Driving stroke quality improvement at scale in EDs across a nationwide network of hospitals: strategies and interventions.

OBJECTIVES: Reducing the treatment time while increasing the proportion of eligible stroke patients who receive intravenous tissue plasminogen activator (tPA) has been a priority for many quality improvement efforts. Recent studies have primarily focused on identifying interventions that reduce door-to-needle (DTN) time, while comparatively little has been done to determine whether these interventions also improve tPA rates. METHODS: In order to investigate interventions related to process improvements, an electronic dashboard serving as a stroke performance tool was implemented to store and retrieve patient outcome data. These data were used to study the efficacy of interventions designed to facilitate triage of stroke patients in the ED, and determine the individual interventions associated with the most significant improvements in the fraction of patients receiving tPA and in reducing the DTN time. Stroke performance data from the dashboard collected over a 2-year period (2015-2017) from 89 US hospitals were analysed with respect to interventions implemented by individual facilities, as verified by a hospital survey. RESULTS: A statistically significant association was found between increases in the fraction of patients receiving tPA and reductions in DTN time over the study period. These improvements in outcomes were most strongly associated with process interventions that allocate stroke-specific physical and human resources in the ED, most notably a designated emergency room space for stroke, and with workflows that decrease the time to key checkpoints for determining a patient's eligibility for tPA. CONCLUSIONS: Data from the stroke performance tool was leveraged to identify the programmes and process interventions that lead to improved patient outcomes and allow EDs to better prioritise process interventions and resources.

Higher Incidence of Ischemic Stroke in Patients Taking Novel Oral Anticoagulants.

Background and Purpose- The increased use of novel oral anticoagulants (NOACs) to control atrial fibrillation is largely driven by the assumption that they are equally effective as warfarin at preventing ischemic stroke while putting patients at lower risk of hemorrhages. To test this hypothesis, a retrospective study of the relative incidence of strokes among patients taking NOACs versus those taking warfarin is performed. Methods- Relative stroke incidence in the 2 groups of patients was compared using odds ratios and Fisher exact tests for significance using a data set of 71 365 on NOACs and 59 546 patients on warfarin. In addition, the 7033 patients with a record of both warfarin and NOAC use were analyzed as a separate cohort. Results- There is a significantly higher (odds ratio=1.29, <0.001) frequency of ischemic strokes among patients prescribed NOACs compared with those on warfarin. The relative frequency of ischemic strokes was also higher for every individual NOAC compared with warfarin (these higher frequencies are statistically significant for dabigatran and apixaban, though not for edoxaban and rivaroxaban). There is a lower incidence of intracranial hemorrhages and nontraumatic hemorrhages in general among patients taking NOACs, consistent with the published literature. Comparisons of the demographic and clinical profiles of the patients taking NOACs to those on warfarin do not show significantly higher background stroke risk in NOAC patients; in fact, patients on NOACs tend to be at lower background risk overall for ischemic strokes. Conclusions- Because NOAC use is associated with higher ischemic stroke risk together with a lower risk of hemorrhages than warfarin use, it can be concluded that patients on warfarin are more strongly anticoagulated. The observed effect could be a secondary consequence of dosage control or alternatively a result of different anticoagulant effects among the different medications.

Rapid and convergent evolution in the glioblastoma multiforme genome.

Determining which mutations drive tumor progression is a defining question in cancer genomics. We analyzed sequence evolution in Glioblastoma multiforme (GBM) by computing the number of parallel mutations and by estimating ω=dN/dS, a measure of the strength and direction of selection. The ω values of almost all 7617 mutated genes in GBM are much higher than in germline genes. We identified only 21 genes under significant positive selection in GBM, as well as 29 genes under significant purifying selection, including several zinc finger proteins. Therefore, most of the high ω values in the GBM genome are due to weaker purifying selection rather than positive selection. We also found multiple recurrent mutations in GBM, several of which are associated with patient survival time. Our results suggest that convergence and neutral evolution play a significant role in GBM, and that sites with recurrent mutations can serve as molecular diagnostics of the clinical course of GBM tumors.

An eQTL analysis of the human glioblastoma multiforme genome.

In this paper we use eQTL mapping to identify associations between gene dysregulation and single nucleotide polymorphism (SNP) genotypes in glioblastoma multiforme (GBM). A set of 532,954 SNPs was evaluated as predictors of the expression levels of 22,279 expression probes. We identified SNPs associated with fold change in expression level rather than raw expression levels in the tumor. Following adjustment for false discovery rate, the complete set of probes yielded 9257 significant associations (p<0.05). We found 18 eQTLs that were missense mutations. Many of the eQTLs in the non-coding regions of a gene, or linked to nearby genes, had large numbers of significant associations (e.g. 321 for RNASE3, 101 for BNC2). Functional enrichment analysis revealed that the expression probes in significant associations were involved in signal transduction, transcription regulation, membrane function, and cell cycle regulation. These results suggest several loci that may serve as hubs in gene regulatory pathways associated with GBM.

Genome-wide association studies: a powerful tool for neurogenomics.

As their power and utility increase, genome-wide association (GWA) studies are poised to become an important element of the neurosurgeon's toolkit for diagnosing and treating disease. In this paper, the authors review recent findings and discuss issues associated with gathering and analyzing GWA data for the study of neurological diseases and disorders, including those of neurosurgical importance. Their goal is to provide neurosurgeons and other clinicians with a better understanding of the practical and theoretical issues associated with this line of research. A modern GWA study involves testing hundreds of thousands of genetic markers across an entire genome, often in thousands of individuals, for any significant association with a particular disease. The number of markers assayed in a study presents several practical and theoretical issues that must be considered when planning the study. Genome-wide association studies show great promise in our understanding of the genes underlying common neurological diseases and disorders, as well as in leading to a new generation of genetic tests for clinicians.

Supporting Breastfeeding in Infants Hospitalized for Jaundice.

BACKGROUND: Infants readmitted for neonatal hyperbilirubinemia requiring phototherapy are less likely to exclusively breastfeed than infants who are not readmitted for phototherapy. Our aim for this study was to increase breastfeeding exclusivity for infants admitted for neonatal hyperbilirubinemia. METHODS: Using the Model for Improvement, we conducted 3 plan-do-study-act cycles to improve exclusive breastfeeding (EB). Our outcome measure was to increase the percentage of EB for infants hospitalized for phototherapy from 30% to 80% in 12 months. Our process measure was to increase lactation consultations from 60% to 80%. Balancing measures included the development of >10% weight loss, acute bilirubin encephalopathy, readmissions, and length of stay. Interventions involved staff breastfeeding education, automated orders for lactation consultations, and use of bilirubin blankets during breastfeeding. Data were analyzed by using run charts and statistical process control. RESULTS: A total of 92 infants with neonatal hyperbilirubinemia were admitted from December 2016 to August 2019, with 61 in the postintervention period. After implementation of an automated order for lactation consultation, EB improved from 30% to 60% and completed lactation consultations increased from 60% to 90%. Infants who received a lactation consultation within the first shift during their hospitalization were 4 times more likely to have EB during hospitalization than infants who did not (odds ratio 3.8; confidence interval: 1.17-12.39.) No infant experienced >10% weight loss, acute bilirubin encephalopathy, or a readmission, and length of stay did not significantly change. CONCLUSIONS: Early involvement of trained lactation consultants safely improves rates of EB for infants hospitalized with neonatal hyperbilirubinemia.

The ascent of the abundant: how mutational networks constrain evolution.

Evolution by natural selection is fundamentally shaped by the fitness landscapes in which it occurs. Yet fitness landscapes are vast and complex, and thus we know relatively little about the long-range constraints they impose on evolutionary dynamics. Here, we exhaustively survey the structural landscapes of RNA molecules of lengths 12 to 18 nucleotides, and develop a network model to describe the relationship between sequence and structure. We find that phenotype abundance--the number of genotypes producing a particular phenotype--varies in a predictable manner and critically influences evolutionary dynamics. A study of naturally occurring functional RNA molecules using a new structural statistic suggests that these molecules are biased toward abundant phenotypes. This supports an "ascent of the abundant" hypothesis, in which evolution yields abundant phenotypes even when they are not the most fit.

Discovery of stroke-related blood biomarkers from gene expression network models.

BACKGROUND: Identifying molecular biomarkers characteristic of ischemic stroke has the potential to aid in distinguishing stroke cases from stroke mimicking symptoms, as well as advancing the understanding of the physiological changes that underlie the body's response to stroke. This study uses machine learning-based analysis of gene co-expression to identify transcription patterns characteristic of patients with acute ischemic stroke. METHODS: Mutual information values for the expression levels among 13,243 quantified transcripts were computed for blood samples from 82 stroke patients and 68 controls to construct a co-expression network of genes (separately) for stroke and control samples. Page rank centrality scores were computed for every gene; a gene's significance in the network was assessed according to the differences in their network's pagerank centrality between stroke and control expression patterns. A hybrid genetic algorithm - support vector machine learning tool was used to classify samples based on gene centrality in order to identify an optimal set of predictor genes for stroke while minimizing the number of genes in the model. RESULTS: A predictive model with 89.6% accuracy was identified using 6 network-central and differentially expressed genes (ID3, MBTPS1, NOG, SFXN2, BMX, SLC22A1), characterized by large differences in association network connectivity between stroke and control samples. In contrast, classification models based solely on individual genes identified by significant fold-changes in expression level provided lower predictive accuracies: < 71% for any single gene, and even models with larger (10-25) numbers of gene transcript biomarkers gave lower predictive accuracies (≤ 82%) than the 6 network-based gene signature classification. miRNA:mRNA target prediction computational analysis revealed 8 differentially expressed micro-RNAs (miRNAs) that are significantly associated with at least 2 of the 6 network-central genes. CONCLUSIONS: Network-based models have the potential to identify a more statistically robust pattern of gene expression typical of acute ischemic stroke and to generate hypotheses about possible interactions among functionally relevant genes, leading to the identification of more informative biomarkers.

The Global academic research organization network: Data sharing to cure diseases and enable learning health systems.

INTRODUCTION: Global data sharing is essential. This is the premise of the Academic Research Organization (ARO) Council, which was initiated in Japan in 2013 and has since been expanding throughout Asia and into Europe and the United States. The volume of data is growing exponentially, providing not only challenges but also the clear opportunity to understand and treat diseases in ways not previously considered. Harnessing the knowledge within the data in a successful way can provide researchers and clinicians with new ideas for therapies while avoiding repeats of failed experiments. This knowledge transfer from research into clinical care is at the heart of a learning health system. METHODS: The ARO Council wishes to form a worldwide complementary system for the benefit of all patients and investigators, catalyzing more efficient and innovative medical research processes. Thus, they have organized Global ARO Network Workshops to bring interested parties together, focusing on the aspects necessary to make such a global effort successful. One such workshop was held in Austin, Texas, in November 2017. Representatives from Japan, Taiwan, Singapore, Europe, and the United States reported on their efforts to encourage data sharing and to use research to inform care through learning health systems. RESULTS: This experience report summarizes presentations and discussions at the Global ARO Network Workshop held in November 2017 in Austin, TX, with representatives from Japan, Korea, Singapore, Taiwan, Europe, and the United States. Themes and recommendations to progress their efforts are explored. Standardization and harmonization are at the heart of these discussions to enable data sharing. In addition, the transformation of clinical research processes through disruptive innovation, while ensuring integrity and ethics, will be key to achieving the ARO Council goal to overcome diseases such that people not only live longer but also are healthier and happier as they age. CONCLUSIONS: The achievement of global learning health systems will require further exploration, consensus-building, funding aligned with incentives for data sharing, standardization, harmonization, and actions that support global interests for the benefit of patients.

Bivalent Chromatin Domains in Glioblastoma Reveal a Subtype-Specific Signature of Glioma Stem Cells.

Glioblastoma multiforme (GBM) can be clustered by gene expression into four main subtypes associated with prognosis and survival, but enhancers and other gene-regulatory elements have not yet been identified in primary tumors. Here, we profiled six histone modifications and CTCF binding as well as gene expression in primary gliomas and identified chromatin states that define distinct regulatory elements across the tumor genome. Enhancers in mesenchymal and classical tumor subtypes drove gene expression associated with cell migration and invasion, whereas enhancers in proneural tumors controlled genes associated with a less aggressive phenotype in GBM. We identified bivalent domains marked by activating and repressive chromatin modifications. Interestingly, the gene interaction network from common (subtype-independent) bivalent domains was highly enriched for homeobox genes and transcription factors and dominated by SHH and Wnt signaling pathways. This subtype-independent signature of early neural development may be indicative of poised dedifferentiation capacity in glioblastoma and could provide potential targets for therapy.Significance: Enhancers and bivalent domains in glioblastoma are regulated in a subtype-specific manner that resembles gene regulation in glioma stem cells. Cancer Res; 78(10); 2463-74. ©2018 AACR.

From bad to good: Fitness reversals and the ascent of deleterious mutations.

Deleterious mutations are considered a major impediment to adaptation, and there are straightforward expectations for the rate at which they accumulate as a function of population size and mutation rate. In a simulation model of an evolving population of asexually replicating RNA molecules, initially deleterious mutations accumulated at rates nearly equal to that of initially beneficial mutations, without impeding evolutionary progress. As the mutation rate was increased within a moderate range, deleterious mutation accumulation and mean fitness improvement both increased. The fixation rates were higher than predicted by many population-genetic models. This seemingly paradoxical result was resolved in part by the observation that, during the time to fixation, the selection coefficient (s) of initially deleterious mutations reversed to confer a selective advantage. Significantly, more than half of the fixations of initially deleterious mutations involved fitness reversals. These fitness reversals had a substantial effect on the total fitness of the genome and thus contributed to its success in the population. Despite the relative importance of fitness reversals, however, the probabilities of fixation for both initially beneficial and initially deleterious mutations were exceedingly small (on the order of 10(-5) of all mutations).

One-year outcomes of early-crossover patients in a cohort receiving nonoperative care for lumbar disc herniation.

OBJECTIVE The authors comprehensively studied the recovery course and 1-year outcomes of early-crossover patients who were randomized to the nonoperative care arm of the Leiden-The Hague Spine Intervention Prognostic Study. The primary goal was to gain insight into the differences in the recovery patterns of early-crossover patients and those treated nonoperatively; secondary goals were to identify predictors of good 1-year outcomes, and to understand when and why patients were likely to cross over. METHODS Individual EuroQol-5D scores were obtained at baseline and at 2, 4, 8, 12, 26, 38, and 52 weeks for 142 patients. Early-crossover patients were defined as those electing to undergo surgery during the first 12 weeks of treatment. Crossover and noncrossover groups were compared using Kruskal-Wallis, Wilcoxon-Mann-Whitney, and chi-square tests. Linear mixed-effects models were used to examine the growth trajectories of crossover and noncrossover groups. Recursive partitioning trees were used to model crossover events and the timing of crossover decisions. Multivariable logistic regression models were used to identify predictors of good 1-year outcomes. RESULTS Of the 142 patients randomized to receive prolonged nonoperative care, 136 were selected for the study. In this cohort, 43/136 (32%) opted for surgery, and 31/43 (72%) of crossover events occurred before the 12-week time point. Early-crossover patients had significantly greater functional impairment at Week 2 than noncrossover patients (p = 0.031), but experienced greater recovery by 26 weeks and better 1-year outcomes (p = 0.045). Patients who did not experience an improvement in their symptoms between 2 and 8 weeks were more likely to cross over (OR 3.5, 95% CI 1.2-10.1; p = 0.01). Recursive partitioning trees were able to identify crossover patients with 76% accuracy. Regression models suggested that better recovery at 26 weeks (p < 0.01) was predictive of good 1-year outcome; declining health status between Weeks 4 and 8 was negatively predictive of good outcome (p < 0.01). CONCLUSIONS This study is the first to comprehensively analyze the recovery and outcomes of crossover patients, and compare them to nonoperatively treated patients. The results suggest that patients who have a low EuroQol-5D score during the early weeks of treatment and who do not respond to nonoperative care during the first few weeks of treatment are most likely to cross over. Early-crossover patients experience a greater rate of recovery and more frequently have a good 1-year outcome when compared with nonoperatively treated patients. The current results motivate a broader investigation into the timing of surgery and the identification of patient populations that will be most benefited by early surgical treatment for lumbar disc herniation.

Distributions of beneficial fitness effects in RNA.

Beneficial mutations are the driving force of evolution by natural selection. Yet, relatively little is known about the distribution of the fitness effects of beneficial mutations in populations. Recent work of Gillespie and Orr suggested some of the first generalizations for the distributions of beneficial fitness effects and, surprisingly, they depend only weakly on biological details. In particular, the theory suggests that beneficial mutations obey an exponential distribution of fitness effects, with the same exponential parameter across different regions of genotype space, provided only that few possible beneficial mutations are available to that genotype. Here we tested this hypothesis with a quasi-empirical model of RNA evolution in which fitness is based on the secondary structures of molecules and their thermodynamic stabilities. The fitnesses of randomly selected genotypes appeared to follow a Gumbel-type distribution and thus conform to a basic assumption of adaptation theory. However, the observed distributions of beneficial fitness effects conflict with specific predictions of the theory. In particular, the distributions of beneficial fitness effects appeared exponential only when the vast majority of small-effect beneficial mutations were ignored. Additionally, the distribution of beneficial fitness effects varied with the fitness of the parent genotype. We believe that correlation of the fitness values among similar genotypes is likely the cause of the departure from the predictions of recent adaptation theory. Although in conflict with the current theory, these results suggest that more complex statistical generalizations about beneficial mutations may be possible.

Reproducibility of SNV-calling in multiple sequencing runs from single tumors.

We examined 55 technical sequencing replicates of Glioblastoma multiforme (GBM) tumors from The Cancer Genome Atlas (TCGA) to ascertain the degree of repeatability in calling single-nucleotide variants (SNVs). We used the same mutation-calling pipeline on all pairs of samples, and we measured the extent of the overlap between two replicates; that is, how many specific point mutations were found in both replicates. We further tested whether additional filtering increased or decreased the size of the overlap. We found that about half of the putative mutations identified in one sequencing run of a given sample were also identified in the second, and that this percentage remained steady throughout orders of magnitude of variation in the total number of mutations identified (from 23 to 10,966). We further found that using filtering after SNV-calling removed the overlap completely. We concluded that there is variation in the frequency of mutations in GBMs, and that while some filtering approaches preferentially removed putative mutations found in only one replicate, others removed a large fraction of putative mutations found in both.

Molecular Predictors of Long-Term Survival in Glioblastoma Multiforme Patients.

Glioblastoma multiforme (GBM) is the most common and aggressive adult primary brain cancer, with <10% of patients surviving for more than 3 years. Demographic and clinical factors (e.g. age) and individual molecular biomarkers have been associated with prolonged survival in GBM patients. However, comprehensive systems-level analyses of molecular profiles associated with long-term survival (LTS) in GBM patients are still lacking. We present an integrative study of molecular data and clinical variables in these long-term survivors (LTSs, patients surviving >3 years) to identify biomarkers associated with prolonged survival, and to assess the possible similarity of molecular characteristics between LGG and LTS GBM. We analyzed the relationship between multivariable molecular data and LTS in GBM patients from the Cancer Genome Atlas (TCGA), including germline and somatic point mutation, gene expression, DNA methylation, copy number variation (CNV) and microRNA (miRNA) expression using logistic regression models. The molecular relationship between GBM LTS and LGG tumors was examined through cluster analysis. We identified 13, 94, 43, 29, and 1 significant predictors of LTS using Lasso logistic regression from the somatic point mutation, gene expression, DNA methylation, CNV, and miRNA expression data sets, respectively. Individually, DNA methylation provided the best prediction performance (AUC = 0.84). Combining multiple classes of molecular data into joint regression models did not improve prediction accuracy, but did identify additional genes that were not significantly predictive in individual models. PCA and clustering analyses showed that GBM LTS typically had gene expression profiles similar to non-LTS GBM. Furthermore, cluster analysis did not identify a close affinity between LTS GBM and LGG, nor did we find a significant association between LTS and secondary GBM. The absence of unique LTS profiles and the lack of similarity between LTS GBM and LGG, indicates that there are multiple genetic and epigenetic pathways to LTS in GBM patients.

Cilia gene expression patterns in cancer.

Non-motile cilia are thought to be important determinants of the progression of many types of cancers. Our goal was to identify patterns of cilia gene dysregulation in eight cancer types (glioblastoma multiforme, colon adenocarcinoma, breast adenocarcinoma, kidney renal clear cell carcinoma, lung squamous cell carcinoma, lung adenocarcinoma, rectal adenocarcinoma, and ovarian cancer) profiled by The Cancer Genome Atlas. Among these types, 2.5-19.8% of cilia-associated genes were significantly differentially expressed (versus 5.5-32.4% dysregulation across all genes). In four cancer types (breast adenocarcinoma, colon adenocarcinoma, glioblastoma multiforme, and ovarian cancer), cilia genes were on average down-regulated (median fold change from -1.53--0.3), in the other four types, cilia genes were up-regulated (fold change=0.86-3.5). Pairwise comparisons between cancer types revealed varying degrees of similarity in the differentially expressed cilia genes, ranging from 7.1% (ovarian cancer and lung squamous cell carcinoma) to 65.8% (ovarian cancer and rectal adenocarcinoma). Hierarchical clustering and principal components analysis of gene expression identified glioblastoma multiforme, colon adenocarcinoma, breast adenocarcinoma; and kidney renal clear cell carcinoma, lung squamous cell carcinoma, lung adenocarcinoma, rectal adenocarcinoma, and ovarian cancer as sub-classes with similar dysregulation patterns. Our study suggests that genes involved in cilia biosynthesis and function are frequently dysregulated in cancer, and may be useful for identifying and classifying cancer types.

Effects of genetic variation on the dynamics of neurodegeneration in Alzheimer's disease.

Although many genetic markers are identified as being associated with Alzheimer's disease (AD), not much is known about their association with the structural changes that happen as the disease progresses. In this study, we investigate the genetic etiology of neurodegeneration in AD by associating genetic markers with atrophy profiles obtained using patient data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort. The atrophy profiles were quantified using a linear least-squares regression model over the span of patient enrollment, and used as imaging features throughout the analysis. A subset of the imaging features were selected for genetic association based on their ability to discriminate between healthy individuals and AD patients in a Support Vector Machines (SVM) classifier. Each imaging feature was associated with single-nucleotide polymorphisms (SNPs) using a linear model that included age and cognitive impairment scores as covariates to correct for normal disease progression. After false discovery rate correction, we observed 53 significant associations between SNPs and our imaging features, including associations of ventricular enlargement with SNPs on estrogen receptor 1 (ESR1) and sortilin-related VPS10 domain containing receptor 1 (SORCS1), hippocampal atrophy with SNPs on ESR1, and cerebral atrophy with SNPs on transferrin (TF) and amyloid beta precursor protein (APP). This study provides important insights into genetic predictors of specific types of neurodegeneration that could potentially be used to improve the efficacy of treatment strategies for the disease and allow the development of personalized treatment plans based on each patient's unique genetic profile.

Three-dimensional brain magnetic resonance imaging segmentation via knowledge-driven decision theory.

Brain tissue segmentation on magnetic resonance (MR) imaging is a difficult task because of significant intensity overlap between the tissue classes. We present a new knowledge-driven decision theory (KDT) approach that incorporates prior information of the relative extents of intensity overlap between tissue class pairs for volumetric MR tissue segmentation. The proposed approach better handles intensity overlap between tissues without explicitly employing methods for removal of MR image corruptions (such as bias field). Adaptive tissue class priors are employed that combine probabilistic atlas maps with spatial contextual information obtained from Markov random fields to guide tissue segmentation. The energy function is minimized using a variational level-set-based framework, which has shown great promise for MR image analysis. We evaluate the proposed method on two well-established real MR datasets with expert ground-truth segmentations and compare our approach against existing segmentation methods. KDT has low-computational complexity and shows better segmentation performance than other segmentation methods evaluated using these MR datasets.

Superpixels in brain MR image analysis.

A large number of sophisticated techniques have been proposed over the last few decades for automatic analysis of brain MR images to help clinicians better diagnose and understand anatomical changes due to neurological disorders. While significant improvements in performance have been achieved, almost all techniques suffer from a common limitation of high computational complexity due to the large number of voxels present in a typical MR volume. Computational complexity is a major hurdle in the clinical application of these sophisticated image analysis techniques. Brain MR volumes consist of approximately piecewise constant tissue regions with high redundancy among voxel intensities, which can be grouped into perceptually meaningful entities (superpixels) to reduce the complexity. In this study, we investigate the utility of superpixels (2D) and supervoxels (3D) in reducing computational complexity of brain MR analysis tasks. We investigate the extent of spatial and intensity distortions introduced in superpixel representation of MR images and evaluate its effect on brain tissue segmentation as an example task. We observe that superpixels are highly promising for significantly reducing the computational complexity of the lower-level image analysis tasks that are often essential components of MR analysis pipelines.