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Matthan W.A. Caan

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13 papers

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YNICL Journal 2025 Journal Article

Prediction of methylphenidate treatment response for ADHD using conventional and radiomics T1 and DTI features: Secondary analysis of a randomized clinical trial

Mingshi Chen
Zarah van der Pal
Maarten G. Poirot
Anouk Schrantee
Marco Bottelier
Sandra J.J. Kooij
Henk A. Marquering
Liesbeth Reneman

BACKGROUND: Attention-Deficit/Hyperactivity Disorder (ADHD) is commonly treated with methylphenidate (MPH). Although highly effective, MPH treatment still has a relatively high non-response rate of around 30%, highlighting the need for a better understanding of treatment response. Radiomics of T1-weighted images and Diffusion Tensor Imaging (DTI) combined with machine learning approaches could offer a novel method for assessing MPH treatment response. PURPOSE: To evaluate the accuracy of both conventional and radiomics approaches in predicting treatment response based on baseline T1 and DTI data in stimulant-naive ADHD participants. METHODS: We performed a secondary analysis of a randomized clinical trial (ePOD-MPH), involving 47 stimulant-naive ADHD participants (23 boys aged 11.4 ± 0.4 years, 24 men aged 28.1 ± 4.3 years) who underwent 16 weeks of treatment with MPH. Baseline T1-weighted and DTI MRI scans were acquired. Treatment response was assessed at 8 weeks (during treatment) and one week after cessation of 16-week treatment (post-treatment) using the Clinical Global Impressions - Improvement scale as our primary outcome. We compared prediction accuracy using a conventional model and a radiomics model. The conventional approach included the volume of bilateral caudate, putamen, pallidum, accumbens, and hippocampus, and for DTI the mean fractional anisotropy (FA) of the entire brain white matter, bilateral Anterior Thalamic Radiation (ATR), and the splenium of the corpus callosum, totaling 14 regional features. For the radiomics approach, 380 features (shape-based and first-order statistics) were extracted from these 14 regions. XGBoost models with nested cross-validation were used and constructed for the total cohort (n = 47), as well as children (n = 23) and adults (n = 24) separately. Exact binomial tests were employed to compare model performance. RESULTS: For the conventional model, balanced accuracy (bAcc) in predicting treatment response during treatment was 63 % for the total cohort, 32 % for children, and 36 % for adults (Area Under the Receiver Operating Characteristic Curve (AUC-ROC): 0.69, 0.33, 0.41 respectively). Radiomics models demonstrated bAcc's of 68 %, 64 %, and 64 % during treatment (AUC-ROCs of 0.73, 0.62, 0.69 respectively). These predictions were better than chance for both conventional and radiomics models in the total cohort (p = 0.04, p = 0.003 respectively). The radiomics models outperformed the conventional models during treatment in children only (p = 0.02). At post-treatment, performance was markedly reduced. CONCLUSION: While conventional and radiomics models performed equally well in predicting clinical improvement across children and adults during treatment, radiomics features offered enhanced structural information beyond conventional region-based volume and FA averages in children. Prediction of symptom improvement one week after treatment cessation was poor, potentially due to the transient effects of stimulant treatment on symptom improvement.

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YNIMG Journal 2022 Journal Article

A unified model for reconstruction and R 2 * mapping of accelerated 7T data using the quantitative recurrent inference machine

Chaoping Zhang
Dimitrios Karkalousos
Pierre-Louis Bazin
Bram F. Coolen
Hugo Vrenken
Jan-Jakob Sonke
Birte U. Forstmann
Dirk H.J. Poot

Quantitative MRI (qMRI) acquired at the ultra-high field of 7 Tesla has been used in visualizing and analyzing subcortical structures. qMRI relies on the acquisition of multiple images with different scan settings, leading to extended scanning times. Data redundancy and prior information from the relaxometry model can be exploited by deep learning to accelerate the imaging process. We propose the quantitative Recurrent Inference Machine (qRIM), with a unified forward model for joint reconstruction and R 2 * -mapping from sparse data, embedded in a Recurrent Inference Machine (RIM), an iterative inverse problem-solving network. To study the dependency of the proposed extension of the unified forward model to network architecture, we implemented and compared a quantitative End-to-End Variational Network (qE2EVN). Experiments were performed with high-resolution multi-echo gradient echo data of the brain at 7T of a cohort study covering the entire adult life span. The error in reconstructed R 2 * from undersampled data relative to reference data significantly decreased for the unified model compared to sequential image reconstruction and parameter fitting using the RIM. With increasing acceleration factor, an increasing reduction in the reconstruction error was observed, pointing to a larger benefit for sparser data. Qualitatively, this was following an observed reduction of image blurriness in R 2 * -maps. In contrast, when using the U-Net as network architecture, a negative bias in R 2 * in selected regions of interest was observed. Compressed Sensing rendered accurate, but less precise estimates of R 2 *. The qE2EVN showed slightly inferior reconstruction quality compared to the qRIM but better quality than the U-Net and Compressed Sensing. Subcortical maturation over age measured by a linearly increasing interquartile range of R 2 * in the striatum was preserved up to an acceleration factor of 9. With the integrated prior of the unified forward model, the proposed qRIM can exploit the redundancy among repeated measurements and shared information between tasks, facilitating relaxometry in accelerated MRI.

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YNICL Journal 2021 Journal Article

Deep brain stimulation response in obsessive–compulsive disorder is associated with preoperative nucleus accumbens volume

Luka C. Liebrand
Paul Zhutovsky
Eva K. Tolmeijer
Ilse Graat
Nienke Vulink
Pelle de Koning
Martijn Figee
P. Richard Schuurman

BACKGROUND: Deep brain stimulation (DBS) is a new treatment option for patients with therapy-resistant obsessive-compulsive disorder (OCD). Approximately 60% of patients benefit from DBS, which might be improved if a biomarker could identify patients who are likely to respond. Therefore, we evaluated the use of preoperative structural magnetic resonance imaging (MRI) in predicting treatment outcome for OCD patients on the group- and individual-level. METHODS: In this retrospective study, we analyzed preoperative MRI data of a large cohort of patients who received DBS for OCD (n = 57). We used voxel-based morphometry to investigate whether grey matter (GM) or white matter (WM) volume surrounding the DBS electrode (nucleus accumbens (NAc), anterior thalamic radiation), and whole-brain GM/WM volume were associated with OCD severity and response status at 12-month follow-up. In addition, we performed machine learning analyses to predict treatment outcome at an individual-level and evaluated its performance using cross-validation. RESULTS: < 0.05). None of the individual-level regression/classification analyses exceeded chance-level performance. CONCLUSIONS: These results provide evidence that patients with larger NAc volumes show a better response to DBS, indicating that DBS success is partly determined by individual differences in brain anatomy. However, the results also indicate that structural MRI data alone does not provide sufficient information to guide clinical decision making at an individual level yet.

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YNICL Journal 2021 Journal Article

Magnetic resonance spectroscopy as marker for neurodegeneration in X-linked adrenoleukodystrophy

Stephanie I.W. van de Stadt
Anouk Schrantee
Irene C. Huffnagel
Wouter J.C. van Ballegoij
Matthan W.A. Caan
Petra J.W. Pouwels
Marc Engelen

X-linked adrenoleukodsytrophy (ALD) is a genetic neuro-metabolic disorder, causing a slowly progressive myelopathy in adult male and female patients. New disease modifying therapies for myelopathy are under development. This calls for new (imaging) markers able to measure disease severity and progression in clinical trials. In this prospective cohort study, we measured cerebral metabolite levels with Magnetic Resonance Spectroscopy (MRS), and evaluated their potential as biomarkers for disease severity and neurodegeneration in ALD. We used a comprehensive protocol of 3T Magnetic Resonance Spectroscopic Imaging (MRSI) and 7T Single Voxel Spectroscopy (SVS) in a large cohort of adult ALD males without cerebral demyelination. One hundred seven baseline scans - 59 obtained in ALD patients (42 3T MRSI and 17 7T SVS) and 48 obtained in healthy male controls (32 3T MRSI and 16 7T SVS) - and 82 one and two-year follow-up scans (66 3T MRSI and 16 7T SVS) of ALD patients were included. Both protocols showed significantly lower concentration ratios of N-acetylaspartate/creatine (tNAA/tCr) and Glx (glutamine + glutamate)/tCr in the grey and white matter of patients, compared to controls. A novel finding is the higher level of inositol (Ins)/tCr and choline containing compounds (tCho)/tCr in ALD patients without cerebral demyelination. Furthermore, tNAA/tCr correlated strongly with clinical measures of severity of myelopathy. There was no detectable change in metabolite ratios after one-year or two-year follow-up. Our results imply that cerebral metabolite levels - and more specifically the tNAA/tCr ratio - measured with MRS, have potential value as (imaging) biomarkers in ALD.

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YNICL Journal 2020 Journal Article

Distance to white matter trajectories is associated with treatment response to internal capsule deep brain stimulation in treatment-refractory depression

Luka C. Liebrand
Samuel J. Natarajan
Matthan W.A. Caan
P. Richard Schuurman
Pepijn van den Munckhof
Bart de Kwaasteniet
Judy Luigjes
Isidoor O. Bergfeld

BACKGROUND: Deep brain stimulation (DBS) is an innovative treatment for treatment-refractory depression. DBS is usually targeted at specific anatomical landmarks, with patients responding to DBS in approximately 50% of cases. Attention has recently shifted to white matter tracts to explain DBS response, with initial open-label trials targeting white matter tracts yielding much higher response rates (>70%). OBJECTIVE/HYPOTHESIS: Our aim was to associate distance to individual white matter tracts around the stimulation target in the ventral anterior limb of the internal capsule to treatment response. METHODS: We performed diffusion magnetic resonance tractography of the superolateral branch of the medial forebrain bundle and the anterior thalamic radiation in fourteen patients that participated in our randomized clinical trial. We combined the tract reconstructions with the postoperative images to identify the DBS leads and estimated the distance between tracts and leads, which we subsequently associated with treatment response. RESULTS: Stimulation closer to both tracts was significantly correlated to a larger symptom decrease (r = 0.61, p = 0.02), suggesting that stimulation more proximal to the tracts was beneficial. Biophysical modelling indicated that 37.5% of tracts were even outside the volume of activated tissue. There was no difference in lead placement with respect to anatomical landmarks, which could mean that differences in treatment response were driven by individual differences in white matter anatomy. CONCLUSIONS: Our results suggest that deep brain stimulation of the ventral anterior limb of the internal capsule could benefit from targeting white matter bundles. We recommend acquiring diffusion magnetic resonance data for each individual patient.

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YNIMG Journal 2020 Journal Article

ExploreASL: An image processing pipeline for multi-center ASL perfusion MRI studies

Henk J.M.M. Mutsaerts
Jan Petr
Paul Groot
Pieter Vandemaele
Silvia Ingala
Andrew D. Robertson
Lena Václavů
Inge Groote

Arterial spin labeling (ASL) has undergone significant development since its inception, with a focus on improving standardization and reproducibility of its acquisition and quantification. In a community-wide effort towards robust and reproducible clinical ASL image processing, we developed the software package ExploreASL, allowing standardized analyses across centers and scanners. The procedures used in ExploreASL capitalize on published image processing advancements and address the challenges of multi-center datasets with scanner-specific processing and artifact reduction to limit patient exclusion. ExploreASL is self-contained, written in MATLAB and based on Statistical Parameter Mapping (SPM) and runs on multiple operating systems. To facilitate collaboration and data-exchange, the toolbox follows several standards and recommendations for data structure, provenance, and best analysis practice. ExploreASL was iteratively refined and tested in the analysis of >10, 000 ASL scans using different pulse-sequences in a variety of clinical populations, resulting in four processing modules: Import, Structural, ASL, and Population that perform tasks, respectively, for data curation, structural and ASL image processing and quality control, and finally preparing the results for statistical analyses on both single-subject and group level. We illustrate ExploreASL processing results from three cohorts: perinatally HIV-infected children, healthy adults, and elderly at risk for neurodegenerative disease. We show the reproducibility for each cohort when processed at different centers with different operating systems and MATLAB versions, and its effects on the quantification of gray matter cerebral blood flow. ExploreASL facilitates the standardization of image processing and quality control, allowing the pooling of cohorts which may increase statistical power and discover between-group perfusion differences. Ultimately, this workflow may advance ASL for wider adoption in clinical studies, trials, and practice.

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YNIMG Journal 2020 Journal Article

Sharpness in motion corrected quantitative imaging at 7T

Pierre-Louis Bazin
Hannah E. Nijsse
Wietske van der Zwaag
Daniel Gallichan
Anneke Alkemade
Frans M. Vos
Birte U. Forstmann
Matthan W.A. Caan

Sub-millimeter imaging at 7T has opened new possibilities for qualitatively and quantitatively studying brain structure as it evolves throughout the life span. However, subject motion introduces image blurring on the order of magnitude of the spatial resolution and is thus detrimental to image quality. Such motion can be corrected for, but widespread application has not yet been achieved and quantitative evaluation is lacking. This raises a need to quantitatively measure image sharpness throughout the brain. We propose a method to quantify sharpness of brain structures at sub-voxel resolution, and use it to assess to what extent limited motion is related to image sharpness. The method was evaluated in a cohort of 24 healthy volunteers with a wide and uniform age range, aiming to arrive at results that largely generalize to larger populations. Using 3D fat-excited motion navigators, quantitative R 1, R 2 * and Quantitative Susceptibility Maps and T 1-weighted images were retrospectively corrected for motion. Sharpness was quantified in all modalities for selected regions of interest (ROI) by fitting the sigmoidally shaped error function to data within locally homogeneous clusters. A strong, almost linear correlation between motion and sharpness improvement was observed, and motion correction significantly improved sharpness. Overall, the Full Width at Half Maximum reduced from 0. 88 mm to 0. 70 mm after motion correction, equivalent to a 2. 0 times smaller voxel volume. Motion and sharpness were not found to correlate with the age of study participants. We conclude that in our data, motion correction using fat navigators is overall able to restore the measured sharpness to the imaging resolution, irrespective of the amount of motion observed during scanning.

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YNICL Journal 2020 Journal Article

Spatial versus angular resolution for tractography-assisted planning of deep brain stimulation

Luka C. Liebrand
Guido A. van Wingen
Frans M. Vos
Damiaan Denys
Matthan W.A. Caan

Given the restricted total scanning time for clinical neuroimaging, it is unclear whether clinical diffusion MRI protocols would benefit more from higher spatial resolution or higher angular resolution. In this work, we investigated the relative benefit of improving spatial or angular resolution in diffusion MRI to separate two parallel running white matter tracts that are targets for deep brain stimulation: the anterior thalamic radiation and the supero-lateral branch of the medial forebrain bundle. Both these tracts are situated in the ventral anterior limb of the internal capsule, and recent studies suggest that targeting a specific tract could improve treatment efficacy. Therefore, we scanned 19 healthy volunteers at 3T and 7T according to three diffusion MRI protocols with respectively standard clinical settings, increased spatial resolution of 1. 4 mm, and increased angular resolution (64 additional gradient directions at b = 2200s/mm2). We performed probabilistic tractography for all protocols and quantified the separability of both tracts. The higher spatial resolution protocol improved separability by 41% with respect to the clinical standard, presumably due to decreased partial voluming. The higher angular resolution protocol resulted in increased apparent tract volumes and overlap, which is disadvantageous for application in precise treatment planning. We thus recommend to increase the spatial resolution for deep brain stimulation planning to 1. 4 mm while maintaining angular resolution. This recommendation complements the general advice to aim for high angular resolution to resolve crossing fibers, confirming that the specific application and anatomical considerations are leading in clinical diffusion MRI protocol optimization.

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YNICL Journal 2020 Journal Article

The muscarinic M1 receptor modulates associative learning and memory in psychotic disorders

Geor Bakker
Claudia Vingerhoets
Oswald J.N. Bloemen
Barbara J. Sahakian
Jan Booij
Matthan W.A. Caan
Thérèse A.M.J. van Amelsvoort

BACKGROUND: receptor signaling in psychotic disorders. METHODS: I-IDEX single photon emission computed tomography. RESULTS: receptor binding in the DLPFC was related to greater functional sensitivity to biperiden of the cingulate gyrus during the memory phase. CONCLUSION: receptor reserve in temporal-limbic areas.

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YNICL Journal 2018 Journal Article

ADHD and maturation of brain white matter: A DTI study in medication naive children and adults

Cheima Bouziane
Matthan W.A. Caan
Hyke G.H. Tamminga
Anouk Schrantee
Marco A. Bottelier
Michiel B. de Ruiter
Sandra J.J. Kooij
Liesbeth Reneman

Several diffusion tensor imaging (DTI) studies in attention deficit hyperactivity disorder (ADHD) have shown a delay in brain white matter (WM) development. Because these studies were mainly conducted in children and adolescents, these WM abnormalities have been assumed, but not proven to progress into adulthood. To provide further insight in the natural history of WM maturation delay in ADHD, we here investigated the modulating effect of age on WM in children and adults. 120 stimulant-treatment naive male ADHD children (10-12 years of age) and adults (23-40 years of age) with ADHD (according to DSM-IV; all subtypes) were included, along with 23 age and gender matched controls. Fractional anisotropy (FA) values were compared throughout the WM by means of tract-based spatial statistics (TBSS) and in specific regions of interest (ROIs). On both TBSS and ROI analyses, we found that stimulant-treatment naive ADHD children did not differ in FA values from control children, whereas adult ADHD subjects had reduced FA values when compared to adult controls in several regions. Significant age × group interactions for whole brain FA (p = 0.015), as well as the anterior thalamic radiation (p = 0.015) suggest that ADHD affects the brain WM age-dependently. In contrast to prior studies conducted in medicated ADHD children, we did not find WM alterations in stimulant treatment naïve children, only treatment-naïve adults. Thus, our findings suggest that the reported developmental delay in WM might appear after childhood, and that previously reported differences between ADHD children and normal developing peers could have been attributed to prior ADHD medications, and/or other factors that affect WM development, such as age and gender.

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YNIMG Journal 2017 Journal Article

Predicting brain age with deep learning from raw imaging data results in a reliable and heritable biomarker

James H. Cole
Rudra P.K. Poudel
Dimosthenis Tsagkrasoulis
Matthan W.A. Caan
Claire Steves
Tim D. Spector
Giovanni Montana

Machine learning analysis of neuroimaging data can accurately predict chronological age in healthy people. Deviations from healthy brain ageing have been associated with cognitive impairment and disease. Here we sought to further establish the credentials of ‘brain-predicted age’ as a biomarker of individual differences in the brain ageing process, using a predictive modelling approach based on deep learning, and specifically convolutional neural networks (CNN), and applied to both pre-processed and raw T1-weighted MRI data. Firstly, we aimed to demonstrate the accuracy of CNN brain-predicted age using a large dataset of healthy adults (N = 2001). Next, we sought to establish the heritability of brain-predicted age using a sample of monozygotic and dizygotic female twins (N = 62). Thirdly, we examined the test-retest and multi-centre reliability of brain-predicted age using two samples (within-scanner N = 20; between-scanner N = 11). CNN brain-predicted ages were generated and compared to a Gaussian Process Regression (GPR) approach, on all datasets. Input data were grey matter (GM) or white matter (WM) volumetric maps generated by Statistical Parametric Mapping (SPM) or raw data. CNN accurately predicted chronological age using GM (correlation between brain-predicted age and chronological age r = 0. 96, mean absolute error [MAE] = 4. 16 years) and raw (r = 0. 94, MAE = 4. 65 years) data. This was comparable to GPR brain-predicted age using GM data (r = 0. 95, MAE = 4. 66 years). Brain-predicted age was a heritable phenotype for all models and input data (h2 ≥ 0. 5). Brain-predicted age showed high test-retest reliability (intraclass correlation coefficient [ICC] = 0. 90–0. 99). Multi-centre reliability was more variable within high ICCs for GM (0. 83–0. 96) and poor-moderate levels for WM and raw data (0. 51–0. 77). Brain-predicted age represents an accurate, highly reliable and genetically-influenced phenotype, that has potential to be used as a biomarker of brain ageing. Moreover, age predictions can be accurately generated on raw T1-MRI data, substantially reducing computation time for novel data, bringing the process closer to giving real-time information on brain health in clinical settings.

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YNICL Journal 2013 Journal Article

Accurate white matter lesion segmentation by k nearest neighbor classification with tissue type priors (kNN-TTPs)

Martijn D. Steenwijk
Petra J.W. Pouwels
Marita Daams
Jan Willem van Dalen
Matthan W.A. Caan
Edo Richard
Frederik Barkhof
Hugo Vrenken

INTRODUCTION: The segmentation and volumetric quantification of white matter (WM) lesions play an important role in monitoring and studying neurological diseases such as multiple sclerosis (MS) or cerebrovascular disease. This is often interactively done using 2D magnetic resonance images. Recent developments in acquisition techniques allow for 3D imaging with much thinner sections, but the large number of images per subject makes manual lesion outlining infeasible. This warrants the need for a reliable automated approach. Here we aimed to improve k nearest neighbor (kNN) classification of WM lesions by optimizing intensity normalization and using spatial tissue type priors (TTPs). METHODS: The kNN-TTP method used kNN classification with 3.0 T 3DFLAIR and 3DT1 intensities as well as MNI-normalized spatial coordinates as features. Additionally, TTPs were computed by nonlinear registration of data from healthy controls. Intensity features were normalized using variance scaling, robust range normalization or histogram matching. The algorithm was then trained and evaluated using a leave-one-out experiment among 20 patients with MS against a reference segmentation that was created completely manually. The performance of each normalization method was evaluated both with and without TTPs in the feature set. Volumetric agreement was evaluated using intra-class coefficient (ICC), and voxelwise spatial agreement was evaluated using Dice similarity index (SI). Finally, the robustness of the method across different scanners and patient populations was evaluated using an independent sample of elderly subjects with hypertension. RESULTS: The intensity normalization method had a large influence on the segmentation performance, with average SI values ranging from 0.66 to 0.72 when no TTPs were used. Independent of the normalization method, the inclusion of TTPs as features increased performance particularly by reducing the lesion detection error. Best performance was achieved using variance scaled intensity features and including TTPs in the feature set: this yielded ICC = 0.93 and average SI = 0.75 ± 0.08. Validation of the method in an independent sample of elderly subjects with hypertension, yielded even higher ICC = 0.96 and SI = 0.84 ± 0.14. CONCLUSION: Adding TTPs increases the performance of kNN based MS lesion segmentation methods. Best performance was achieved using variance scaling for intensity normalization and including TTPs in the feature set, showing excellent agreement with the reference segmentations across a wide range of lesion severity, irrespective of the scanner used or the pathological substrate of the lesions.

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YNIMG Journal 2013 Journal Article

No association between striatal dopamine transporter binding and body mass index: A multi-center European study in healthy volunteers

Elsmarieke van de Giessen
Swen Hesse
Matthan W.A. Caan
Franziska Zientek
John C. Dickson
Livia Tossici-Bolt
Terez Sera
Susanne Asenbaum

Introduction Dopamine is one among several neurotransmitters that regulate food intake and overeating. Thus, it has been linked to the pathophysiology of obesity and high body mass index (BMI). Striatal dopamine D2 receptor availability is lower in obesity and there are indications that striatal dopamine transporter (DAT) availability is also decreased. In this study, we tested whether BMI and striatal DAT availability are associated. Methods The study included 123 healthy individuals from a large European multi-center database. They had a BMI range of 18. 2–41. 1kg/m2 and were scanned using [123I]FP-CIT SPECT imaging. Scans were analyzed with both region-of-interest and voxel-based analysis to determine the binding potential for DAT availability in the caudate nucleus and putamen. A direct relation between BMI and DAT availability was assessed and groups with high and low BMI were compared for DAT availability. Results No association between BMI and striatal DAT availability was found. Conclusion The lack of an association between BMI and striatal DAT availability suggests that the regulation of striatal synaptic dopamine levels by DAT plays no or a limited role in the pathophysiology of overweight and obesity.

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