characteristic curve (AUC). The correlation between the individual contribution index and Fazekas score was assessed, and the interaction between age and individual contribution index was determined. A generalized linear model was fitted with the individual contribution index as the dependent variable and the mean standardized uptake value (SUVmean) of nodes in the whole-brain network or seven classic functional networks as independent variables to determine their association. RESULTS The means ± standard deviations of the individual contribution index were (0.697 ± 10.9) × 10-3 and (0.0967 ± 0.0545) × 10-3 in the WMH and HC groups, respectively (p < 0.001). The AUC of the individual contribution index was 0.864 (95% confidence interval, 0.785-0.943). A positive correlation was identified between the individual contribution index and the Fazekas scores in patients with WMHs (r = 0.57, p < 0.001). Age and individual contribution index demonstrated a significant interaction effect on the Fazekas score. A significant direct association was observed between the individual contribution index and the SUVmean of the limbic network (p < 0.001). CONCLUSION The individual contribution index may demonstrate the redistribution of the brain metabolic network in patients with WMHs.
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Affiliation(s)
- Jie Ma
- Center of Rehabilitation Medicine, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Xu-Yun Hua
- Department of Traumatology and Orthopedics, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Mou-Xiong Zheng
- Department of Traumatology and Orthopedics, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Jia-Jia Wu
- Center of Rehabilitation Medicine, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Bei-Bei Huo
- School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Xiang-Xin Xing
- Center of Rehabilitation Medicine, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Xin Gao
- Panoramic Medical Imaging Diagnostic Center, Shanghai, China
| | - Han Zhang
- School of Biomedical Engineering, ShanghaiTech University, Shanghai, China.
| | - Jian-Guang Xu
- Center of Rehabilitation Medicine, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Engineering Research Center of Traditional Chinese Medicine Intelligent Rehabilitation, Ministry of Education, Shanghai, China.
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10
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da Silva PHR, Paschoal AM, Secchinatto KF, Zotin MCZ, Dos Santos AC, Viswanathan A, Pontes-Neto OM, Leoni RF. Contrast agent-free state-of-the-art magnetic resonance imaging on cerebral small vessel disease - Part 2: Diffusion tensor imaging and functional magnetic resonance imaging. NMR IN BIOMEDICINE 2022; 35:e4743. [PMID: 35429070 DOI: 10.1002/nbm.4743] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/17/2022] [Accepted: 04/04/2022] [Indexed: 06/14/2023]
Abstract
Cerebral small vessel disease (cSVD) has been widely studied using conventional magnetic resonance imaging (MRI) methods, although the association between MRI findings and clinical features of cSVD is not always concordant. We assessed the additional contribution of contrast agent-free, state-of-the-art MRI techniques, particularly diffusion tensor imaging (DTI) and functional magnetic resonance imaging (fMRI), to understand brain damage and structural and functional connectivity impairment related to cSVD. We performed a review following the PICOS worksheet and Search Strategy, including 152 original papers in English, published from 2000 to 2022. For each MRI method, we extracted information about their contributions regarding the origins, pathology, markers, and clinical outcomes in cSVD. In general, DTI studies have shown that changes in mean, radial, and axial diffusivity measures are related to the presence of cSVD. In addition to the classical deficit in executive functions and processing speed, fMRI studies indicate connectivity dysfunctions in other domains, such as sensorimotor, memory, and attention. Neuroimaging metrics have been correlated with the diagnosis, prognosis, and rehabilitation of patients with cSVD. In short, the application of contrast agent-free, state-of-the-art MRI techniques has provided a complete picture of cSVD markers and tools to explore questions that have not yet been clarified about this clinical condition. Longitudinal studies are desirable to look for causal relationships between image biomarkers and clinical outcomes.
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Affiliation(s)
| | - André Monteiro Paschoal
- Department of Physics, FFCLRP, University of São Paulo, Ribeirão Preto, São Paulo, Brazil
- Department of Medical Imaging, Hematology and Clinical Oncology, Ribeirão Preto Medical School, University of São Paulo, Ribeirão Preto, Brazil
| | | | - Maria Clara Zanon Zotin
- Department of Medical Imaging, Hematology and Clinical Oncology, Ribeirão Preto Medical School, University of São Paulo, Ribeirão Preto, Brazil
- J Philip Kistler Stroke Research Center, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Antônio Carlos Dos Santos
- Department of Medical Imaging, Hematology and Clinical Oncology, Ribeirão Preto Medical School, University of São Paulo, Ribeirão Preto, Brazil
| | - Anand Viswanathan
- J Philip Kistler Stroke Research Center, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Octavio M Pontes-Neto
- Department of Neurosciences and Behavioral Science, Ribeirão Preto Medical School, University of Sao Paulo, Ribeirão Preto, São Paulo, Brazil
| | - Renata Ferranti Leoni
- Department of Physics, FFCLRP, University of São Paulo, Ribeirão Preto, São Paulo, Brazil
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11
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Yin W, Zhou X, Li C, You M, Wan K, Zhang W, Zhu W, Li M, Zhu X, Qian Y, Sun Z. The Clustering Analysis of Time Properties in Patients With Cerebral Small Vessel Disease: A Dynamic Connectivity Study. Front Neurol 2022; 13:913241. [PMID: 35795790 PMCID: PMC9251301 DOI: 10.3389/fneur.2022.913241] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2022] [Accepted: 05/27/2022] [Indexed: 11/13/2022] Open
Abstract
PurposeThis study aimed to investigate the dynamic functional connectivity (DFC) pattern in cerebral small vessel disease (CSVD) and explore the relationships between DFC temporal properties and cognitive impairment in CSVD.MethodsFunctional data were collected from 67 CSVD patients, including 35 patients with subcortical vascular cognitive impairment (SVCI) and 32 cognitively unimpaired (CU) patients, as well as 35 healthy controls (HCs). The DFC properties were estimated by k-means clustering analysis. DFC strength analysis was used to explore the regional functional alterations between CSVD patients and HCs. Correlation analysis was used for DFC properties with cognition and SVD scores, respectively.ResultsThe DFC analysis showed three distinct connectivity states (state I: sparsely connected, state II: strongly connected, state III: intermediate pattern). Compared to HCs, CSVD patients exhibited an increased proportion in state I and decreased proportion in state II. Besides, CSVD patients dwelled longer in state I while dwelled shorter in state II. CSVD subgroup analyses showed that state I frequently occurred and dwelled longer in SVCI compared with CSVD-CU. Also, the internetwork (frontal-parietal lobe, frontal-occipital lobe) and intranetwork (frontal lobe, occipital lobe) functional activities were obviously decreased in CSVD. Furthermore, the fractional windows and mean dwell time (MDT) in state I were negatively correlated with cognition in CSVD but opposite to cognition in state II.ConclusionPatients with CSVD accounted for a higher proportion and dwelled longer mean time in the sparsely connected state, while presented lower proportion and shorter mean dwell time in the strongly connected state, which was more prominent in SVCI. The changes in the DFC are associated with altered cognition in CSVD. This study provides a better explanation of the potential mechanism of CSVD patients with cognitive impairment from the perspective of DFC.
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Affiliation(s)
- Wenwen Yin
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Xia Zhou
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Chenchen Li
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Mengzhe You
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Ke Wan
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Wei Zhang
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Wenhao Zhu
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Mingxu Li
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Xiaoqun Zhu
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Yinfeng Qian
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Zhongwu Sun
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
- *Correspondence: Zhongwu Sun
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12
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Dewenter A, Gesierich B, Ter Telgte A, Wiegertjes K, Cai M, Jacob MA, Marques JP, Norris DG, Franzmeier N, de Leeuw FE, Tuladhar AM, Duering M. Systematic validation of structural brain networks in cerebral small vessel disease. J Cereb Blood Flow Metab 2022; 42:1020-1032. [PMID: 34929104 PMCID: PMC9125482 DOI: 10.1177/0271678x211069228] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Cerebral small vessel disease (SVD) is considered a disconnection syndrome, which can be quantified using structural brain network analysis obtained from diffusion MRI. Network analysis is a demanding analysis approach and the added benefit over simpler diffusion MRI analysis is largely unexplored in SVD. In this pre-registered study, we assessed the clinical and technical validity of network analysis in two non-overlapping samples of SVD patients from the RUN DMC study (n = 52 for exploration and longitudinal analysis and n = 105 for validation). We compared two connectome pipelines utilizing single-shell or multi-shell diffusion MRI, while also systematically comparing different node and edge definitions. For clinical validation, we assessed the added benefit of network analysis in explaining processing speed and in detecting short-term disease progression. For technical validation, we determined test-retest repeatability.Our findings in clinical validation show that structural brain networks provide only a small added benefit over simpler global white matter diffusion metrics and do not capture short-term disease progression. Test-retest reliability was excellent for most brain networks. Our findings question the added value of brain network analysis in clinical applications in SVD and highlight the utility of simpler diffusion MRI based markers.
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Affiliation(s)
- Anna Dewenter
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Munich, Germany
| | - Benno Gesierich
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Munich, Germany
| | - Annemieke Ter Telgte
- Department of Neurology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands.,VASCage - Research Centre on Vascular Ageing and Stroke, Innsbruck, Austria
| | - Kim Wiegertjes
- Department of Neurology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Mengfei Cai
- Department of Neurology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Mina A Jacob
- Department of Neurology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands
| | - José P Marques
- Donders Institute for Brain, Cognition and Behavior, Radboud University, Nijmegen, The Netherlands
| | - David G Norris
- Donders Institute for Brain, Cognition and Behavior, Radboud University, Nijmegen, The Netherlands
| | - Nicolai Franzmeier
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Munich, Germany
| | - Frank-Erik de Leeuw
- Department of Neurology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Anil M Tuladhar
- Department of Neurology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Marco Duering
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Munich, Germany.,Department of Neurology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands.,Medical Image Analysis Center (MIAC) and Department of Biomedical Engineering, University of Basel, Basel, Switzerland
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13
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Janssen E, ter Telgte A, Verburgt E, de Jong JJA, Marques JP, Kessels RPC, Backes WH, Maas MC, Meijer FJA, Deinum J, Riksen NP, Tuladhar AM, de Leeuw FE. The Hyperintense study: Assessing the effects of induced blood pressure increase and decrease on MRI markers of cerebral small vessel disease: Study rationale and protocol. Eur Stroke J 2022; 7:331-338. [PMID: 36082259 PMCID: PMC9446329 DOI: 10.1177/23969873221100331] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Accepted: 04/24/2022] [Indexed: 11/16/2022] Open
Abstract
Background: Neuroimaging markers of cerebral small vessel disease (SVD) are common in
older individuals, but the pathophysiological mechanisms causing these
lesions remain poorly understood. Although hypertension is a major risk
factor for SVD, the direct causal effects of increased blood pressure are
unknown. The Hyperintense study is designed to examine cerebrovascular and
structural abnormalities, possibly preceding SVD, in young adults with
hypertension. These patients undergo a diagnostic work-up that requires
patients to temporarily discontinue their antihypertensive agents, often
leading to an increase in blood pressure followed by a decrease once
effective medication is restarted. This allows examination of the effects of
blood pressure increase and decrease on the cerebral small vessels. Methods: Hyperintense is a prospective observational cohort study in 50 hypertensive
adults (18–55 years) who will temporarily discontinue antihypertensive
medication for diagnostic purposes. MRI and clinical data is collected at
four timepoints: before medication withdrawal (baseline), once
antihypertensives are largely or completely withdrawn
(T = 1), when patients have restarted medication
(T = 2) and reached target blood pressure and 1 year
later (T = 3). The 3T MRI protocol includes conventional
structural sequences and advanced techniques to assess various aspects of
microvascular integrity, including blood-brain barrier function using
Dynamic Contrast Enhanced MRI, white matter integrity, and microperfusion.
Clinical assessments include motor and cognitive examinations and blood
sampling. Discussion: The Hyperintense study will improve the understanding of the
pathophysiological mechanisms following hypertension that may cause SVD.
This knowledge can ultimately help to identify new targets for treatment of
SVD, aimed at prevention or limiting disease progression.
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Affiliation(s)
- Esther Janssen
- Department of Neurology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen Medical Centre, Nijmegen, The Netherlands
- Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, Nijmegen, The Netherlands
| | | | - Esmée Verburgt
- Department of Neurology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen Medical Centre, Nijmegen, The Netherlands
- Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, Nijmegen, The Netherlands
| | - Joost JA de Jong
- School for Mental Health & Neuroscience, Maastricht University Medical Center, Maastricht, The Netherlands
| | - José P Marques
- Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, Nijmegen, The Netherlands
| | - Roy PC Kessels
- Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, Nijmegen, The Netherlands
- Vincent van Gogh Institute for Psychiatry, Venray, The Netherlands
- Department of Medical Psychology and Radboudumc Alzheimer Center, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Walter H Backes
- School for Mental Health & Neuroscience, Maastricht University Medical Center, Maastricht, The Netherlands
| | - Marnix C Maas
- Department of Medical Imaging, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Frederick JA Meijer
- Department of Medical Imaging, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Jaap Deinum
- Department of Internal Medicine and Radboud Institute for Molecular Life Sciences, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Niels P Riksen
- Department of Internal Medicine and Radboud Institute for Molecular Life Sciences, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Anil M Tuladhar
- Department of Neurology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen Medical Centre, Nijmegen, The Netherlands
- Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, Nijmegen, The Netherlands
| | - Frank-Erik de Leeuw
- Department of Neurology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen Medical Centre, Nijmegen, The Netherlands
- Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, Nijmegen, The Netherlands
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14
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Vemuri P, Decarli CS, Duering M. Imaging Markers of Vascular Brain Health: Quantification, Clinical Implications, and Future Directions. Stroke 2022; 53:416-426. [PMID: 35000423 PMCID: PMC8830603 DOI: 10.1161/strokeaha.120.032611] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Abstract
Cerebrovascular disease (CVD) manifests through a broad spectrum of mechanisms that negatively impact brain and cognitive health. Oftentimes, CVD changes (excluding acute stroke) are insufficiently considered in aging and dementia studies which can lead to an incomplete picture of the etiologies contributing to the burden of cognitive impairment. Our goal with this focused review is 3-fold. First, we provide a research update on the current magnetic resonance imaging methods that can measure CVD lesions as well as early CVD-related brain injury specifically related to small vessel disease. Second, we discuss the clinical implications and relevance of these CVD imaging markers for cognitive decline, incident dementia, and disease progression in Alzheimer disease, and Alzheimer-related dementias. Finally, we present our perspective on the outlook and challenges that remain in the field. With the increased research interest in this area, we believe that reliable CVD imaging biomarkers for aging and dementia studies are on the horizon.
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Affiliation(s)
| | - Charles S. Decarli
- Departments of Neurology and Center for Neuroscience, University of California at Davis, Sacramento, California, USA
| | - Marco Duering
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Germany
- Medical Image Analysis Center (MIAC AG) and qbig, Department of Biomedical Engineering, University of Basel, Switzerland
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15
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Li T, Yang Y, Krueger F, Feng C, Wang J. Static and Dynamic Topological Organizations of the Costly Punishment Network Predict Individual Differences in Punishment Propensity. Cereb Cortex 2021; 32:4012-4024. [PMID: 34905766 DOI: 10.1093/cercor/bhab462] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Revised: 11/12/2021] [Accepted: 11/13/2021] [Indexed: 12/17/2022] Open
Abstract
Human costly punishment plays a vital role in maintaining social norms. Recently, a brain network model is conceptually proposed indicating that the implement of costly punishment depends on a subset of nodes in three high-level networks. This model, however, has not yet been empirically examined from an integrated perspective of large-scale brain networks. Here, we conducted comprehensive graph-based network analyses of resting-state functional magnetic resonance imaging data to explore system-level characteristics of intrinsic functional connectivity among 18 regions related to costly punishment. Nontrivial organizations (small-worldness, connector hubs, and high flexibility) were found that were qualitatively stable across participants and over time but quantitatively exhibited low test-retest reliability. The organizations were predictive of individual costly punishment propensities, which was reproducible on independent samples and robust against different analytical strategies and parameter settings. Moreover, the prediction was specific to system-level network organizations (rather than interregional functional connectivity) derived from positive (rather than negative or combined) connections among the specific (rather than randomly chosen) subset of regions from the three high-order (rather than primary) networks. Collectively, these findings suggest that human costly punishment emerges from integrative behaviors among specific regions in certain functional networks, lending support to the brain network model for costly punishment.
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Affiliation(s)
- Ting Li
- Institute for Brain Research and Rehabilitation, South China Normal University, 510631 Guangzhou, China
| | - Yuping Yang
- Institute for Brain Research and Rehabilitation, South China Normal University, 510631 Guangzhou, China
| | - Frank Krueger
- School of Systems Biology, George Mason University, Fairfax, 22030 VA, USA.,Department of Psychology, George Mason University, Fairfax, 22030 VA, USA
| | - Chunliang Feng
- School of Psychology, South China Normal University, 510631 Guangzhou, China.,Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education, 510631 Guangzhou, China.,Center for Studies of Psychological Application, South China Normal University, 510631 Guangzhou, China.,Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, 510631 Guangzhou, China
| | - Jinhui Wang
- Institute for Brain Research and Rehabilitation, South China Normal University, 510631 Guangzhou, China.,Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education, 510631 Guangzhou, China.,Center for Studies of Psychological Application, South China Normal University, 510631 Guangzhou, China.,Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, 510631 Guangzhou, China
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16
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Schulz M, Malherbe C, Cheng B, Thomalla G, Schlemm E. Functional connectivity changes in cerebral small vessel disease - a systematic review of the resting-state MRI literature. BMC Med 2021; 19:103. [PMID: 33947394 PMCID: PMC8097883 DOI: 10.1186/s12916-021-01962-1] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/09/2021] [Accepted: 03/17/2021] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND Cerebral small vessel disease (CSVD) is a common neurological disease present in the ageing population that is associated with an increased risk of dementia and stroke. Damage to white matter tracts compromises the substrate for interneuronal connectivity. Analysing resting-state functional magnetic resonance imaging (fMRI) can reveal dysfunctional patterns of brain connectivity and contribute to explaining the pathophysiology of clinical phenotypes in CSVD. MATERIALS AND METHODS This systematic review provides an overview of methods and results of recent resting-state functional MRI studies in patients with CSVD. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) protocol, a systematic search of the literature was performed. RESULTS Of 493 studies that were screened, 44 reports were identified that investigated resting-state fMRI connectivity in the context of cerebral small vessel disease. The risk of bias and heterogeneity of results were moderate to high. Patterns associated with CSVD included disturbed connectivity within and between intrinsic brain networks, in particular the default mode, dorsal attention, frontoparietal control, and salience networks; decoupling of neuronal activity along an anterior-posterior axis; and increases in functional connectivity in the early stage of the disease. CONCLUSION The recent literature provides further evidence for a functional disconnection model of cognitive impairment in CSVD. We suggest that the salience network might play a hitherto underappreciated role in this model. Low quality of evidence and the lack of preregistered multi-centre studies remain challenges to be overcome in the future.
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Affiliation(s)
- Maximilian Schulz
- Department of Neurology, University Medical Centre Hamburg-Eppendorf, Hamburg, Germany
| | - Caroline Malherbe
- Department of Neurology, University Medical Centre Hamburg-Eppendorf, Hamburg, Germany
- Department of Computational Neuroscience, University Medical Centre Hamburg-Eppendorf, Hamburg, Germany
| | - Bastian Cheng
- Department of Neurology, University Medical Centre Hamburg-Eppendorf, Hamburg, Germany
| | - Götz Thomalla
- Department of Neurology, University Medical Centre Hamburg-Eppendorf, Hamburg, Germany
| | - Eckhard Schlemm
- Department of Neurology, University Medical Centre Hamburg-Eppendorf, Hamburg, Germany.
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17
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van den Brink H, Kopczak A, Arts T, Onkenhout L, Siero JC, Zwanenburg JJ, Duering M, Blair GW, Doubal FN, Stringer MS, Thrippleton MJ, Kuijf HJ, de Luca A, Hendrikse J, Wardlaw JM, Dichgans M, Biessels GJ. Zooming in on cerebral small vessel function in small vessel diseases with 7T MRI: Rationale and design of the "ZOOM@SVDs" study. CEREBRAL CIRCULATION - COGNITION AND BEHAVIOR 2021; 2:100013. [PMID: 36324717 PMCID: PMC9616370 DOI: 10.1016/j.cccb.2021.100013] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/18/2021] [Revised: 04/14/2021] [Accepted: 04/15/2021] [Indexed: 06/01/2023]
Abstract
Background Cerebral small vessel diseases (SVDs) are a major cause of stroke and dementia. Yet, specific treatment strategies are lacking in part because of a limited understanding of the underlying disease processes. There is therefore an urgent need to study SVDs at their core, the small vessels themselves. Objective This paper presents the rationale and design of the ZOOM@SVDs study, which aims to establish measures of cerebral small vessel dysfunction on 7T MRI as novel disease markers of SVDs. Methods ZOOM@SVDs is a prospective observational cohort study with two years follow-up. ZOOM@SVDs recruits participants with Cerebral Autosomal Dominant Arteriopathy with Subcortical Infarcts and Leukoencephalopathy (CADASIL, N = 20), sporadic SVDs (N = 60), and healthy controls (N = 40). Participants undergo 7T brain MRI to assess different aspects of small vessel function including small vessel reactivity, cerebral perforating artery flow, and pulsatility. Extensive work-up at baseline and follow-up further includes clinical and neuropsychological assessment as well as 3T brain MRI to assess conventional SVD imaging markers. Measures of small vessel dysfunction are compared between patients and controls, and related to the severity of clinical and conventional MRI manifestations of SVDs. Discussion ZOOM@SVDs will deliver novel markers of cerebral small vessel function in patients with monogenic and sporadic forms of SVDs, and establish their relation with disease burden and progression. These small vessel markers can support etiological studies in SVDs and may serve as surrogate outcome measures in future clinical trials to show target engagement of drugs directed at the small vessels.
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Key Words
- ASL, Arterial Spin Labeling
- BOLD, Blood Oxygenation Level-Dependent
- CADASIL
- CADASIL, Cerebral Autosomal Dominant Arteriopathy with Leukoencephalopathy and Subcortical Infarcts
- CDR, Clinical Dementia Rating scale
- CERAD+, Consortium to Establish a Disease Registry for Alzheimer's Disease Plus battery
- CES-D, Center for Epidemiologic Studies Depression Scale
- CO2, Carbon Dioxide
- CSF, Cerebrospinal Fluid
- Cerebral small vessel disease
- DTI, Diffusion Tensor Imaging
- EPIC, European Prospective Investigation into Cancer and Nutrition
- EtCO2, End-tidal Carbon Dioxide
- FLAIR, Fluid Attenuated Inversion Recovery
- FOV, Field Of View
- FWHM, Full-Width-at-Half-Maximum
- GE, Gradient Echo
- GM, Grey Matter
- GPRS, General Packet Radio Service
- HRF, Hemodynamic Response Function
- High field strength MRI
- LMU, Ludwig-Maximilians-Universität
- MMSE, Mini-Mental State Examination
- NAWM, Normal Appearing White Matter
- NIHSS, National Institute for Health Stroke Scale
- PI, Pulsatility Index
- ROI, Region Of Interest
- SPPB, Short Physical Performance Battery
- SVDs, Small Vessel Diseases
- SWI, Susceptibility Weighted Imaging
- Small vessel function
- Sporadic SVD
- Stroke
- TE, Echo Time
- TI, Inversion Time
- TR, Repetition Time
- TSE, Turbo Spin Echo
- UMCU, University Medical Center Utrecht
- Vmax, Maximum velocity
- Vmean, Mean velocity
- Vmin, Minimum velocity
- WM, White Matter
- WMH, White Matter Hyperintensity
- fMRI, Functional Magnetic Resonance Imaging
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Affiliation(s)
- Hilde van den Brink
- Department of Neurology and Neurosurgery, UMC Utrecht Brain Center, University Medical Center Utrecht, Heidelberglaan 100, Utrecht, 3508 GA, the Netherlands
| | - Anna Kopczak
- Institute for Stroke and Dementia Research, University Hospital, Ludwig-Maximilians-Universität, Munich, Germany
| | - Tine Arts
- Department of Radiology, Center for Image Sciences, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Laurien Onkenhout
- Department of Neurology and Neurosurgery, UMC Utrecht Brain Center, University Medical Center Utrecht, Heidelberglaan 100, Utrecht, 3508 GA, the Netherlands
| | - Jeroen C.W. Siero
- Department of Radiology, Center for Image Sciences, University Medical Center Utrecht, Utrecht, the Netherlands
- Spinoza Centre for Neuroimaging Amsterdam, Amsterdam, the Netherlands
| | - Jaco J.M. Zwanenburg
- Department of Radiology, Center for Image Sciences, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Marco Duering
- Institute for Stroke and Dementia Research, University Hospital, Ludwig-Maximilians-Universität, Munich, Germany
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
- German Center for Neurodegenerative Disease (DZNE), Munich, Germany
| | - Gordon W. Blair
- Brain Research Imaging Centre, Centre for Clinical Brain Sciences, UK Dementia Research Institute Centre at the University of Edinburgh, Edinburgh, United Kingdom
| | - Fergus N. Doubal
- Brain Research Imaging Centre, Centre for Clinical Brain Sciences, UK Dementia Research Institute Centre at the University of Edinburgh, Edinburgh, United Kingdom
| | - Michael S. Stringer
- Brain Research Imaging Centre, Centre for Clinical Brain Sciences, UK Dementia Research Institute Centre at the University of Edinburgh, Edinburgh, United Kingdom
| | - Michael J. Thrippleton
- Brain Research Imaging Centre, Centre for Clinical Brain Sciences, UK Dementia Research Institute Centre at the University of Edinburgh, Edinburgh, United Kingdom
| | - Hugo J. Kuijf
- Image Sciences Institute, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Alberto de Luca
- Department of Neurology and Neurosurgery, UMC Utrecht Brain Center, University Medical Center Utrecht, Heidelberglaan 100, Utrecht, 3508 GA, the Netherlands
- Brain Research Imaging Centre, Centre for Clinical Brain Sciences, UK Dementia Research Institute Centre at the University of Edinburgh, Edinburgh, United Kingdom
| | - Jeroen Hendrikse
- Department of Radiology, Center for Image Sciences, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Joanna M. Wardlaw
- Brain Research Imaging Centre, Centre for Clinical Brain Sciences, UK Dementia Research Institute Centre at the University of Edinburgh, Edinburgh, United Kingdom
| | - Martin Dichgans
- Institute for Stroke and Dementia Research, University Hospital, Ludwig-Maximilians-Universität, Munich, Germany
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
- German Center for Neurodegenerative Disease (DZNE), Munich, Germany
| | - Geert Jan Biessels
- Department of Neurology and Neurosurgery, UMC Utrecht Brain Center, University Medical Center Utrecht, Heidelberglaan 100, Utrecht, 3508 GA, the Netherlands
| | - SVDs@target group
- Department of Neurology and Neurosurgery, UMC Utrecht Brain Center, University Medical Center Utrecht, Heidelberglaan 100, Utrecht, 3508 GA, the Netherlands
- Institute for Stroke and Dementia Research, University Hospital, Ludwig-Maximilians-Universität, Munich, Germany
- Department of Radiology, Center for Image Sciences, University Medical Center Utrecht, Utrecht, the Netherlands
- Spinoza Centre for Neuroimaging Amsterdam, Amsterdam, the Netherlands
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
- German Center for Neurodegenerative Disease (DZNE), Munich, Germany
- Brain Research Imaging Centre, Centre for Clinical Brain Sciences, UK Dementia Research Institute Centre at the University of Edinburgh, Edinburgh, United Kingdom
- Image Sciences Institute, University Medical Center Utrecht, Utrecht, the Netherlands
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18
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Gesierich B, Tuladhar AM, ter Telgte A, Wiegertjes K, Konieczny MJ, Finsterwalder S, Hübner M, Pirpamer L, Koini M, Abdulkadir A, Franzmeier N, Norris DG, Marques JP, zu Eulenburg P, Ewers M, Schmidt R, de Leeuw F, Duering M. Alterations and test-retest reliability of functional connectivity network measures in cerebral small vessel disease. Hum Brain Mapp 2020; 41:2629-2641. [PMID: 32087047 PMCID: PMC7294060 DOI: 10.1002/hbm.24967] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2019] [Revised: 01/30/2020] [Accepted: 02/13/2020] [Indexed: 12/19/2022] Open
Abstract
While structural network analysis consolidated the hypothesis of cerebral small vessel disease (SVD) being a disconnection syndrome, little is known about functional changes on the level of brain networks. In patients with genetically defined SVD (CADASIL, n = 41) and sporadic SVD (n = 46), we independently tested the hypothesis that functional networks change with SVD burden and mediate the effect of disease burden on cognitive performance, in particular slowing of processing speed. We further determined test-retest reliability of functional network measures in sporadic SVD patients participating in a high-frequency (monthly) serial imaging study (RUN DMC-InTENse, median: 8 MRIs per participant). Functional networks for the whole brain and major subsystems (i.e., default mode network, DMN; fronto-parietal task control network, FPCN; visual network, VN; hand somatosensory-motor network, HSMN) were constructed based on resting-state multi-band functional MRI. In CADASIL, global efficiency (a graph metric capturing network integration) of the DMN was lower in patients with high disease burden (standardized beta = -.44; p [corrected] = .035) and mediated the negative effect of disease burden on processing speed (indirect path: std. beta = -.20, p = .047; direct path: std. beta = -.19, p = .25; total effect: std. beta = -.39, p = .02). The corresponding analyses in sporadic SVD showed no effect. Intraclass correlations in the high-frequency serial MRI dataset of the sporadic SVD patients revealed poor test-retest reliability and analysis of individual variability suggested an influence of age, but not disease burden, on global efficiency. In conclusion, our results suggest that changes in functional connectivity networks mediate the effect of SVD-related brain damage on cognitive deficits. However, limited reliability of functional network measures, possibly due to age-related comorbidities, impedes the analysis in elderly SVD patients.
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Affiliation(s)
- Benno Gesierich
- Institute for Stroke and Dementia Research (ISD)University HospitalMunichGermany
| | - Anil Man Tuladhar
- Department of Neurology, Donders Institute for Brain, Cognition and BehaviourRadboud University Medical CenterNijmegenThe Netherlands
| | - Annemieke ter Telgte
- Department of Neurology, Donders Institute for Brain, Cognition and BehaviourRadboud University Medical CenterNijmegenThe Netherlands
| | - Kim Wiegertjes
- Department of Neurology, Donders Institute for Brain, Cognition and BehaviourRadboud University Medical CenterNijmegenThe Netherlands
| | - Marek J. Konieczny
- Institute for Stroke and Dementia Research (ISD)University HospitalMunichGermany
| | - Sofia Finsterwalder
- Institute for Stroke and Dementia Research (ISD)University HospitalMunichGermany
| | - Mathias Hübner
- Institute for Stroke and Dementia Research (ISD)University HospitalMunichGermany
| | - Lukas Pirpamer
- Department of NeurologyMedical University of GrazGrazAustria
| | - Marisa Koini
- Department of NeurologyMedical University of GrazGrazAustria
| | - Ahmed Abdulkadir
- University Hospital of Old Age Psychiatry, Universitäre Psychiatrische Dienste (UPD) BernUniversity of BernBernSwitzerland
| | - Nicolai Franzmeier
- Institute for Stroke and Dementia Research (ISD)University HospitalMunichGermany
| | - David G. Norris
- Donders Institute for Brain, Cognition, and BehaviorRadboud UniversityNijmegenThe Netherlands
| | - José P. Marques
- Donders Institute for Brain, Cognition, and BehaviorRadboud UniversityNijmegenThe Netherlands
| | - Peter zu Eulenburg
- German Center for Vertigo and Balance DisordersUniversity HospitalMunichGermany
| | - Michael Ewers
- Institute for Stroke and Dementia Research (ISD)University HospitalMunichGermany
| | | | - Frank‐Erik de Leeuw
- Department of Neurology, Donders Institute for Brain, Cognition and BehaviourRadboud University Medical CenterNijmegenThe Netherlands
| | - Marco Duering
- Institute for Stroke and Dementia Research (ISD)University HospitalMunichGermany
- Department of Neurology, Donders Institute for Brain, Cognition and BehaviourRadboud University Medical CenterNijmegenThe Netherlands
- Munich Cluster for Systems Neurology (SyNergy)MunichGermany
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