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Artikel ; Online: Random expert sampling for deep learning segmentation of acute ischemic stroke on non-contrast CT.

Ostmeier, Sophie / Axelrod, Brian / Liu, Yongkai / Yu, Yannan / Jiang, Bin / Yuen, Nicole / Pulli, Benjamin / Verhaaren, Benjamin F J / Kaka, Hussam / Wintermark, Max / Michel, Patrik / Mahammedi, Abdelkader / Federau, Christian / Lansberg, Maarten G / Albers, Gregory W / Moseley, Michael E / Zaharchuk, Gregory / Heit, Jeremy J

Journal of neurointerventional surgery

2024

Abstract: Background: Outlining acutely infarcted tissue on non-contrast CT is a challenging task for which human inter-reader agreement is limited. We explored two different methods for training a supervised deep learning algorithm: one that used a segmentation ... ...

Abstract	Background: Outlining acutely infarcted tissue on non-contrast CT is a challenging task for which human inter-reader agreement is limited. We explored two different methods for training a supervised deep learning algorithm: one that used a segmentation defined by majority vote among experts and another that trained randomly on separate individual expert segmentations. Methods: The data set consisted of 260 non-contrast CT studies in 233 patients with acute ischemic stroke recruited from the multicenter DEFUSE 3 (Endovascular Therapy Following Imaging Evaluation for Ischemic Stroke 3) trial. Additional external validation was performed using 33 patients with matched stroke onset times from the University Hospital Lausanne. A benchmark U-Net was trained on the reference annotations of three experienced neuroradiologists to segment ischemic brain tissue using majority vote and random expert sampling training schemes. The median of volume, overlap, and distance segmentation metrics were determined for agreement in lesion segmentations between (1) three experts, (2) the majority model and each expert, and (3) the random model and each expert. The two sided Wilcoxon signed rank test was used to compare performances (1) to 2) and (1) to (3). We further compared volumes with the 24 hour follow-up diffusion weighted imaging (DWI, final infarct core) and correlations with clinical outcome (modified Rankin Scale (mRS) at 90 days) with the Spearman method. Results: The random model outperformed the inter-expert agreement ((1) to (2)) and the majority model ((1) to (3)) (dice 0.51±0.04 vs 0.36±0.05 (P Conclusion: The random model for ischemic injury delineation on non-contrast CT surpassed the inter-expert agreement ((1) to (2)) and the performance of the majority model ((1) to (3)). We showed that the random model volumetric measures of the model were consistent with 24 hour follow-up DWI.
Sprache	Englisch
Erscheinungsdatum	2024-02-01
Erscheinungsland	England
Dokumenttyp	Journal Article
ZDB-ID	2514982-9
ISSN	1759-8486 ; 1759-8478
ISSN (online)	1759-8486
ISSN	1759-8478
DOI	10.1136/jnis-2023-021283
Datenquelle	MEDical Literature Analysis and Retrieval System OnLINE

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Buch ; Online: Random Expert Sampling for Deep Learning Segmentation of Acute Ischemic Stroke on Non-contrast CT

Ostmeier, Sophie / Axelrod, Brian / Pulli, Benjamin / Verhaaren, Benjamin F. J. / Mahammedi, Abdelkader / Liu, Yongkai / Federau, Christian / Zaharchuk, Greg / Heit, Jeremy J.

2023

Abstract: Purpose: Multi-expert deep learning training methods to automatically quantify ischemic brain tissue on Non-Contrast CT Materials and Methods: The data set consisted of 260 Non-Contrast CTs from 233 patients of acute ischemic stroke patients recruited in ...

Abstract	Purpose: Multi-expert deep learning training methods to automatically quantify ischemic brain tissue on Non-Contrast CT Materials and Methods: The data set consisted of 260 Non-Contrast CTs from 233 patients of acute ischemic stroke patients recruited in the DEFUSE 3 trial. A benchmark U-Net was trained on the reference annotations of three experienced neuroradiologists to segment ischemic brain tissue using majority vote and random expert sampling training schemes. We used a one-sided Wilcoxon signed-rank test on a set of segmentation metrics to compare bootstrapped point estimates of the training schemes with the inter-expert agreement and ratio of variance for consistency analysis. We further compare volumes with the 24h-follow-up DWI (final infarct core) in the patient subgroup with full reperfusion and we test volumes for correlation to the clinical outcome (mRS after 30 and 90 days) with the Spearman method. Results: Random expert sampling leads to a model that shows better agreement with experts than experts agree among themselves and better agreement than the agreement between experts and a majority-vote model performance (Surface Dice at Tolerance 5mm improvement of 61% to 0.70 +- 0.03 and Dice improvement of 25% to 0.50 +- 0.04). The model-based predicted volume similarly estimated the final infarct volume and correlated better to the clinical outcome than CT perfusion. Conclusion: A model trained on random expert sampling can identify the presence and location of acute ischemic brain tissue on Non-Contrast CT similar to CT perfusion and with better consistency than experts. This may further secure the selection of patients eligible for endovascular treatment in less specialized hospitals.
Schlagwörter	Computer Science - Computer Vision and Pattern Recognition
Thema/Rubrik (Code)	616
Erscheinungsdatum	2023-09-07
Erscheinungsland	us
Dokumenttyp	Buch ; Online
Datenquelle	BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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Artikel ; Online: Non-inferiority of deep learning ischemic stroke segmentation on non-contrast CT within 16-hours compared to expert neuroradiologists.

Ostmeier, Sophie / Axelrod, Brian / Verhaaren, Benjamin F J / Christensen, Soren / Mahammedi, Abdelkader / Liu, Yongkai / Pulli, Benjamin / Li, Li-Jia / Zaharchuk, Greg / Heit, Jeremy J

Scientific reports

2023 Band 13, Heft 1, Seite(n) 16153

Abstract: We determined if a convolutional neural network (CNN) deep learning model can accurately segment acute ischemic changes on non-contrast CT compared to neuroradiologists. Non-contrast CT (NCCT) examinations from 232 acute ischemic stroke patients who were ...

Abstract	We determined if a convolutional neural network (CNN) deep learning model can accurately segment acute ischemic changes on non-contrast CT compared to neuroradiologists. Non-contrast CT (NCCT) examinations from 232 acute ischemic stroke patients who were enrolled in the DEFUSE 3 trial were included in this study. Three experienced neuroradiologists independently segmented hypodensity that reflected the ischemic core on each scan. The neuroradiologist with the most experience (expert A) served as the ground truth for deep learning model training. Two additional neuroradiologists' (experts B and C) segmentations were used for data testing. The 232 studies were randomly split into training and test sets. The training set was further randomly divided into 5 folds with training and validation sets. A 3-dimensional CNN architecture was trained and optimized to predict the segmentations of expert A from NCCT. The performance of the model was assessed using a set of volume, overlap, and distance metrics using non-inferiority thresholds of 20%, 3 ml, and 3 mm, respectively. The optimized model trained on expert A was compared to test experts B and C. We used a one-sided Wilcoxon signed-rank test to test for the non-inferiority of the model-expert compared to the inter-expert agreement. The final model performance for the ischemic core segmentation task reached a performance of 0.46 ± 0.09 Surface Dice at Tolerance 5mm and 0.47 ± 0.13 Dice when trained on expert A. Compared to the two test neuroradiologists the model-expert agreement was non-inferior to the inter-expert agreement, [Formula: see text]. The before, CNN accurately delineates the hypodense ischemic core on NCCT in acute ischemic stroke patients with an accuracy comparable to neuroradiologists.
Mesh-Begriff(e)	Humans ; Ischemic Stroke/diagnostic imaging ; Deep Learning ; Neural Networks, Computer ; Radiologists ; Tomography, X-Ray Computed ; Stroke/diagnostic imaging
Sprache	Englisch
Erscheinungsdatum	2023-09-26
Erscheinungsland	England
Dokumenttyp	Journal Article
ZDB-ID	2615211-3
ISSN	2045-2322 ; 2045-2322
ISSN (online)	2045-2322
ISSN	2045-2322
DOI	10.1038/s41598-023-42961-x
Datenquelle	MEDical Literature Analysis and Retrieval System OnLINE

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Artikel ; Online: Semiautomated Detection of Early Infarct Signs on Noncontrast CT Improves Interrater Agreement.

Christensen, Soren / Demeestere, Jelle / Verhaaren, Benjamin F J / Heit, Jeremy J / Von Stein, Erica Leah / Madill, Evan S / Kennedy Loube, Deanne / Dugue, Rachelle / Rengarajan, Sophie / Mlynash, Michael / Albers, Gregory W / Lemmens, Robin / Lansberg, Maarten G

Stroke

2023 Band 54, Heft 12, Seite(n) 3090–3096

Abstract: Background: Acute ischemic infarct identification on noncontrast computed tomography (NCCT) is highly variable between raters. A semiautomated method for segmentation of acute ischemic lesions on NCCT may improve interrater reliability.: Methods: ... ...

Abstract	Background: Acute ischemic infarct identification on noncontrast computed tomography (NCCT) is highly variable between raters. A semiautomated method for segmentation of acute ischemic lesions on NCCT may improve interrater reliability. Methods: Patients with successful endovascular reperfusion from the DEFUSE 3 trial (Endovascular Therapy Following Imaging Evaluation for Ischemic Stroke) were included. We created relative NCCT (rNCCT) color-gradient overlays by comparing the density of a voxel on NCCT to the homologous region in the contralateral hemisphere. Regions with a relative hypodensity of at least 5% were visualized. We coregistered baseline and follow-up images. Two neuroradiologists and 6 nonradiologists segmented the acute ischemic lesion on the baseline scans with 2 methods: (1) manually outlining hypodense regions on the NCCT (unassisted segmentation) and (2) manually excluding areas deemed outside of the ischemic lesion on the rNCCT color map (rNCCT-assisted segmentation). Voxelwise interrater agreement was quantified using the Dice similarity coefficient and volumetric agreement between raters with the detection index (DI), defined as the true positive volume minus the false positive volume. Results: From a total of 92, we included 61 patients. Median age was 59 (64-77), and 57% were female. Stroke onset was known in 39%. Onset to NCCT was median, 8.5 hours (7-11) and median 10 hours (8.4-11.5) in patients with known and unknown onset, respectively. Compared with unassisted NCCT segmentation, rNCCT-assisted segmentation increased the Dice similarity coefficient by >50% for neuroradiologists (Dice similarity coefficient, 0.38 versus 0.83; Conclusions: The high variability of manual segmentations of the acute ischemic lesion on NCCT is greatly reduced using semiautomated rNCCT. The rNCCT map may therefore aid acute infarct detection and provide more reliable infarct estimates for clinicians with less experience.
Mesh-Begriff(e)	Female ; Humans ; Male ; Middle Aged ; Brain Ischemia/diagnostic imaging ; Brain Ischemia/therapy ; Infarction ; Ischemic Stroke ; Reproducibility of Results ; Stroke/diagnostic imaging ; Stroke/therapy ; Tomography, X-Ray Computed/methods ; Follow-Up Studies
Sprache	Englisch
Erscheinungsdatum	2023-11-01
Erscheinungsland	United States
Dokumenttyp	Journal Article
ZDB-ID	80381-9
ISSN	1524-4628 ; 0039-2499 ; 0749-7954
ISSN (online)	1524-4628
ISSN	0039-2499 ; 0749-7954
DOI	10.1161/STROKEAHA.123.044058
Datenquelle	MEDical Literature Analysis and Retrieval System OnLINE

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Artikel ; Online: USE-Evaluator: Performance metrics for medical image segmentation models supervised by uncertain, small or empty reference annotations in neuroimaging.

Ostmeier, Sophie / Axelrod, Brian / Isensee, Fabian / Bertels, Jeroen / Mlynash, Michael / Christensen, Soren / Lansberg, Maarten G / Albers, Gregory W / Sheth, Rajen / Verhaaren, Benjamin F J / Mahammedi, Abdelkader / Li, Li-Jia / Zaharchuk, Greg / Heit, Jeremy J

Medical image analysis

2023 Band 90, Seite(n) 102927

Abstract: Performance metrics for medical image segmentation models are used to measure the agreement between the reference annotation and the predicted segmentation. Usually, overlap metrics, such as the Dice, are used as a metric to evaluate the performance of ... ...

Abstract	Performance metrics for medical image segmentation models are used to measure the agreement between the reference annotation and the predicted segmentation. Usually, overlap metrics, such as the Dice, are used as a metric to evaluate the performance of these models in order for results to be comparable. However, there is a mismatch between the distributions of cases and the difficulty level of segmentation tasks in public data sets compared to clinical practice. Common metrics used to assess performance fail to capture the impact of this mismatch, particularly when dealing with datasets in clinical settings that involve challenging segmentation tasks, pathologies with low signal, and reference annotations that are uncertain, small, or empty. Limitations of common metrics may result in ineffective machine learning research in designing and optimizing models. To effectively evaluate the clinical value of such models, it is essential to consider factors such as the uncertainty associated with reference annotations, the ability to accurately measure performance regardless of the size of the reference annotation volume, and the classification of cases where reference annotations are empty. We study how uncertain, small, and empty reference annotations influence the value of metrics on a stroke in-house data set regardless of the model. We examine metrics behavior on the predictions of a standard deep learning framework in order to identify suitable metrics in such a setting. We compare our results to the BRATS 2019 and Spinal Cord public data sets. We show how uncertain, small, or empty reference annotations require a rethinking of the evaluation. The evaluation code was released to encourage further analysis of this topic https://github.com/SophieOstmeier/UncertainSmallEmpty.git.
Sprache	Englisch
Erscheinungsdatum	2023-08-10
Erscheinungsland	Netherlands
Dokumenttyp	Journal Article
ZDB-ID	1356436-5
ISSN	1361-8423 ; 1361-8431 ; 1361-8415
ISSN (online)	1361-8423 ; 1361-8431
ISSN	1361-8415
DOI	10.1016/j.media.2023.102927
Datenquelle	MEDical Literature Analysis and Retrieval System OnLINE

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Buch ; Online: Non-inferiority of Deep Learning Acute Ischemic Stroke Segmentation on Non-Contrast CT Compared to Expert Neuroradiologists

Ostmeier, Sophie / Axelrod, Brian / Verhaaren, Benjamin F. J. / Christensen, Soren / Mahammedi, Abdelkader / Liu, Yongkai / Pulli, Benjamin / Li, Li-Jia / Zaharchuk, Greg / Heit, Jeremy J.

2022

Abstract: To determine if a convolutional neural network (CNN) deep learning model can accurately segment acute ischemic changes on non-contrast CT compared to neuroradiologists. Non-contrast CT (NCCT) examinations from 232 acute ischemic stroke patients who were ... ...

Abstract	To determine if a convolutional neural network (CNN) deep learning model can accurately segment acute ischemic changes on non-contrast CT compared to neuroradiologists. Non-contrast CT (NCCT) examinations from 232 acute ischemic stroke patients who were enrolled in the DEFUSE 3 trial were included in this study. Three experienced neuroradiologists independently segmented hypodensity that reflected the ischemic core on each scan. The neuroradiologist with the most experience (expert A) served as the ground truth for deep learning model training. Two additional neuroradiologists (experts B and C) segmentations were used for data testing. The 232 studies were randomly split into training and test sets. The training set was further randomly divided into 5 folds with training and validation sets. A 3-dimensional CNN architecture was trained and optimized to predict the segmentations of expert A from NCCT. The performance of the model was assessed using a set of volume, overlap, and distance metrics using non-inferiority thresholds of 20%, 3ml, and 3mm. The optimized model trained on expert A was compared to test experts B and C. We used a one-sided Wilcoxon signed-rank test to test for the non-inferiority of the model-expert compared to the inter-expert agreement. The final model performance for the ischemic core segmentation task reached a performance of 0.46+-0.09 Surface Dice at Tolerance 5mm and 0.47+-0.13 Dice when trained on expert A. Compared to the two test neuroradiologists the model-expert agreement was non-inferior to the inter-expert agreement, p < 0.05. The CNN accurately delineates the hypodense ischemic core on NCCT in acute ischemic stroke patients with an accuracy comparable to neuroradiologists.
Schlagwörter	Electrical Engineering and Systems Science - Image and Video Processing ; Computer Science - Computer Vision and Pattern Recognition ; Computer Science - Machine Learning
Erscheinungsdatum	2022-11-24
Erscheinungsland	us
Dokumenttyp	Buch ; Online
Datenquelle	BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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Artikel ; Online: Evaluation of Sarcopenia in Older Patients Undergoing Head and Neck Cancer Surgery.

Orzell, Susannah / Verhaaren, Benjamin F J / Grewal, Rajan / Sklar, Michael / Irish, Jonathan C / Gilbert, Ralph / Brown, Dale / Gullane, Patrick / de Almeida, John R / Yu, Eugene / Su, Jie / Xu, Wei / Alibhai, Shabbir M H / Goldstein, David P

The Laryngoscope

2021 Band 132, Heft 2, Seite(n) 356–363

Abstract: Objectives/hypothesis: Sarcopenia is a hallmark of aging and its identification may help predict adverse postoperative events in patients undergoing head and neck surgery. The study objective was to assess the relationship between sarcopenia and ... ...

Abstract	Objectives/hypothesis: Sarcopenia is a hallmark of aging and its identification may help predict adverse postoperative events in patients undergoing head and neck surgery. The study objective was to assess the relationship between sarcopenia and postoperative complications and length of stay in patients undergoing major head and neck cancer surgery. Study design: Prospective cohort study. Methods: A prospective cohort study was performed of patients 50 years and older undergoing major head and neck surgery. Sarcopenia was defined as low muscle mass (determined by neck muscle cross-sectional imaging) with either low muscle strength (grip strength) or low muscle performance (timed walk test). Logistic regression was applied on binary outcomes, and linear regression was used for log-transformed length of hospital stay (LOS). Univariate and multivariate analyses were performed. Results: Of the 251 patients enrolled, pre-sarcopenia was present in 34.9% (n = 87) and sarcopenia in 15.6% (n = 39) of patients. Patients with sarcopenia were more likely to be older (P = .001), female (P = .001), have a lower body mass index (P = .001), and lower preoperative hemoglobin (P < .001). On univariate analysis, the presence and severity of sarcopenia was associated with the development of medical complications (P = .029), higher grade of complications (P = .032), LOS (P = .015), and overall survival (P = .001). On multivariate analysis, sarcopenia was associated with a longer LOS (β = 0.32 [95% CI: 0.19-0.45], P < .001) and worse overall survival (HR = 2.21 [95% CI: 1.01-4.23], P = .017). Conclusions: Sarcopenia may aid in the prediction of prolonged hospital stay and death in patients who are candidates for major head and neck surgery. Level of evidence: 3 Laryngoscope, 132:356-363, 2022.
Mesh-Begriff(e)	Aged ; Aged, 80 and over ; Female ; Head and Neck Neoplasms/complications ; Head and Neck Neoplasms/surgery ; Humans ; Length of Stay ; Male ; Middle Aged ; Postoperative Complications/epidemiology ; Prospective Studies ; Sarcopenia/complications ; Sarcopenia/diagnosis
Sprache	Englisch
Erscheinungsdatum	2021-08-12
Erscheinungsland	United States
Dokumenttyp	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	80180-x
ISSN	1531-4995 ; 0023-852X
ISSN (online)	1531-4995
ISSN	0023-852X
DOI	10.1002/lary.29782
Datenquelle	MEDical Literature Analysis and Retrieval System OnLINE

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Artikel ; Online: Reversal of endothelial dysfunction reduces white matter vulnerability in cerebral small vessel disease in rats.

Rajani, Rikesh M / Quick, Sophie / Ruigrok, Silvie R / Graham, Delyth / Harris, Sarah E / Verhaaren, Benjamin F J / Fornage, Myriam / Seshadri, Sudha / Atanur, Santosh S / Dominiczak, Anna F / Smith, Colin / Wardlaw, Joanna M / Williams, Anna

Science translational medicine

2018 Band 10, Heft 448

Abstract: Dementia is a major social and economic problem for our aging population. One of the most common of dementia in the elderly is cerebral small vessel disease (SVD). Magnetic resonance scans of SVD patients typically show white matter abnormalities, but we ...

Abstract	Dementia is a major social and economic problem for our aging population. One of the most common of dementia in the elderly is cerebral small vessel disease (SVD). Magnetic resonance scans of SVD patients typically show white matter abnormalities, but we do not understand the mechanistic pathological link between blood vessels and white matter myelin damage. Hypertension is suggested as the cause of sporadic SVD, but a recent alternative hypothesis invokes dysfunction of the blood-brain barrier as the primary cause. In a rat model of SVD, we show that endothelial cell (EC) dysfunction is the first change in development of the disease. Dysfunctional ECs secrete heat shock protein 90α, which blocks oligodendroglial differentiation, contributing to impaired myelination. Treatment with EC-stabilizing drugs reversed these EC and oligodendroglial pathologies in the rat model. EC and oligodendroglial dysfunction were also observed in humans with early, asymptomatic SVD pathology. We identified a loss-of-function mutation in ATPase11B, which caused the EC dysfunction in the rat SVD model, and a single-nucleotide polymorphism in ATPase11B that was associated with white matter abnormalities in humans with SVD. We show that EC dysfunction is a cause of SVD white matter vulnerability and provide a therapeutic strategy to treat and reverse SVD in the rat model, which may also be of relevance to human SVD.
Mesh-Begriff(e)	Adenosine Triphosphatases/genetics ; Animals ; Blood-Brain Barrier/pathology ; Blood-Brain Barrier/physiopathology ; Cell Proliferation ; Cerebral Small Vessel Diseases/pathology ; Cerebral Small Vessel Diseases/physiopathology ; Disease Models, Animal ; Endothelial Cells/pathology ; Endothelium, Vascular/pathology ; Endothelium, Vascular/physiopathology ; HSP90 Heat-Shock Proteins/metabolism ; Homozygote ; Humans ; Hypertension/pathology ; Hypertension/physiopathology ; Membrane Transport Proteins/genetics ; Middle Aged ; Oligodendrocyte Precursor Cells/metabolism ; Polymorphism, Single Nucleotide/genetics ; Rats ; White Matter/pathology ; White Matter/physiopathology
Chemische Substanzen	HSP90 Heat-Shock Proteins ; Membrane Transport Proteins ; ATP11B protein, human (EC 3.6.1.-) ; Adenosine Triphosphatases (EC 3.6.1.-)
Sprache	Englisch
Erscheinungsdatum	2018-07-03
Erscheinungsland	United States
Dokumenttyp	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2518854-9
ISSN	1946-6242 ; 1946-6234
ISSN (online)	1946-6242
ISSN	1946-6234
DOI	10.1126/scitranslmed.aam9507
Datenquelle	MEDical Literature Analysis and Retrieval System OnLINE

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Artikel ; Online: High blood pressure and cerebral white matter lesion progression in the general population.

Verhaaren, Benjamin F J / Vernooij, Meike W / de Boer, Renske / Hofman, Albert / Niessen, Wiro J / van der Lugt, Aad / Ikram, M Arfan

Hypertension (Dallas, Tex. : 1979)

2013 Band 61, Heft 6, Seite(n) 1354–1359

Abstract: High blood pressure is considered an important risk factor for cerebral white matter lesions (WMLs) in the aging population. In a longitudinal population-based study of 665 nondemented persons, we investigated the longitudinal relationship of systolic ... ...

Abstract	High blood pressure is considered an important risk factor for cerebral white matter lesions (WMLs) in the aging population. In a longitudinal population-based study of 665 nondemented persons, we investigated the longitudinal relationship of systolic blood pressure, diastolic blood pressure, and pulse pressure with annual progression of WMLs. Means of blood pressure were calculated over a 5-year period before longitudinal MRI scanning. WML progression was subsequently measured on 2 scans 3.5 years apart. We performed analyses with linear regression models and evaluated adjustments for age, sex, cardiovascular risk factors, and baseline WML volume. In addition, we evaluated whether treatment of hypertension is related to less WML progression. Both systolic and diastolic blood pressures were significantly associated with annual WML progression (regression coefficient [95% confidence interval], 0.08 [0.03; 0.14] mL/y and 0.09 [0.03; 0.15] mL/y per SD increase in systolic and diastolic blood pressure, respectively). Pulse pressure was also significantly associated with WML progression, but not independent from hypertension. After adjustment for baseline WML volume, only systolic blood pressure remained significantly associated: 0.05 (0.00; 0.09) mL/y per SD increase. People with uncontrolled untreated hypertension had significantly more WML progression than people with uncontrolled treated hypertension (difference [95% confidence interval], 0.12 [0.00; 0.23] mL/y). The present study further establishes high blood pressure to precede WMLs and implies that hypertension treatment could reduce WML progression in the general population.
Mesh-Begriff(e)	Aged ; Aged, 80 and over ; Blood Pressure/physiology ; Brain/pathology ; Disease Progression ; Female ; Follow-Up Studies ; Humans ; Hypertension/diagnosis ; Hypertension/epidemiology ; Hypertension/physiopathology ; Magnetic Resonance Imaging ; Male ; Middle Aged ; Netherlands/epidemiology ; Prevalence ; Retrospective Studies ; Severity of Illness Index
Sprache	Englisch
Erscheinungsdatum	2013-06
Erscheinungsland	United States
Dokumenttyp	Comparative Study ; Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	423736-5
ISSN	1524-4563 ; 0194-911X ; 0362-4323
ISSN (online)	1524-4563
ISSN	0194-911X ; 0362-4323
DOI	10.1161/HYPERTENSIONAHA.111.00430
Datenquelle	MEDical Literature Analysis and Retrieval System OnLINE

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Artikel ; Online: Markers of cerebral small vessel disease and severity of depression in the general population.

Direk, Nese / Perez, Heidi Saavedra / Akoudad, Saloua / Verhaaren, Benjamin F J / Niessen, Wiro J / Hofman, Albert / Vernooij, Meike W / Ikram, M Arfan / Tiemeier, Henning

Psychiatry research

2016 Band 253, Seite(n) 1–6

Abstract: The vascular depression hypothesis postulates that cerebral small vessel disease can cause or exacerbate depression in elderly persons. Numerous studies explored the association of imaging markers of cerebral small vessel disease including white matter ... ...

Abstract	The vascular depression hypothesis postulates that cerebral small vessel disease can cause or exacerbate depression in elderly persons. Numerous studies explored the association of imaging markers of cerebral small vessel disease including white matter lesions (WMLs) and lacunar infarcts with depressive symptoms or disorders. However, cerebral microbleeds have not been tested in depression. In the current study, we aimed to explore the association of WMLs, lacunar infarcts and cerebral microbleeds with depression continuum in a large population-based sample, the Rotterdam Study. Study population consisted of 3799 participants (aged 45 or over) free of dementia. WML volumes, lacunar infarcts and cerebral microbleeds were measured with brain magnetic resonance imaging. Depressive symptoms, depressive disorders and co-morbid anxiety disorders were assessed with validated questionnaires and clinical interview. WML volumes and lacunar infarcts were associated with depressive symptoms and disorders. Cerebral microbleeds, especially in deep or infratentorial brain regions, were related to depressive disorders only. Our results indicate that WMLs and lacunar infarcts might be non-specific vascular lesions seen in depressive symptoms and disorders. Association of cerebral microbleeds with more severe forms of depression may indicate impaired brain iron homeostasis or minor episodes of cerebrovascular extraversion, which may play a role in depression etiology.
Sprache	Englisch
Erscheinungsdatum	2016-07-30
Erscheinungsland	Ireland
Dokumenttyp	Journal Article
ZDB-ID	445361-x
ISSN	1872-7123 ; 1872-7506 ; 0925-4927 ; 0165-1781
ISSN (online)	1872-7123 ; 1872-7506
ISSN	0925-4927 ; 0165-1781
DOI	10.1016/j.pscychresns.2016.05.002
Datenquelle	MEDical Literature Analysis and Retrieval System OnLINE

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