Automated White Matter Hyperintensity Segmentation Using Bayesian Model Selection: Assessment and Correlations with Cognitive Change.

Accurate, automated white matter hyperintensity (WMH) segmentations are needed for large-scale studies to understand contributions of WMH to neurological diseases. We evaluated Bayesian Model Selection (BaMoS), a hierarchical fully-unsupervised model selection framework for WMH segmentation. We compared BaMoS segmentations to semi-automated segmentations, and assessed whether they predicted longitudinal cognitive change in control, early Mild Cognitive Impairment (EMCI), late Mild Cognitive Impairment (LMCI), subjective/significant memory concern (SMC) and Alzheimer's (AD) participants. Data were downloaded from the Alzheimer's disease Neuroimaging Initiative (ADNI). Magnetic resonance images from 30 control and 30 AD participants were selected to incorporate multiple scanners, and were semi-automatically segmented by 4 raters and BaMoS. Segmentations were assessed using volume correlation, Dice score, and other spatial metrics. Linear mixed-effect models were fitted to 180 control, 107 SMC, 320 EMCI, 171 LMCI and 151 AD participants separately in each group, with the outcomes being cognitive change (e.g. mini-mental state examination; MMSE), and BaMoS WMH, age, sex, race and education used as predictors. There was a high level of agreement between BaMoS' WMH segmentation volumes and a consensus of rater segmentations, with a median Dice score of 0.74 and correlation coefficient of 0.96. BaMoS WMH predicted cognitive change in: control, EMCI, and SMC groups using MMSE; LMCI using clinical dementia rating scale; and EMCI using Alzheimer's disease assessment scale-cognitive subscale (p < 0.05, all tests). BaMoS compares well to semi-automated segmentation, is robust to different WMH loads and scanners, and can generate volumes which predict decline. BaMoS can be applicable to further large-scale studies

Distinguishing patterns in the dynamics of long-term medication use by Markov analysis: beyond persistence

<p>Abstract</p> <p>Background</p> <p>In order to accurately distinguish gaps of varying length in drug treatment for chronic conditions from discontinuation without resuming therapy, short-term observation does not suffice. Thus, the use of inhalation corticosteroids (ICS) in the long-term, during a ten-year period is investigated. To describe medication use as a continuum, taking into account the timeliness and consistency of refilling, a Markov model is proposed.</p> <p>Methods</p> <p>Patients, that filled at least one prescription in 1993, were selected from the PHARMO medical record linkage system (RLS) containing >95% prescription dispensings per patient originating from community pharmacy records of 6 medium-sized cities in the Netherlands.</p> <p>The probabilities of continuous use, the refilling of at least one ICS prescription in each year of follow-up, and medication free periods were assessed by Markov analysis. Stratified analysis according to new use was performed.</p> <p>Results</p> <p>The transition probabilities of the refilling of at least one ICS prescription in the subsequent year of follow-up, were assessed for each year of follow-up and for the total study period.</p> <p>The change of transition probabilities in time was evaluated, e.g. the probability of continuing ICS use of starters in the first two years (51%) of follow-up increased to more than 70% in the following years. The probabilities of different patterns of medication use were assessed: continuous use (7.7%), cumulative medication gaps (1–8 years 69.1%) and discontinuing (23.2%) during ten-year follow-up for new users. New users had lower probability of continuous use (7.7%) and more variability in ICS refill patterns than previous users (56%).</p> <p>Conclusion</p> <p>In addition to well-established methods in epidemiology to ascertain compliance and persistence, a Markov model could be useful to further specify the variety of possible patterns of medication use within the continuum of adherence. This Markov model describes variation in behaviour and patterns of ICS use and could also be useful to investigate continuous use of other drugs applied in chronic diseases.</p

Opioid prescribing patterns after arthroplasty of the knee and hip: a Dutch nationwide cohort study from 2013 to 2018

BACKGROUND AND PURPOSE: Numbers on opioid prescriptions over time in arthroplasty patients are currently lacking. Therefore we determined the annual opioid prescribing rate in patients who received a hip/knee arthroplasty (HA/KA) between 2013 and 2018. PATIENTS AND METHODS: The Dutch Foundation for Pharmaceutical Statistics, which provides national coverage of medication prescriptions, was linked to the Dutch Arthroplasty Register, which provides arthroplasty procedures. The opioid prescription rates were expressed as the number of defined daily dosages (DDD) and morphine milligram equivalent (MME) per person year (PY) and stratified for primary and revision arthroplasty. Amongst subgroups for age (< 75; ≥ 75 years) and sex for primary osteoarthritis arthroplasties, prescription rates stratified for opioid type (weak/strong) and prevalent preoperative opioid prescriptions (yes/no) were assessed. RESULTS: 48,051 primary KAs and 53,964 HAs were included, and 3,540 revision KAs and 4,118 HAs. In 2013, after primary KA 58% were dispensed ≥ 1 opioid within the first year; this increased to 89% in 2018. For primary HA these numbers increased from 38% to 75%. In KAs the prescription rates increased from 13.1 DDD/PY to 14.4 DDD/PY, mainly due to oxycodone prescriptions (2.9 DDD/PY to 7.3 DDD/PY), while tramadol decreased (7.3 DDD/PY to 4.6 DDD/PY). The number of MME/PY also increased (888 MME/PY to 1224 MME/PY). Similar changes were observed for HA and revision arthroplasties. Irrespective of joint, prescription of opioid medication increased over time, with highest levels in groups with preoperative opioid prescriptions while weak opioid prescriptions decreased. INTERPRETATION: In the Netherlands, between 2013 and 2018 postoperative opioid prescriptions after KA and HA increased, mainly due to increased oxycodone prescriptions with highest levels after surgeries with preoperative prescriptions

MRBrainS Challenge: Online Evaluation Framework for Brain Image Segmentation in 3T MRI Scans

Many methods have been proposed for tissue segmentation in brain MRI scans. The multitude of methods proposed complicates the choice of one method above others. We have therefore established the MRBrainS online evaluation framework for evaluating (semi) automatic algorithms that segment gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) on 3T brain MRI scans of elderly subjects (65-80 y). Participants apply their algorithms to the provided data, after which their results are evaluated and ranked. Full manual segmentations of GM, WM, and CSF are available for all scans and used as the reference standard. Five datasets are provided for training and fifteen for testing. The evaluated methods are ranked based on their overall performance to segment GM, WM, and CSF and evaluated using three evaluation metrics (Dice, H95, and AVD) and the results are published on the MRBrainS13 website. We present the results of eleven segmentation algorithms that participated in the MRBrainS13 challenge workshop at MICCAI, where the framework was launched, and three commonly used freeware packages: FreeSurfer, FSL, and SPM. The MRBrainS evaluation framework provides an objective and direct comparison of all evaluated algorithms and can aid in selecting the best performing method for the segmentation goal at hand.This study was financially supported by IMDI Grant 104002002 (Brainbox) from ZonMw, the Netherlands Organisation for Health Research and Development, within kind sponsoring by Philips, the University Medical Center Utrecht, and Eindhoven University of Technology. The authors would like to acknowledge the following members of the Utrecht Vascular Cognitive Impairment Study Group who were not included as coauthors of this paper but were involved in the recruitment of study participants and MRI acquisition at the UMC Utrecht (in alphabetical order by department): E. van den Berg, M. Brundel, S. Heringa, and L. J. Kappelle of the Department of Neurology, P. R. Luijten and W. P. Th. M. Mali of the Department of Radiology, and A. Algra and G. E. H. M. Rutten of the Julius Center for Health Sciences and Primary Care. The research of Geert Jan Biessels and the VCI group was financially supported by VIDI Grant 91711384 from ZonMw and by Grant 2010T073 of the Netherlands Heart Foundation. The research of Jeroen de Bresser is financially supported by a research talent fellowship of the University Medical Center Utrecht (Netherlands). The research of Annegreet van Opbroek and Marleen de Bruijne is financially supported by a research grant from NWO (the Netherlands Organisation for Scientific Research). The authors would like to acknowledge MeVis Medical Solutions AG (Bremen, Germany) for providing MeVisLab. Duygu Sarikaya and Liang Zhao acknowledge their Advisor Professor Jason Corso for his guidance. Duygu Sarikaya is supported by NIH 1 R21CA160825-01 and Liang Zhao is partially supported by the China Scholarship Council (CSC).info:eu-repo/semantics/publishedVersio

Bayesian Model Selection for Pathological Neuroimaging Data Applied to White Matter Lesion Segmentation

In neuroimaging studies, pathologies can present themselves as abnormal intensity patterns. Thus, solutions for detecting abnormal intensities are currently under investigation. As each patient is unique, an unbiased and biologically plausible model of pathological data would have to be able to adapt to the subject's individual presentation. Such a model would provide the means for a better understanding of the underlying biological processes and improve one's ability to define pathologically meaningful imaging biomarkers. With this aim in mind, this work proposes a hierarchical fully unsupervised model selection framework for neuroimaging data which enables the distinction between different types of abnormal image patterns without pathological a priori knowledge. Its application on simulated and clinical data demonstrated the ability to detect abnormal intensity clusters, resulting in a competitive to improved behavior in white matter lesion segmentation when compared to three other freelyavailable automated methods

An Update on Less Invasive and Endoscopic Techniques Mimicking the Effect of Bariatric Surgery

Obesity (BMI 30–35 kg/m2) and its associated disorders such as type 2 diabetes, nonalcoholic fatty liver disease, and cardiovascular disease have reached pandemic proportions worldwide. For the morbidly obese population (BMI 35–50 kg/m2), bariatric surgery has proven to be the most effective treatment to achieve significant and sustained weight loss, with concomitant positive effects on the metabolic syndrome. However, only a minor percentage of eligible candidates are treated by means of bariatric surgery. In addition, the expanding obesity epidemic consists mostly of relatively less obese patients who are not (yet) eligible for bariatric surgery. Hence, less invasive techniques and devices are rapidly being developed. These novel entities mimic several aspects of bariatric surgery either by gastric restriction (gastric balloons, gastric plication), by influencing gastric function (gastric botulinum injections, gastric pacing, and vagal nerve stimulation), or by partial exclusion of the small intestine (duodenal-jejunal sleeve). In the last decade, several novel less invasive techniques have been introduced and some have been abandoned again. The aim of this paper is to discuss the safety, efficacy, complications, reversibility, and long-term results of these latest developments in the treatment of obesity

Cerebral Microbleeds Are Not Associated with Long-Term Cognitive Outcome in Patients with Transient Ischemic Attack or Minor Stroke

Background: Cerebral microbleeds have been related to cerebrovascular disease and dementia. They occur more frequently in patients with ischemic stroke than in the general population, but their relation to cognition in these patients is uncertain, particularly in the long run. We examined the relationship between microbleeds in patients with a transient ischemic attack (TIA) or minor ischemic stroke, and cognitive performance 4 years later. Methods: Participants were recruited from a prospective multicenter cohort of patients with a TIA or minor ischemic stroke (n = 397). They underwent magnetic resonance imaging (MRI), including a T2*-weighted sequence, within 3 months after their ischemic event. Microbleeds, atrophy, lacunae and white matter hyperintensities (WMH) were rated visually. Cognitive status was examined in 94% of all patients who were still alive after a mean interval of 3.8 years by the Dutch version of the Telephone Interview for Cognitive Status (TICS; n = 280) or by an Informant Questionnaire on Cognitive Decline in the Elderly (IQCODE) obtained from a close relative if a TICS could not be obtained (n = 48). The relationship between presence of microbleeds and TICS or IQCODE score was assessed with linear regression analyses adjusted for age, sex, educational level and time interval between MRI and cognitive evaluation. Results: The mean age was 65 +/- 12 years at inclusion. The vascular event at inclusion was a TIA in 170 patients (52%) and a minor ischemic stroke in 155 patients (47%). Microbleeds were present in 11.6% of the patients. Patients with microbleeds were significantly older than patients without microbleeds (70 +/- 9 vs. 64 +/- 12 years), more often had hypertension, and had more cerebral atrophy, WMH and lacunae on MRI (all p <0.05). The mean TICS score was 35.3 +/- 5.9 for patients with microbleeds (n = 29) and 34.6 +/- 5.2 for patients without microbleeds (n = 251); the adjusted mean difference (95% CI) was 1.69 (-0.01 to 3.38). The total IQCODE score was 66.0 +/- 10.8 for patients with microbleeds (n = 9) and 63.1 +/- 12.9 for patients without microbleeds (n = 39); the adjusted mean difference was 2.43 (-7.55 to 12.41). The relative risk (adjusted for age) for abnormal cognitive performance when having microbleeds was 1.19 (95% CI: 0.63-2.26). Subcortical atrophy was associated with lower TICS score [standardized regression coefficient beta: -0.12 (-0.23 to 0.00); p = 0.04] and with lower IQCODE score [0.51 (0.19-0.83); p = 0.00]. The adjusted mean difference of IQCODE scores between patients with and those without a lacunar infarct was 0.39 (0.12-0.65; p = 0.01). Conclusions: In this sample of patients with a recent TIA or minor ischemic stroke, microbleeds were not associated with cognitive performance 4 years later. Apparently, this association is different from other markers of small vessel disease. (C) 2014 S. Karger AG, Base

Employing visual analytics to aid the design of white matter hyperintensity classifiers

Accurate segmentation of brain white matter hyperintensities (WMHs) is important for prognosis and disease monitoring. To this end,classifiers are often trained – usually,using T1 and FLAIR weighted MR images. Incorporating additional features,derived from diffusion weighted MRI,could improve classification. However,the multitude of diffusion-derived features requires selecting the most adequate. For this,automated feature selection is commonly employed,which can often be sub-optimal. In this work,we propose a different approach,introducing a semi-automated pipeline to select interactively features for WMH classification. The advantage of this solution is the integration of the knowledge and skills of experts in the process. In our pipeline,a Visual Analytics (VA) system is employed,to enable user-driven feature selection. The resulting features are T1,FLAIR,Mean Diffusivity (MD),and Radial Diffusivity (RD) – and secondarily,CS and Fractional Anisotropy (FA). The next step in the pipeline is to train a classifier with these features,and compare its results to a similar classifier,used in previous work with automated feature selection. Finally,VA is employed again,to analyze and understand the classifier performance and results

The relationship between retinal microvascular abnormalities, brain damage and cognitive dysfunction

Doel Het onderzoeken van de relatie tussen vasculaire af wijkingen in de retina, af wijkingen op hersenscans en cognitievedysfunctie, in de context van veroudering en verhoogd cardiovasculair risico. Opzet Systematische review.Methode In Medline werd een literatuursearch gedaan naar studies gepubliceerd in de periode 1990-2012, over de associatietussen retinale vaataf wijkingen, vasculaire af wijkingen en atrof ie op hersenscans, cognitieve stoornissen endementie.Resultaten In totaal werden 32 studies geïncludeerd. In transversale studies was retinale vaatschade geassocieerd met vasculaireaf wijkingen en atrof ie op hersenscans (oddsratio’s (OR): 0,9-3,0), verminderd cognitief func tioneren en dementie(OR: 1,1-5,5). In longitudinale studies werd geen duidelijk verband gevonden met cognitief func tioneren of dementie, maar er waren wel aanwijzingen dat retinale vaatschade was geassocieerd met af wijkingen op hersenscans(OR: 0,8-3,2).Conclusie Retinale vaatafwijkingen zijn geassocieerd met vasculaire laesies en atrofie op hersenscans, en met cognitieve disfunc tie en dementie, vooral wanneer de retinale vaatschade uitgesproken is. Dit onderstreept het etiologischbelang van vaatziekten bij het ontstaan van cognitieve stoornissen en dementie. Onderzoek van de retina lijkt geenef fec tief screeningsinstrument om personen met een verhoogd risico op cognitieve stoornissen of dementie op tesporen