| 1. |
- De Luca, Alberto, et al.
(författare)
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On the generalizability of diffusion MRI signal representations across acquisition parameters, sequences and tissue types : Chronicles of the MEMENTO challenge
- 2021
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Ingår i: NeuroImage. - : Elsevier BV. - 1053-8119 .- 1095-9572. ; 240
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Tidskriftsartikel (refereegranskat)abstract
- Diffusion MRI (dMRI) has become an invaluable tool to assess the microstructural organization of brain tissue. Depending on the specific acquisition settings, the dMRI signal encodes specific properties of the underlying diffusion process. In the last two decades, several signal representations have been proposed to fit the dMRI signal and decode such properties. Most methods, however, are tested and developed on a limited amount of data, and their applicability to other acquisition schemes remains unknown. With this work, we aimed to shed light on the generalizability of existing dMRI signal representations to different diffusion encoding parameters and brain tissue types. To this end, we organized a community challenge - named MEMENTO, making available the same datasets for fair comparisons across algorithms and techniques. We considered two state-of-the-art diffusion datasets, including single-diffusion-encoding (SDE) spin-echo data from a human brain with over 3820 unique diffusion weightings (the MASSIVE dataset), and double (oscillating) diffusion encoding data (DDE/DODE) of a mouse brain including over 2520 unique data points. A subset of the data sampled in 5 different voxels was openly distributed, and the challenge participants were asked to predict the remaining part of the data. After one year, eight participant teams submitted a total of 80 signal fits. For each submission, we evaluated the mean squared error, the variance of the prediction error and the Bayesian information criteria. The received submissions predicted either multi-shell SDE data (37%) or DODE data (22%), followed by cartesian SDE data (19%) and DDE (18%). Most submissions predicted the signals measured with SDE remarkably well, with the exception of low and very strong diffusion weightings. The prediction of DDE and DODE data seemed more challenging, likely because none of the submissions explicitly accounted for diffusion time and frequency. Next to the choice of the model, decisions on fit procedure and hyperparameters play a major role in the prediction performance, highlighting the importance of optimizing and reporting such choices. This work is a community effort to highlight strength and limitations of the field at representing dMRI acquired with trending encoding schemes, gaining insights into how different models generalize to different tissue types and fiber configurations over a large range of diffusion encodings.
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| 2. |
- Coenen, Mirthe, et al.
(författare)
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Spatial distributions of white matter hyperintensities on brain MRI: A pooled analysis of individual participant data from 11 memory clinic cohorts
- 2023
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Ingår i: NeuroImage. Clinical. - 2213-1582. ; 40
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Tidskriftsartikel (refereegranskat)abstract
- INTRODUCTION: The spatial distribution of white matter hyperintensities (WMH) on MRI is often considered in the diagnostic evaluation of patients with cognitive problems. In some patients, clinicians may classify WMH patterns as "unusual", but this is largely based on expert opinion, because detailed quantitative information about WMH distribution frequencies in a memory clinic setting is lacking. Here we report voxel wise 3D WMH distribution frequencies in a large multicenter dataset and also aimed to identify individuals with unusual WMH patterns. METHODS: Individual participant data (N = 3525, including 777 participants with subjective cognitive decline, 1389 participants with mild cognitive impairment and 1359 patients with dementia) from eleven memory clinic cohorts, recruited through the Meta VCI Map Consortium, were used. WMH segmentations were provided by participating centers or performed in Utrecht and registered to the Montreal Neurological Institute (MNI)-152 brain template for spatial normalization. To determine WMH distribution frequencies, we calculated WMH probability maps at voxel level. To identify individuals with unusual WMH patterns, region-of-interest (ROI) based WMH probability maps, rule-based scores, and a machine learning method (Local Outlier Factor (LOF)), were implemented. RESULTS: WMH occurred in 82% of voxels from the white matter template with large variation between subjects. Only a small proportion of the white matter (1.7%), mainly in the periventricular areas, was affected by WMH in at least 20% of participants. A large portion of the total white matter was affected infrequently. Nevertheless, 93.8% of individual participants had lesions in voxels that were affected in less than 2% of the population, mainly located in subcortical areas. Only the machine learning method effectively identified individuals with unusual patterns, in particular subjects with asymmetric WMH distribution or with WMH at relatively rarely affected locations despite common locations not being affected. DISCUSSION: Aggregating data from several memory clinic cohorts, we provide a detailed 3D map of WMH lesion distribution frequencies, that informs on common as well as rare localizations. The use of data-driven analysis with LOF can be used to identify unusual patterns, which might serve as an alert that rare causes of WMH should be considered.
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| 3. |
- De Guio, François, et al.
(författare)
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Reproducibility and variability of quantitative magnetic resonance imaging markers in cerebral small vessel disease
- 2016
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Ingår i: Journal of Cerebral Blood Flow and Metabolism. - 0271-678X. ; 36:8, s. 1319-1337
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Forskningsöversikt (refereegranskat)abstract
- Brain imaging is essential for the diagnosis and characterization of cerebral small vessel disease. Several magnetic resonance imaging markers have therefore emerged, providing new information on the diagnosis, progression, and mechanisms of small vessel disease. Yet, the reproducibility of these small vessel disease markers has received little attention despite being widely used in cross-sectional and longitudinal studies. This review focuses on the main small vessel disease-related markers on magnetic resonance imaging including: white matter hyperintensities, lacunes, dilated perivascular spaces, microbleeds, and brain volume. The aim is to summarize, for each marker, what is currently known about: (1) its reproducibility in studies with a scan-rescan procedure either in single or multicenter settings; (2) the acquisition-related sources of variability; and, (3) the techniques used to minimize this variability. Based on the results, we discuss technical and other challenges that need to be overcome in order for these markers to be reliably used as outcome measures in future clinical trials. We also highlight the key points that need to be considered when designing multicenter magnetic resonance imaging studies of small vessel disease.
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| 4. |
- de Wilde, Arno, et al.
(författare)
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Assessment of the appropriate use criteria for amyloid PET in an unselected memory clinic cohort : The ABIDE project
- 2019
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Ingår i: Alzheimer's and Dementia. - : Wiley. - 1552-5260.
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Tidskriftsartikel (refereegranskat)abstract
- Introduction: The objective of this study was to assess the usefulness of the appropriate use criteria (AUC) for amyloid imaging in an unselected cohort. Methods: We calculated sensitivity and specificity of appropriate use (increased confidence and management change), as defined by Amyloid Imaging Taskforce in the AUC, and other clinical utility outcomes. Furthermore, we compared differences in post–positron emission tomography diagnosis and management change between “AUC-consistent” and “AUC-inconsistent” patients. Results: Almost half (250/507) of patients were AUC-consistent. In both AUC-consistent and AUC-inconsistent patients, post–positron emission tomography diagnosis (28%–21%) and management (32%–17%) change was substantial. The Amyloid Imaging Taskforce's definition of appropriate use occurred in 55/507 (13%) patients, detected by the AUC with a sensitivity of 93%, and a specificity of 56%. Diagnostic changes occurred independently of AUC status (sensitivity: 57%, specificity: 53%). Discussion: The current AUC are not sufficiently able to discriminate between patients who will benefit from amyloid positron emission tomography and those who will not.
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| 5. |
- Dichgans, Martin, et al.
(författare)
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METACOHORTS for the study of vascular disease and its contribution to cognitive decline and neurodegeneration : An initiative of the Joint Programme for Neurodegenerative Disease Research
- 2016
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Ingår i: Alzheimer's and Dementia. - : Wiley. - 1552-5260 .- 1552-5279. ; 12:12, s. 1235-1249
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Tidskriftsartikel (refereegranskat)abstract
- Dementia is a global problem and major target for health care providers. Although up to 45% of cases are primarily or partly due to cerebrovascular disease, little is known of these mechanisms or treatments because most dementia research still focuses on pure Alzheimer's disease. An improved understanding of the vascular contributions to neurodegeneration and dementia, particularly by small vessel disease, is hampered by imprecise data, including the incidence and prevalence of symptomatic and clinically “silent” cerebrovascular disease, long-term outcomes (cognitive, stroke, or functional), and risk factors. New large collaborative studies with long follow-up are expensive and time consuming, yet substantial data to advance the field are available. In an initiative funded by the Joint Programme for Neurodegenerative Disease Research, 55 international experts surveyed and assessed available data, starting with European cohorts, to promote data sharing to advance understanding of how vascular disease affects brain structure and function, optimize methods for cerebrovascular disease in neurodegeneration research, and focus future research on gaps in knowledge. Here, we summarize the results and recommendations from this initiative. We identified data from over 90 studies, including over 660,000 participants, many being additional to neurodegeneration data initiatives. The enthusiastic response means that cohorts from North America, Australasia, and the Asia Pacific Region are included, creating a truly global, collaborative, data sharing platform, linked to major national dementia initiatives. Furthermore, the revised World Health Organization International Classification of Diseases version 11 should facilitate recognition of vascular-related brain damage by creating one category for all cerebrovascular disease presentations and thus accelerate identification of targets for dementia prevention.
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| 6. |
- Dichgans, Martin, et al.
(författare)
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Third European stroke science workshop
- 2016
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Ingår i: ; , s. 178-186
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Konferensbidrag (refereegranskat)abstract
- Lake Eibsee, Garmisch-Partenkirchen, November 19 to 21, 2015: The European Stroke Organization convened >120 stroke experts from 27 countries to discuss latest results and hot topics in clinical, translational, and basic stroke research. Since its inception in 2011, the European Stroke Science Workshop has become a cornerstone of European Stroke Organization's academic activities and major highlight for researchers in the field. Participants include stroke researchers at all career stages who convene for plenary lectures and discussions, thus facilitating crosstalk among researchers from different fields. As in previous years, the workshop was organized into 7 scientific sessions each focusing on a major research topic. All sessions started with a keynote lecture that provided an overview on current developments and set the scene for the following presentations. The latter were short focused talks on a timely topic and included the most recent findings, including unpublished data. A new element at this year's meeting was a hot topic session in which speakers had to present a provocative concept or update sharply within 5 minutes. In the following, we summarize the key contents of the meeting. The program is provided in the online-only Data Supplement.
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| 7. |
- Exalto, Lieza G, et al.
(författare)
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Midlife risk score for the prediction of dementia four decades later
- 2013
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Ingår i: Alzheimer's & Dementia. - : Wiley. - 1552-5260 .- 1552-5279. ; 10:5, s. 562-570
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Tidskriftsartikel (refereegranskat)abstract
- ObjectiveThe objective of this study was to obtain external validation of the only available midlife dementia risk score cardiovascular risk factors , aging and dementia study (CAIDE) constituting age, education, hypertension, obesity, and hyperlipidemia in a larger, more diverse population. Our second aim was to improve the CAIDE risk score by additional midlife risk factors.MethodsThis retrospective cohort study was conducted in an integrated health care delivery system. A total of 9480 Kaiser Permanente members who participated in a health survey study (age range, 40–55 years) from 1964 to 1973 were included in this study. Dementia diagnoses from primary care and medical specialist visits were collected from January 1, 1994 to January 16, 2006, using International Classification of Diseases 9 codes 290.0, 290.1 for “possible dementia,” and 331.0 and 290.4 for “specialist confirmed dementia.” Risk model prediction and validation were examined with the C statistic, net reclassification improvement, and integrated discrimination improvement. Dementia risk per sum score was calculated with Kaplan-Meier estimates.ResultsA total of 2767 participants (25%) were diagnosed with any type of dementia, of which 1011 diagnoses (10.7%) were specialist-confirmed diagnoses. Average time between midlife examination and end of follow-up was 36.1 years. The CAIDE risk score replicated well with a C statistic of 0.75, quite similar to the original CAIDE C statistic of 0.78. The CAIDE score also predicted well within different race strata. Other midlife risk factors (central obesity, depressed mood, diabetes mellitus, head trauma, lung function, and smoking) did not improve predictability. The risk score allowed stratification of participants into those with 40-year low (9%) and high (29%) dementia risk.ConclusionsA combination of modifiable vascular risk factors in midlife is highly predictive of the likelihood of dementia decades later. Possible dementia prevention strategies should point to a life course perspective on maintaining vascular health.
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| 8. |
- Jansen, Iris E, et al.
(författare)
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Genome-wide meta-analysis for Alzheimer's disease cerebrospinal fluid biomarkers.
- 2022
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Ingår i: Acta neuropathologica. - : Springer Science and Business Media LLC. - 1432-0533 .- 0001-6322. ; 144:5, s. 821-842
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Tidskriftsartikel (refereegranskat)abstract
- Amyloid-beta 42 (Aβ42) and phosphorylated tau (pTau) levels in cerebrospinal fluid (CSF) reflect core features of the pathogenesis of Alzheimer's disease (AD) more directly than clinical diagnosis. Initiated by the European Alzheimer & Dementia Biobank (EADB), the largest collaborative effort on genetics underlying CSF biomarkers was established, including 31 cohorts with a total of 13,116 individuals (discovery n = 8074; replication n = 5042 individuals). Besides the APOE locus, novel associations with two other well-established AD risk loci were observed; CR1 was shown a locus for Aβ42 and BIN1 for pTau. GMNC and C16orf95 were further identified as loci for pTau, of which the latter is novel. Clustering methods exploring the influence of all known AD risk loci on the CSF protein levels, revealed 4 biological categories suggesting multiple Aβ42 and pTau related biological pathways involved in the etiology of AD. In functional follow-up analyses, GMNC and C16orf95 both associated with lateral ventricular volume, implying an overlap in genetic etiology for tau levels and brain ventricular volume.
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| 9. |
- Jansen, Marielle J A, et al.
(författare)
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Patient-specific fine-tuning of CNNs for follow-up lesion quantification
- 2020
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Ingår i: Journal of Medical Imaging.
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Tidskriftsartikel (refereegranskat)abstract
- Convolutional neural network (CNN) methods have been proposed to quantify lesions in medical imaging. Commonly more than one imaging examination is available for a patient, but the serial information in these images often remains unused. CNNbased methods have the potential to extract valuable information from previously acquired imaging to better quantify current imaging of the same patient. A pre-trained CNN can be updated with a patient’s previously acquired imaging: patient-specific fine-tuning. In this work, we studied the improvement in performance of lesion quantification methods on MR images after fine-tuning compared to a base CNN. We applied the method to two different approaches: the detection of liver metastases and the segmentation of brain white matter hyperintensities (WMH). The patient-specific fine-tuned CNN has a better performance than the base CNN. For the liver metastases, the median true positive rate increases from 0.67 to 0.85. For the WMH segmentation, the mean Dice similarity coefficient increases from 0.82 to 0.87. In this study we showed that patient-specific fine-tuning has potential to improve the lesion quantification performance of general CNNs by exploiting the patient’s previously acquired imaging
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| 10. |
- Jansen, Marielle J. A., et al.
(författare)
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Patient-specific fine-tuning of convolutional neural networks for follow-up lesion quantification
- 2020
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Ingår i: Journal of Medical Imaging. - : SPIE-SOC PHOTO-OPTICAL INSTRUMENTATION ENGINEERS. - 2329-4302 .- 2329-4310. ; 7:6
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Tidskriftsartikel (refereegranskat)abstract
- Purpose: Convolutional neural network (CNN) methods have been proposed to quantify lesions in medical imaging. Commonly, more than one imaging examination is available for a patient, but the serial information in these images often remains unused. CNN-based methods have the potential to extract valuable information from previously acquired imaging to better quantify lesions on current imaging of the same patient.Approach: A pretrained CNN can be updated with a patient's previously acquired imaging: patient-specific fine-tuning (FT). In this work, we studied the improvement in performance of lesion quantification methods on magnetic resonance images after FT compared to a pretrained base CNN. We applied the method to two different approaches: the detection of liver metastases and the segmentation of brain white matter hyperintensities (WMH).Results: The patient-specific fine-tuned CNN has a better performance than the base CNN. For the liver metastases, the median true positive rate increases from 0.67 to 0.85. For the WMH segmentation, the mean Dice similarity coefficient increases from 0.82 to 0.87.Conclusions: We showed that patient-specific FT has the potential to improve the lesion quantification performance of general CNNs by exploiting a patient's previously acquired imaging.
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