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Liesbeth Reneman

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6 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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YNICL Journal 2023 Journal Article

Neurometabolite changes in response to antidepressant medication: A systematic review of 1H-MRS findings

Daphne E. Boucherie
Liesbeth Reneman
Henricus G. Ruhé
Anouk Schrantee

Selective serotonin reuptake inhibitors (SSRIs), serotonin and noradrenaline reuptake inhibitors (SNRIs), and (es)ketamine are used to treat major depressive disorder (MDD). These different types of medication may involve common neural pathways related to glutamatergic and GABAergic neurotransmitter systems, both of which have been implicated in MDD pathology. We conducted a systematic review of pharmacological proton Magnetic Resonance Spectroscopy (1H-MRS) studies in healthy volunteers and individuals with MDD to explore the potential impact of these medications on glutamatergic and GABAergic systems. We searched PubMed, Web of Science and Embase and included randomized controlled trials or cohort studies, which assessed the effects of SSRIs, SNRIs, or (es)ketamine on glutamate, glutamine, Glx or GABA using single-voxel 1H-MRS or Magnetic Resonance Spectroscopic Imaging (MRSI). Additionally, studies were included when they used a field strength > 1. 5 T, and when a comparison of metabolite levels between antidepressant treatment and placebo or baseline with post-medication metabolite levels was done. We excluded animal studies, duplicate publications, or articles with 1H-MRS data already described in another included article. Twenty-nine studies were included in this review. Fifteen studies investigated the effect of administration or treatment with SSRIs or SNRIs, and fourteen studies investigated the effect of (es)ketamine on glutamatergic and GABAergic metabolite levels. Studies on SSRIs and SNRIs were highly variable, generally underpowered, and yielded no consistent findings across brain regions or specific populations. Although studies on (es)ketamine were also highly variable, some demonstrated an increase in glutamate levels in the anterior cingulate cortex in a time-dependent manner after administration. Our findings highlight the need for standardized study and acquisition protocols. Additionally, measuring metabolites dynamically over time or combining 1H-MRS with whole brain functional imaging techniques could provide valuable insights into the effects of these medications on glutamate and GABAergic neurometabolism.

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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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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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YNICL Journal 2017 Journal Article

Age-dependent, lasting effects of methylphenidate on the GABAergic system of ADHD patients

Michelle M. Solleveld
Anouk Schrantee
Nicolaas A.J. Puts
Liesbeth Reneman
Paul J. Lucassen

Stimulants are the main pharmacological treatment for patients with attention-deficit/hyperactivity disorder (ADHD). Their current prescription rates are rising, both in children, adolescents and adults. Related to the impulse control phenotype, both preclinical and clinical studies have demonstrated lower γ-amino butyric acid (GABA) levels in prefrontal brain regions in ADHD. Whereas stimulant treatment increases GABA levels, preclinical studies have suggested that stimulant treatment effects may be age-dependent. As the long-term consequences of stimulant use in ADHD children and adolescents have so far been poorly studied, we used magnetic resonance spectroscopy to assess GABA+ and glutamate + glutamine (Glx) levels in the medial prefrontal cortex (mPFC) of adult ADHD patients, both before and after an oral methylphenidate (MPH) challenge. Three groups were studied: 1) ADHD patients who were first treated with stimulants before 16 years of age, i.e. during periods of ongoing brain development (early-stimulant-treated, EST); 2) patients first treated with stimulants in adulthood (i.e. > 23 years) (late-stimulant-treated, LST), and 3) stimulant-treatment-naive (STN) ADHD patients. Reduced basal GABA+ levels were found in EST compared to LST patients (p = 0.04), while after an MPH challenge, only the EST patients showed significant increases in GABA+ (p = 0.01). For Glx, no differences were found at baseline, nor after an MPH challenge. First stimulant exposure at a young age is thus associated with lower baseline levels of GABA+ and increased responsivity in adulthood. This effect could not be found in patients that started treatment at an adult age. Hence, while adult stimulant treatment seems to exert no major effects on GABA+ levels in the mPFC, MPH may induce long-lasting alterations in the adult mPFC GABAergic system when treatment was started at a young age.

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YNICL Journal 2015 Journal Article

Multimodal MRI and cognitive function in patients with breast cancer prior to adjuvant treatment — The role of fatigue

Sanne Menning
Michiel B. de Ruiter
Dick J. Veltman
V. Koppelmans
Clemens Kirschbaum
Willem Boogerd
Liesbeth Reneman
Sanne B. Schagen

An increasing body of literature indicates that chemotherapy (ChT) for breast cancer (BC) is associated with adverse effects on the brain. Recent research suggests that cognitive and brain function in patients with BC may already be compromised before the start of chemotherapy. This is the first study combining neuropsychological testing, patient-reported outcomes, and multimodal magnetic resonance imaging (MRI) to examine pretreatment cognition and various aspects of brain function and structure in a large sample. Thirty-two patients with BC scheduled to receive ChT (pre-ChT+), 33 patients with BC not indicated to undergo ChT (pre-ChT-), and 38 no-cancer controls (NCs) were included. The examination consisted of a neuropsychological test battery, self-reported aspects of psychosocial functioning, and multimodal MRI. Patients with BC reported worse scores on several aspects of quality of life, such as higher levels of fatigue and stress. However, cortisol levels were not elevated in the patient groups compared to the control group. Overall cognitive performance was lower in the pre-ChT+ and the pre-ChT- groups compared to NC. Further, patients demonstrated prefrontal hyperactivation with increasing task difficulty on a planning task compared to NC, but not during a memory task. White matter integrity was lower in both patient groups. No differences in regional brain volume and brain metabolites were found. The cognitive and imaging data converged to show that symptoms of fatigue were associated with the observed abnormalities; the observed differences were no longer significant when fatigue was accounted for. This study suggests that cancer-related psychological or biological processes may adversely impact cognitive functioning and associated aspects of brain structure and function before the start of adjuvant treatment. Our findings stress the importance to further explore the processes underlying the expression of fatigue and to study whether it has a contributory role in subsequent treatment-related cognitive decline.

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