Author name cluster

Aad van der Lugt

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15 papers

1 author row

YNICL Journal 2023 Journal Article

Cerebral blood flow quantification with multi-delay arterial spin labeling in ischemic stroke and the association with early neurological outcome

Sven P.R. Luijten
Daniel Bos
Pieter-Jan van Doormaal
Mayank Goyal
Rick M. Dijkhuizen
Diederik W.J. Dippel
Bob Roozenbeek
Aad van der Lugt

Restoring blood flow to brain tissue at risk of infarction is essential for tissue survival and clinical outcome. We used cerebral blood flow (CBF) quantified with multiple post-labeling delay (PLD) pseudocontinuous arterial spin labeling (ASL) MRI after ischemic stroke and assessed the association between CBF and early neurological outcome. We acquired ASL with 7 PLDs at 3.0 T in large vessel occlusion stroke patients at 24 h. We quantified CBF relative to the contralateral hemisphere (rCBF) and defined hyperperfusion as a ≥30% increase and hypoperfusion as a ≥40% decrease in rCBF. We included 44 patients (median age: 70 years, median NIHSS: 13, 40 treated with endovascular thrombectomy) of whom 37 were recanalized. Hyperperfusion in ischemic core occurred in recanalized but not in non-recanalized patients (65.8% vs 0%, p = 0.006). Hypoperfusion occurred only in the latter group (0% vs 85.7%, p < 0.001). In recanalized patients, hyperperfusion was also seen in salvaged penumbra (38.9%). Higher rCBF in ischemic core (aβ, -2.75 [95% CI: -4.11 to -1.40]) and salvaged penumbra (aβ, -5.62 [95% CI: -9.57 to -1.68]) was associated with lower NIHSS scores at 24 h. In conclusion, hyperperfusion frequently occurs in infarcted and salvaged brain tissue following successful recanalization and early neurological outcome is positively associated with the level of reperfusion.

YNICL Journal 2021 Journal Article

Cross-cohort generalizability of deep and conventional machine learning for MRI-based diagnosis and prediction of Alzheimer’s disease

Esther E. Bron
Stefan Klein
Janne M. Papma
Lize C. Jiskoot
Vikram Venkatraghavan
Jara Linders
Pauline Aalten
Peter Paul De Deyn

This work validates the generalizability of MRI-based classification of Alzheimer's disease (AD) patients and controls (CN) to an external data set and to the task of prediction of conversion to AD in individuals with mild cognitive impairment (MCI). We used a conventional support vector machine (SVM) and a deep convolutional neural network (CNN) approach based on structural MRI scans that underwent either minimal pre-processing or more extensive pre-processing into modulated gray matter (GM) maps. Classifiers were optimized and evaluated using cross-validation in the Alzheimer's Disease Neuroimaging Initiative (ADNI; 334 AD, 520 CN). Trained classifiers were subsequently applied to predict conversion to AD in ADNI MCI patients (231 converters, 628 non-converters) and in the independent Health-RI Parelsnoer Neurodegenerative Diseases Biobank data set. From this multi-center study representing a tertiary memory clinic population, we included 199 AD patients, 139 participants with subjective cognitive decline, 48 MCI patients converting to dementia, and 91 MCI patients who did not convert to dementia. AD-CN classification based on modulated GM maps resulted in a similar area-under-the-curve (AUC) for SVM (0.940; 95%CI: 0.924–0.955) and CNN (0.933; 95%CI: 0.918–0.948). Application to conversion prediction in MCI yielded significantly higher performance for SVM (AUC = 0.756; 95%CI: 0.720-0.788) than for CNN (AUC = 0.742; 95%CI: 0.709-0.776) (p<0.01 for McNemar's test). In external validation, performance was slightly decreased. For AD-CN, it again gave similar AUCs for SVM (0.896; 95%CI: 0.855–0.932) and CNN (0.876; 95%CI: 0.836–0.913). For prediction in MCI, performances decreased for both SVM (AUC = 0.665; 95%CI: 0.576-0.760) and CNN (AUC = 0.702; 95%CI: 0.624-0.786). Both with SVM and CNN, classification based on modulated GM maps significantly outperformed classification based on minimally processed images (p=0.01). Deep and conventional classifiers performed equally well for AD classification and their performance decreased only slightly when applied to the external cohort. We expect that this work on external validation contributes towards translation of machine learning to clinical practice.

YNIMG Journal 2020 Journal Article

Changes in the intracranial volume from early adulthood to the sixth decade of life: A longitudinal study

Yaron Caspi
Rachel M. Brouwer
Hugo G. Schnack
Marieke E. van de Nieuwenhuijzen
Wiepke Cahn
René S. Kahn
Wiro J. Niessen
Aad van der Lugt

Normal brain-aging occurs at all structural levels. Excessive pathophysiological changes in the brain, beyond the normal one, are implicated in the etiology of brain disorders such as severe forms of the schizophrenia spectrum and dementia. To account for brain-aging in health and disease, it is critical to study the age-dependent trajectories of brain biomarkers at various levels and among different age groups. The intracranial volume (ICV) is a key biological marker, and changes in the ICV during the lifespan can teach us about the biology of development, aging, and gene X environment interactions. However, whether ICV changes with age in adulthood is not resolved. Applying a semi-automatic in-house-built algorithm for ICV extraction on T1w MR brain scans in the Dutch longitudinal cohort (GROUP), we measured ICV changes. Individuals between the ages of 16 and 55 years were scanned up to three consecutive times with 3.32±0.32 years between consecutive scans (N = 482, 359, 302). Using the extracted ICVs, we calculated ICV longitudinal aging-trajectories based on three analysis methods; direct calculation of ICV differences between the first and the last scan, fitting all ICV measurements of individuals to a straight line, and applying a global linear mixed model fitting. We report statistically significant increase in the ICV in adulthood until the fourth decade of life (average change +0.03%/y, or about 0.5 ml/y, at age 20), and decrease in the ICV afterward (-0.09%/y, or about -1.2 ml/y, at age 55). To account for previous cross-sectional reports of ICV changes, we analyzed the same data using a cross-sectional approach. Our cross-sectional analysis detected ICV changes consistent with the previously reported cross-sectional effect. However, the reported amount of cross-sectional changes within this age range was significantly larger than the longitudinal changes. We attribute the cross-sectional results to a generational effect. In conclusion, the human intracranial volume does not stay constant during adulthood but instead shows a small increase during young adulthood and a decrease thereafter from the fourth decade of life. The age-related changes in the longitudinalmeasure are smaller than those reported using cross-sectional approaches and unlikely to affect structural brain imaging studies correcting for intracranial volume considerably. As to the possible mechanisms involved, this awaits further study, although thickening of the meninges and skull bones have been proposed, as well as a smaller amount of brain fluids addition above the overall loss of brain tissue.

YNIMG Journal 2019 Journal Article

Patterns of functional connectivity in an aging population: The Rotterdam Study

Hazel I. Zonneveld
Raimon HR. Pruim
Daniel Bos
Henri A. Vrooman
Ryan L. Muetzel
Albert Hofman
Serge ARB. Rombouts
Aad van der Lugt

Structural brain markers are studied extensively in the field of neurodegeneration, but are thought to occur rather late in the process. Functional measures such as functional connectivity are gaining interest as potentially more subtle markers of neurodegeneration. However, brain structure and function are also affected by ‘normal’ brain ageing. More information is needed on how functional connectivity relates to aging, particularly in the absence of overt neurodegenerative disease. We investigated the association of age with resting-state functional connectivity in 2878 non-demented persons between 50 and 95 years of age (54. 1% women) from the population-based Rotterdam Study. We obtained nine well-known resting state networks using data-driven methodology. Within the anterior default mode network, ventral attention network, and sensorimotor network, functional connectivity was significantly lower with older age. In contrast, functional connectivity was higher with older age within the visual network. Between resting state networks, we found patterns of both increases and decreases in connectivity in approximate equal proportions. Our results reinforce the notion that the aging brain undergoes a reorganization process, and serves as a solid basis for exploring functional connectivity as a preclinical marker of neurodegenerative disease.

YNICL Journal 2018 Journal Article

Automatic normative quantification of brain tissue volume to support the diagnosis of dementia: A clinical evaluation of diagnostic accuracy

Meike W. Vernooij
Bas Jasperse
Rebecca Steketee
Marcel Koek
Henri Vrooman
M. Arfan Ikram
Janne Papma
Aad van der Lugt

Objectives: To assesses whether automated brain image analysis with quantification of structural brain changes improves diagnostic accuracy in a memory clinic setting. Methods: = 4915, aged >45 years), and were expressed as age- and sex-specific percentiles. Experienced radiologists determined the most likely imaging-based diagnosis based on structural brain MRI using three strategies (visual assessment of MRI only, quantitative normative information only, or a combination of both). Diagnostic accuracy of each strategy was calculated with the clinical diagnosis as the reference standard. Results: Providing radiologists with only quantitative data decreased diagnostic accuracy both for AD and FTD compared to conventional visual rating. The combination of quantitative with visual information, however, led to better diagnostic accuracy compared to only visual ratings for AD. This was not the case for FTD. Conclusion: Quantitative assessment of structural brain MRI combined with a reference standard in addition to standard visual assessment may improve diagnostic accuracy in a memory clinic setting.

YNIMG Journal 2018 Journal Article

Subregional volumes of the hippocampus in relation to cognitive function and risk of dementia

Tavia E. Evans
Hieab H.H. Adams
Silvan Licher
Frank J. Wolters
Aad van der Lugt
M. Kamran Ikram
Michael J. O'Sullivan
Meike W. Vernooij

Background Total hippocampal volume has been consistently linked to cognitive function and dementia. Yet, given its complex and parcellated internal structure, the role of subregions of the hippocampus in cognition and risk of dementia remains relatively underexplored. We studied subregions of the hippocampus in a large population-based cohort to further understand their role in cognitive impairment and dementia risk. Methods We studied 5035 dementia- and stroke-free persons from the Rotterdam Study, aged over 45 years. All participants underwent magnetic resonance imaging (1. 5 T) between 2005 and 2015. Automatic segmentation of the hippocampus and 12 of its subregions was performed using the FreeSurfer software (version 6. 0). A cognitive test battery was performed, and participants were followed up for the development of dementia until 2015. Associations of hippocampal subregion volumes with cognition and incident dementia were examined using linear and Cox regression models, respectively. All analyses were adjusted for age, sex, education, and total hippocampal volume. Results Mean age was 64. 3 years (SD 10. 6) with 56% women. Smaller volumes of the hippocampal fimbria, presubiculum and subiculum showed the strongest associations with poor performance on several cognitive domains, including executive function but not memory. During a mean follow-up of 5. 5 years, 76 persons developed dementia. Smaller subiculum volume was associated with risk of dementia adjusted for total volume (hazard ratio per SD decrease in volume: 1. 75, 95% confidence interval 1. 35; 2. 26). Conclusions In a community-dwelling non-demented population, we describe patterns of association between hippocampal subregions with cognition and risk of dementia. Specifically, the subiculum was associated with both poorer cognition and higher risk of dementia.

YNIMG Journal 2017 Journal Article

Improving data availability for brain image biobanking in healthy subjects: Practice-based suggestions from an international multidisciplinary working group

Susan D. Shenkin
Cyril Pernet
Thomas E. Nichols
Jean-Baptiste Poline
Paul M. Matthews
Aad van der Lugt
Clare Mackay
Linda Lanyon

Brain imaging is now ubiquitous in clinical practice and research. The case for bringing together large amounts of image data from well-characterised healthy subjects and those with a range of common brain diseases across the life course is now compelling. This report follows a meeting of international experts from multiple disciplines, all interested in brain image biobanking. The meeting included neuroimaging experts (clinical and non-clinical), computer scientists, epidemiologists, clinicians, ethicists, and lawyers involved in creating brain image banks. The meeting followed a structured format to discuss current and emerging brain image banks; applications such as atlases; conceptual and statistical problems (e. g. defining ‘normality’); legal, ethical and technological issues (e. g. consents, potential for data linkage, data security, harmonisation, data storage and enabling of research data sharing). We summarise the lessons learned from the experiences of a wide range of individual image banks, and provide practical recommendations to enhance creation, use and reuse of neuroimaging data. Our aim is to maximise the benefit of the image data, provided voluntarily by research participants and funded by many organisations, for human health. Our ultimate vision is of a federated network of brain image biobanks accessible for large studies of brain structure and function.

YNICL Journal 2016 Journal Article

Structural and functional brain abnormalities place phenocopy frontotemporal dementia (FTD) in the FTD spectrum

Rebecca M.E. Steketee
Rozanna Meijboom
Esther E. Bron
Robert Jan Osse
Inge de Koning
Lize C. Jiskoot
Stefan Klein
Frank Jan de Jong

PURPOSE: 'Phenocopy' frontotemporal dementia (phFTD) patients may clinically mimic the behavioral variant of FTD (bvFTD), but do not show functional decline or abnormalities upon visual inspection of routine neuroimaging. We aimed to identify abnormalities in gray matter (GM) volume and perfusion in phFTD and to assess whether phFTD belongs to the FTD spectrum. We compared phFTD patients with both healthy controls and bvFTD patients. MATERIALS & METHODS: Seven phFTD and 11 bvFTD patients, and 20 age-matched controls underwent structural T1-weighted magnetic resonance imaging (MRI) and 3D pseudo-continuous arterial spin labeling (pCASL) at 3T. Normalized GM (nGM) volumes and perfusion, corrected for partial volume effects, were quantified regionally as well as in the entire supratentorial cortex, and compared between groups taking into account potential confounding effects of gender and scanner. RESULTS: PhFTD patients showed cortical atrophy, most prominently in the right temporal lobe. Apart from this regional atrophy, GM volume was generally not different from either controls or from bvFTD. BvFTD however showed extensive frontotemporal atrophy. Perfusion was increased in the left prefrontal cortex compared to bvFTD and to a lesser extent to controls. CONCLUSION: PhFTD and bvFTD show overlapping cortical structural abnormalities indicating a continuum of changes especially in the frontotemporal regions. Together with functional changes suggestive of a compensatory response to incipient pathology in the left prefrontal regions, these findings are the first to support a possible neuropathological etiology of phFTD and suggest that phFTD may be a neurodegenerative disease on the FTD spectrum.

YNIMG Journal 2015 Journal Article

White matter integrity and cognitive performance in school-age children: A population-based neuroimaging study

Ryan L. Muetzel
Sabine E. Mous
Jan van der Ende
Laura M.E. Blanken
Aad van der Lugt
Vincent W.V. Jaddoe
Frank C. Verhulst
Henning Tiemeier

Child and adolescent brain development are typically accompanied by marked improvements in a wide range of cognitive abilities. However, limited information is available surrounding the role of white matter in shaping cognitive abilities in children. The current study examined associations between white matter microstructure and cognitive performance in a large sample (n=778) of 6- to 10-year-old children. Results show white matter microstructure is related to non-verbal intelligence and to visuospatial ability, independent of age. Specificity was demonstrated, as white matter associations with visuospatial ability were independent of general intellectual ability. Associations between white matter integrity and cognition were similar in boys and girls. In summary, results demonstrate white matter structure–function associations are present in children, independent of age and broader cognitive abilities. The presence of such associations in the general population is informative for studies examining child psychopathology

YNICL Journal 2013 Journal Article

The influence of cerebral small vessel disease on default mode network deactivation in mild cognitive impairment

Janne M. Papma
Tom den Heijer
Inge de Koning
Francesco U. Mattace-Raso
Aad van der Lugt
Fedde van der Lijn
John C. van Swieten
Peter J. Koudstaal

Introduction Cerebral small vessel disease (CSVD) is thought to contribute to cognitive dysfunction in patients with mild cognitive impairment (MCI). The underlying mechanisms, and more specifically, the effects of CSVD on brain functioning in MCI are incompletely understood. The objective of the present study was to examine the effects of CSVD on brain functioning, activation and deactivation, in patients with MCI using task-related functional MRI (fMRI). Methods We included 16 MCI patients with CSVD, 26 MCI patients without CSVD and 25 controls. All participants underwent a physical and neurological examination, neuropsychological testing, structural MRI, and fMRI during a graded working memory paradigm. Results MCI patients with and without CSVD had a similar neuropsychological profile and task performance during fMRI, but differed with respect to underlying (de)activation patterns. MCI patients with CSVD showed impaired deactivation in the precuneus/posterior cingulate cortex, a region known to be involved in the default mode network. In MCI patients without CSVD, brain activation depended on working memory load, as they showed relative ‘hyperactivation’ during vigilance, and ‘hypoactivation’ at a high working memory load condition in working memory related brain regions. Conclusions We present evidence that the potential underlying mechanism of CSVD affecting cognition in MCI is through network interference. The observed differences in brain activation and deactivation between MCI patients with and without CSVD, who had a similar ‘clinical phenotype’, support the view that, in patients with MCI, different types of pathology can contribute to cognitive impairment through different pathways.

YNIMG Journal 2012 Journal Article

Automated measurement of local white matter lesion volume

Fedde van der Lijn
Benjamin F.J. Verhaaren
M. Arfan Ikram
Stefan Klein
Marleen de Bruijne
Henri A. Vrooman
Meike W. Vernooij
Alexander Hammers

It has been hypothesized that white matter lesions at different locations may have different etiology and clinical consequences. Several approaches for the quantification of local white matter lesion load have been proposed in the literature, most of which rely on a distinction between lesions in a periventricular region close to the ventricles and a subcortical zone further away. In this work we present a novel automated method for local white matter lesion volume quantification in magnetic resonance images. The method segments and measures the white matter lesion volume in 43 regions defined by orientation and distance to the ventricles, which allows a more spatially detailed study of lesion load. The potential of the method was demonstrated by analyzing the effect of blood pressure on the regional white matter lesion volume in 490 elderly subjects taken from a longitudinal population study. The method was also compared to two commonly used techniques to assess the periventricular and subcortical lesion load. The main finding was that high blood pressure was primarily associated with lesion load in the vascular watershed area that forms the border between the periventricular and subcortical regions. It explains the associations found for both the periventricular and subcortical load computed for the same data, and that were reported in the literature. But the proposed method can localize the region of association with greater precision than techniques that distinguish between periventricular and subcortical lesions only.

YNIMG Journal 2011 Journal Article

Remedial action and feedback processing in a time-estimation task: Evidence for a role of the rostral cingulate zone in behavioral adjustments without learning

Frederik M. van der Veen
Christian H. Röder
Gabry W. Mies
Aad van der Lugt
Marion Smits

The present study examined the role of the rostral cingulate zone (RCZ) in feedback processing, and especially focused on effects of modality of the feedback stimulus and remedial action. Participants performed a time-estimation task in which they had to estimate a 1-second interval. After the estimation participants received verbal (correct/false) or facial (fearful face/happy face) feedback. Percentage of positive and negative feedback was kept at 50% by dynamically adjusting the interval in which estimations were labeled correct. Contrary to predictions of the reinforcement learning theory, which predicts more RCZ activation when the outcome of behavior is worse than expected, we found that the RCZ was more active after positive feedback than after negative feedback, independent of the modality of the feedback stimulus. More in line with the suggested role of the RCZ in reinforcement learning was the finding that the RCZ was more active after negative feedback that was followed by a correct adjustment as compared to negative feedback followed by an incorrect adjustment. Both findings can be explained in terms of the RCZ being involved in facilitating remedial action as opposed to the suggested signaling function (outcome is worse than expected) proposed by the reinforcement learning theory.

YNIMG Journal 2011 Journal Article

Statistical analysis of minimum cost path based structural brain connectivity

Renske de Boer
Michiel Schaap
Fedde van der Lijn
Henri A. Vrooman
Marius de Groot
Aad van der Lugt
M. Arfan Ikram
Meike W. Vernooij

Diffusion MRI can be used to study the structural connectivity within the brain. Brain connectivity is often represented by a binary network whose topology can be studied using graph theory. We present a framework for the construction of weighted structural brain networks, containing information about connectivity, which can be effectively analyzed using statistical methods. Network nodes are defined by segmentation of subcortical structures and by cortical parcellation. Connectivity is established using a minimum cost path (mcp) method with an anisotropic local cost function based directly on diffusion weighted images. We refer to this framework as Statistical Analysis of Minimum cost path based Structural Connectivity (SAMSCo) and the weighted structural connectivity networks as mcp-networks. In a proof of principle study we investigated the information contained in mcp-networks by predicting subject age based on the mcp-networks of a group of 974 middle-aged and elderly subjects. Using SAMSCo, age was predicted with an average error of 3. 7years. This was significantly better than predictions based on fractional anisotropy or mean diffusivity averaged over the whole white matter or over the corpus callosum, which showed average prediction errors of at least 4. 8years. Additionally, we classified subjects, based on the mcp-networks, into groups with low and high white matter lesion load, while correcting for age, sex and white matter atrophy. The SAMSCo classification outperformed the classification based on the diffusion measures with a classification accuracy of 76. 0% versus 63. 2%. We also performed a classification in groups with mild and severe atrophy, correcting for age, sex and white matter lesion load. In this case, mcp-networks and diffusion measures yielded similar classification accuracies of 68. 3% and 67. 8% respectively. The SAMSCo prediction and classification experiments indicate that the mcp-networks contain information regarding age, white matter lesion load and white matter atrophy, and that in case of age and white matter lesion load the mcp-network based models outperformed the predictions based on diffusion measures.

YNIMG Journal 2010 Journal Article

Accuracy and reproducibility study of automatic MRI brain tissue segmentation methods

Renske de Boer
Henri A. Vrooman
M. Arfan Ikram
Meike W. Vernooij
Monique M.B. Breteler
Aad van der Lugt
Wiro J. Niessen

The ability to study changes in brain morphometry in longitudinal studies majorly depends on the accuracy and reproducibility of the brain tissue quantification. We evaluate the accuracy and reproducibility of four previously proposed automatic brain tissue segmentation methods: FAST, SPM5, an automatically trained k-nearest neighbor (kNN) classifier, and a conventional kNN classifier based on a prior training set. The intensity nonuniformity correction and skull-stripping mask were the same for all methods. Evaluations were performed on MRI scans of elderly subjects derived from the general population. Accuracy was evaluated by comparison to two manual segmentations of MRI scans of six subjects (mean age 65. 9±4. 4years). Reproducibility was assessed by comparing the automatic segmentations of 30 subjects (mean age 57. 0±3. 7years) who were scanned twice within a short time interval. All methods showed good accuracy and reproducibility, with only small differences between methods. The conventional kNN classifier was the most accurate method with similarity indices of 0. 82/0. 90/0. 94 for cerebrospinal fluid/gray matter/white matter, but it showed the lowest reproducibility. FAST yielded the most reproducible segmentation volumes with volume difference standard deviations of 0. 55/0. 49/0. 38 (percentage of intracranial volume) respectively. The results of the reproducibility experiment can be used to calculate the required number of subjects in the design of a longitudinal study with sufficient power to detect changes over time in brain (tissue) volume. Example sample size calculations demonstrate a rather large effect of the choice of segmentation method on the required number of subjects.

YNIMG Journal 2009 Journal Article

White matter lesion extension to automatic brain tissue segmentation on MRI

Renske de Boer
Henri A. Vrooman
Fedde van der Lijn
Meike W. Vernooij
M. Arfan Ikram
Aad van der Lugt
Monique M.B. Breteler
Wiro J. Niessen

A fully automated brain tissue segmentation method is optimized and extended with white matter lesion segmentation. Cerebrospinal fluid (CSF), gray matter (GM) and white matter (WM) are segmented by an atlas-based k-nearest neighbor classifier on multi-modal magnetic resonance imaging data. This classifier is trained by registering brain atlases to the subject. The resulting GM segmentation is used to automatically find a white matter lesion (WML) threshold in a fluid-attenuated inversion recovery scan. False positive lesions are removed by ensuring that the lesions are within the white matter. The method was visually validated on a set of 209 subjects. No segmentation errors were found in 98% of the brain tissue segmentations and 97% of the WML segmentations. A quantitative evaluation using manual segmentations was performed on a subset of 6 subjects for CSF, GM and WM segmentation and an additional 14 for the WML segmentations. The results indicated that the automatic segmentation accuracy is close to the interobserver variability of manual segmentations.