Order selection for heterogeneous semiparametric hidden Markov models.

Hidden Markov models (HMMs), which can characterize dynamic heterogeneity, are valuable tools for analyzing longitudinal data. The order of HMMs (ie, the number of hidden states) is typically assumed to be known or predetermined by some model selection criterion in conventional analysis. As prior information about the order frequently lacks, pairwise comparisons under criterion-based methods become computationally expensive with the model space growing. A few studies have conducted order selection and parameter estimation simultaneously, but they only considered homogeneous parametric instances. This study proposes a Bayesian double penalization (BDP) procedure for simultaneous order selection and parameter estimation of heterogeneous semiparametric HMMs. To overcome the difficulties in updating the order, we create a brand-new Markov chain Monte Carlo algorithm coupled with an effective adjust-bound reversible jump strategy. Simulation results reveal that the proposed BDP procedure performs well in estimation and works noticeably better than the conventional criterion-based approaches. Application of the suggested method to the Alzheimer's Disease Neuroimaging Initiative research further supports its usefulness.

Increasing Utilization of Emergency Department Neuroimaging From 2007 Through 2017.

BACKGROUND. The volume of emergency department (ED) visits and the number of neuroimaging examinations have increased since the start of the century. Little is known about this growth in the commercially insured and Medicare Advantage populations. OBJECTIVE. The purpose of our study was to evaluate changing ED utilization of neuroimaging from 2007 through 2017 in both commercially insured and Medicare Advantage enrollees. METHODS. Using patient-level claims from Optum's deidentified Clinformatics Data Mart database, which annually includes approximately 12-14 million commercial and Medicare Advantage health plan enrollees, annual ED utilization rates of head CT, head MRI, head CTA, neck CTA, head MRA, neck MRA, and carotid duplex ultrasound (US) were assessed from 2007 through 2017. To account for an aging sample population, utilization rates were adjusted using annual relative proportions of age groups and stratified by patient demographics, payer type, and provider state. RESULTS. Between 2007 and 2017, age-adjusted ED neuroimaging utilization rates per 1000 ED visits increased 72% overall (compound annual growth rate [CAGR], 5%). This overall increase corresponded to an increase of 69% for head CT (CAGR, 5%), 67% for head MRI (CAGR, 5%), 1100% for head CTA (CAGR, 25%), 1300% for neck CTA (CAGR, 27%), 36% for head MRA (CAGR, 3%), and 52% for neck MRA (CAGR, 4%) and to a decrease of 8% for carotid duplex US (CAGR, -1%). The utilization of head CT and CTA of the head and neck per 1000 ED visits increased in enrollees 65 years old or older by 48% (CAGR, 4%) and 1011% (CAGR, 24%). CONCLUSION. Neuroimaging utilization in the ED grew considerably between 2007 and 2017, with growth of head and neck CTA far outpacing the growth of other modalities. Unenhanced head CT remains by far the dominant ED neuroimaging examination. CLINICAL IMPACT. The rapid growth of head and neck CTA observed in the fee-for-service Medicare population is also observed in the commercially insured and Medicare Advantage populations. The appropriateness of this growth should be monitored as the indications for CTA expand.

Denoising Diffusion MRI via Graph Total Variance in Spatioangular Domain.

Diffusion MRI (DMRI) plays an essential role in diagnosing brain disorders related to white matter abnormalities. However, it suffers from heavy noise, which restricts its quantitative analysis. The total variance (TV) regularization is an effective noise reduction technique that penalizes noise-induced variances. However, existing TV-based denoising methods only focus on the spatial domain, overlooking that DMRI data lives in a combined spatioangular domain. It eventually results in an unsatisfactory noise reduction effect. To resolve this issue, we propose to remove the noise in DMRI using graph total variance (GTV) in the spatioangular domain. Expressly, we first represent the DMRI data using a graph, which encodes the geometric information of sampling points in the spatioangular domain. We then perform effective noise reduction using the powerful GTV regularization, which penalizes the noise-induced variances on the graph. GTV effectively resolves the limitation in existing methods, which only rely on spatial information for removing the noise. Extensive experiments on synthetic and real DMRI data demonstrate that GTV can remove the noise effectively and outperforms state-of-the-art methods.

Neurosurgery in Ethiopia: A New Chapter and Future Prospects.

BACKGROUND: Inequitable access to surgical care is most conspicuous in low-income countries (LICs), such as Ethiopia, where infectious diseases, malnutrition, and other maladies consume the lion's share of the available health resources. The aim of this article was to provide an update on the current state of neurosurgery in Ethiopia and identify targets for future development of surgical capacity as a universal health coverage component in this East African nation. METHODS: Publicly available data included in this report were gathered from resources published by international organizations. A PubMed search was used for a preliminary bibliometric analysis of scholarly output of neurosurgeons in Ethiopia and other low-income countries. Statistical analysis was used to determine the correlation between the number of neurosurgeons and academic productivity. RESULTS: Neurosurgeon density has increased >20-fold from 0.0022 to 0.045 neurosurgeons per 100,000 population between 2006 and 2020. The increase in neurosurgeons was strongly correlated with an increase in total publications (P < 0.001) and the number of new publications per year (P = 0.003). Despite recent progress, the availability of neuroimaging equipment remains inadequate, with 38 computed tomography scanners and 11 magnetic resonance imaging machines for a population of 112.07 million. The geographic distribution of neurosurgical facilities is limited to 12 urban centers. CONCLUSIONS: Ethiopian neurosurgery exemplifies the profound effect of international partnerships for training local surgeons on progress in low-income countries toward improved neurosurgical capacity. Collaborations that focus on increasing the neurosurgical workforce should synchronize with efforts to enhance the availability of diagnostic and surgical equipment necessary for basic neurosurgical care.

Increasing and disparate use of neuroimaging for adults and children with non-traumatic headaches in the US emergency departments: Opportunities for improvement.

BACKGROUND: Optimization of neuroimaging practices for headache is considered a national priority; however, nationwide patterns and predictors of neuroimaging use for headache in the US emergency departments (EDs) are unknown. OBJECTIVE: To analyze temporal neuroimaging utilization trends for adults and children with non-traumatic headache in the US EDs and identify factors predictive of neuroimaging use in this patient population. METHODS: Retrospective cross-sectional study using the Healthcare Cost and Utilization Project Nationwide Emergency Department Sample database for administrative encounter-level data analysis of a nationwide group of adult and pediatric patients with primary diagnosis of headache (ICD-9CM codes 784.0x, 339.xx, 346.xx) visited the US EDs between January 1, 2006 and December 31, 2014. Temporal trends and independent predictors of neuroimaging use (e.g., patient and hospital characteristics, primary payment sources) were determined. RESULTS: In 2006-2014, a weighted group of 18,146,302 patients with a primary diagnosis of non-traumatic headache visited US EDs. Advanced neuroimaging utilization increased from 18.6% (n = 350,777) to 34.8% (n = 756,895) in the total group, from 18.8% (n = 314,646) to 36.5% (n = 698,080) in the adult subgroup (+94.1%), and from 16.9% (n = 36,131) to 22.0% (n = 58,815) (+30.2%) in the pediatric subgroup (+87.0%) between 2006 and 2014. The strongest predictors of higher neuroimaging utilization were hospital location in the Northeast (OR 3.17, 95% CI 2.67-3.76) or South (OR 2.42, 95% CI 2.03-2.88) regions. Lower utilization of imaging was associated with weekend ED visits (OR 0.92, 95% CI 0.92-0.93), female gender (OR 0.82, 95% CI 0.81-0.83), and Medicare, Medicaid, or self-pay (vs. private insurance) encounters. CONCLUSION: Neuroimaging utilization in patients with headache in US EDs nearly doubled in 2006-2014, and was used in 34.8% of all ED encounters in 2014. Utilization was higher and increased at faster rates for adults than children. In US EDs, imaging for headache is preferentially performed on commercially insured and male patients, at urban hospitals, in certain geographic regions, and on weekdays, raising concerns regarding disparate imaging use.

Patients With Acute Ischemic Stroke Who Receive Brain Magnetic Resonance Imaging Demonstrate Favorable In-Hospital Outcomes.

Background Use of inpatient brain magnetic resonance imaging (MRI) in patients with acute ischemic stroke is highly institution dependent and has been associated with increased length and cost of hospital stay. We examined whether inpatient brain MRI in patients with acute ischemic stroke is associated with improved clinical outcomes to justify its resource requirements. Methods and Results The National Inpatient Sample database was queried retrospectively to find 94 003 patients who were admitted for acute ischemic stroke and then received inpatient brain MRI between 2012 and 2014. Multivariable regression analysis was performed with respect to a control group to assess for differences in the rates of inpatient mortality and complications, as well as the length and cost of hospital stay based on brain MRI use. Inpatient brain MRI was independently associated with lower rates of inpatient mortality (1.67% versus 3.09%; adjusted odds ratio [OR], 0.60; 95% CI, 0.53-0.68; P<0.001), gastrostomy (2.28% versus 2.89%; adjusted OR, 0.82; 95% CI, 0.73-0.93; P<0.001), and mechanical ventilation (1.97% versus 2.82%; adjusted OR, 0.68; 95% CI, 0.60-0.77; P<0.001). Brain MRI was independently associated with ≈0.32 days (8%) and $1131 (11%) increase in the total length (P<0.001) and cost (P<0.001) of hospital stay, respectively. Conclusions Inpatient brain MRI in patients with acute ischemic stroke is associated with substantial decrease in the rates of inpatient mortality and complications, at the expense of marginally increased length and cost of hospitalization.

Acute Stroke Care Is at Risk in the Era of COVID-19: Experience at a Comprehensive Stroke Center in Barcelona.

BACKGROUND AND PURPOSE: The purpose of the study is to analyze how the coronavirus disease 2019 (COVID-19) pandemic affected acute stroke care in a Comprehensive Stroke Center. METHODS: On February 28, 2020, contingency plans were implemented at Hospital Clinic of Barcelona to contain the COVID-19 pandemic. Among them, the decision to refrain from reallocating the Stroke Team and Stroke Unit to the care of patients with COVID-19. From March 1 to March 31, 2020, we measured the number of emergency calls to the Emergency Medical System in Catalonia (7.5 million inhabitants), and the Stroke Codes dispatched to Hospital Clinic of Barcelona. We recorded all stroke admissions, and the adequacy of acute care measures, including the number of thrombectomies, workflow metrics, angiographic results, and clinical outcomes. Data were compared with March 2019 using parametric or nonparametric methods as appropriate. RESULTS: At Hospital Clinic of Barcelona, 1232 patients with COVID-19 were admitted in March 2020, demanding 60% of the hospital bed capacity. Relative to March 2019, the Emergency Medical System had a 330% mean increment in the number of calls (158 005 versus 679 569), but fewer Stroke Code activations (517 versus 426). Stroke admissions (108 versus 83) and the number of thrombectomies (21 versus 16) declined at Hospital Clinic of Barcelona, particularly after lockdown of the population. Younger age was found in stroke admissions during the pandemic (median [interquartile range] 69 [64-73] versus 75 [73-80] years, P=0.009). In-hospital, there were no differences in workflow metrics, angiographic results, complications, or outcomes at discharge. CONCLUSIONS: The COVID-19 pandemic reduced by a quarter the stroke admissions and thrombectomies performed at a Comprehensive Stroke Center but did not affect the quality of care metrics. During the lockdown, there was an overload of emergency calls but fewer Stroke Code activations, particularly in elderly patients. Hospital contingency plans, patient transport systems, and population-targeted alerts must act concertedly to better protect the chain of stroke care in times of pandemic.

An Assessment of the Relationship between Structural and Functional Imaging of Cerebrovascular Disease and Cognition-Related Fibers.

In order to assess the relationship between structural and functional imaging of cerebrovascular disease and cognition-related fibers, this paper chooses a total of 120 patients who underwent cerebral small vessel disease (CSVD) treatment at a designated hospital by this study from June 2013 to June 2018 and divides them into 3 groups according to the random number table method: vascular dementia (VaD) group, vascular cognitive impairment no dementia (VCIND) group, and noncognition impairment (NCI) group with 40 cases of patients in each group. Cognitive function measurement and imaging examination were performed for these 3 groups of patients, and the observation indicators of cognitive state examination (CSE), mental assessment scale (MAS), clock drawing test (CDT), adult intelligence scale (AIS), frontal assessment battery (FAB), verbal fluency test (VFT), trail making test (TMT), cognitive index (CI), white matter lesions (WML), third ventricle width (TVW), and frontal horn index (FHI) were tested, respectively. The results shows that the average scores of CSE, MAS, AIS, and VFT in the VaD and VCIND group are lower than those of the NCI group and the differences are statistically significant (P < 0.05); the average scores of FAB, TMT, and CI in the VaD group are higher than those of the VCIND group and the differences are also statistically significant (P < 0.05); the average scores of FHI and TVW in the VaD group are lower than those of the VCIND and NCI group with statistically significant differences (P < 0.05); the average scores of WML, CDT, and AIS in the VaD group are higher than those of the VCIND and NCI group with statistically significant differences (P < 0.05). Therefore, it is believed that the structural and functional imaging features of cerebrovascular disease are closely related to cognition-related fibers, and the incidence of white matter lesions is closely related to the degree of lesions and cognitive dysfunction of cerebral small vessel disease, in which a major risk factor for cognitive dysfunction in patients with small blood vessels is the severity of white matter lesions; brain imaging and neuropsychiatric function assessment can better understand the relationship between cerebrovascular disease and cognitive impairment. The results of this study provide a reference for the further research studies on the relationship between structural and functional imaging of cerebrovascular disease and cognition-related fibers.

Empirical assessment of changing sample-characteristics in task-fMRI over two decades: An example from gustatory and food studies.

Over the past two decades, functional neuroimaging has not only grown into a large field of research, but also substantially evolved. Here we provide a quantitative assessment of these presumed in sample composition and data analysis, using fMRI studies on food/taste research published between 1998 and 2019 as an exemplary case in which the scientific objectives themselves have remained largely stable. A systematic search for papers written in English was done using multiple databases and identified 426 original articles that were subsequently analyzed. The median sample size significantly increased from 11.5 to 35.5 while the ratio of male to female subjects remained stable. There were, however, more papers involving female subjects only, rather than male subjects only, since 2003. There was a decline in uncorrected results and statistical correction by false-discovery rate. Reflecting a trend toward more conservative thresholding, the number of foci reported per paper did not change significantly and sample size (power) did not correlate with the number of reported foci. The median journal impact factor and the normalized number of citations (citations per year) of the papers, in turn, showed a significantly decreasing trend. Number of citations negatively correlated to sample size, publication year but positively correlated to journal impact factor, and was also influenced by statistical correction method. There was a decreasing trend in studies recruiting both left-handed and right-handed subjects. In summary, the present paper quantifies several large-scale trends that have often been anecdotally discussed and reveals the changing nature of neuroimaging studies that may be considered when pursuing meta-analytic approaches.

Hippocampal Neuroimaging-Informed Clinical Trial Enrichment Tool for Amnestic Mild Cognitive Impairment Using Open Data.

Our goal was to assess the enrichment utility of hippocampal volume (HV) as an enrichment biomarker in amnestic mild cognitive impairment (aMCI) clinical trials, and, hence, develop an HV neuroimaging-informed clinical trial enrichment tool. Modeling of integrated longitudinal patient-level data came from open-access natural history studies in patients diagnosed with aMCI-the Alzheimer's Disease Neuroimaging Initiative (ADNI)-1 and ADNI-2-and indicated that a decrease of 1 cm3 with respect to the analysis dataset median baseline intracranial volume-adjusted HV (ICV-HV; ~ 5 cm3 ) is associated with > 50% increase in disease progression rate as measured by the Clinical Dementia Rating Scale-Sum of Boxes. Clinical trial simulations showed that the inclusion of aMCI subjects with baseline ICV-HV below the 84th or 50th percentile allowed an approximate reduction in trial size of at least 26% and 55%, respectively. This clinical trial enrichment tool can help design more efficient and informative clinical trials.

Effect-Size Estimation Using Semiparametric Hierarchical Mixture Models in Disease-Association Studies with Neuroimaging Data.

In disease-association studies using neuroimaging data, evaluating the biological or clinical significance of individual associations requires not only detection of disease-associated areas of the brain but also estimation of the magnitudes of the associations or effect sizes for individual brain areas. In this paper, we propose a model-based framework for voxel-based inferences under spatial dependency in neuroimaging data. Specifically, we employ hierarchical mixture models with a hidden Markov random field structure to incorporate the spatial dependency between voxels. A nonparametric specification is proposed for the effect size distribution to flexibly estimate the underlying effect size distribution. Simulation experiments demonstrate that compared with a naive estimation method, the proposed methods can substantially reduce the selection bias in the effect size estimates of the selected voxels with the greatest observed associations. An application to neuroimaging data from an Alzheimer's disease study is provided.

Simultaneous confidence corridors for mean functions in functional data analysis of imaging data.

Motivated by recent work involving the analysis of biomedical imaging data, we present a novel procedure for constructing simultaneous confidence corridors for the mean of imaging data. We propose to use flexible bivariate splines over triangulations to handle an irregular domain of the images that is common in brain imaging studies and in other biomedical imaging applications. The proposed spline estimators of the mean functions are shown to be consistent and asymptotically normal under some regularity conditions. We also provide a computationally efficient estimator of the covariance function and derive its uniform consistency. The procedure is also extended to the two-sample case in which we focus on comparing the mean functions from two populations of imaging data. Through Monte Carlo simulation studies, we examine the finite sample performance of the proposed method. Finally, the proposed method is applied to analyze brain positron emission tomography data in two different studies. One data set used in preparation of this article was obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database.

Analysis of secondary phenotypes in multigroup association studies.

Although case-control association studies have been widely used, they are insufficient for many complex diseases, such as Alzheimer's disease and breast cancer, since these diseases may have multiple subtypes with distinct morphologies and clinical implications. Many multigroup studies, such as the Alzheimer's Disease Neuroimaging Initiative (ADNI), have been undertaken by recruiting subjects based on their multiclass primary disease status, while extensive secondary outcomes have been collected. The aim of this paper is to develop a general regression framework for the analysis of secondary phenotypes collected in multigroup association studies. Our regression framework is built on a conditional model for the secondary outcome given the multigroup status and covariates and its relationship with the population regression of interest of the secondary outcome given the covariates. Then, we develop generalized estimation equations to estimate the parameters of interest. We use both simulations and a large-scale imaging genetic data analysis from the ADNI to evaluate the effect of the multigroup sampling scheme on standard genome-wide association analyses based on linear regression methods, while comparing it with our statistical methods that appropriately adjust for the multigroup sampling scheme. Data used in preparation of this article were obtained from the ADNI database.

Determining the Patient Complexity of Head CT Examinations: Implications for Proper Valuation of a Critical Imaging Service.

PURPOSE: The head-computed tomography (CT) exam code was recently identified by policy makers as having a potentially overvalued resource value units (RVU). A critical aspect in determining RVUs is the complexity of patients undergoing the service. This study evaluated the complexity of patients undergoing head-CT. METHODS: The 2017 Medicare PSPS Master File was used to identify the most common site for performing head-CT examinations. Given the most common location, the 5% Research Identifiable File, was then used to evaluate complexity of patients undergoing head CT on the same day as an emergency department (ED) visit based on the Evaluation & Management (E&M) "level" of these visits (1-least complex to 5-most complex patient) and the ICD-10 diagnosis coding associated with the billed head CT claims. RESULTS: 56.1% of head CT examinations were performed in the ED. Seventy percent of noncontrast exams performed in the ED were ordered in the most complex patient encounters (level 5 E&M visits). The most common ICD-10 code for head-CT without intravenous contrast billed with a level 5 E&M visit was "dizziness and giddiness," and for head-CT without and with intravenous contrast was "headache." CONCLUSION: Head-CT is not only most frequently ordered in the ED, but also during the most complex ED visits, suggesting that the ICD-10 codes associated with such exams do not appropriately reflects patient complexity. The valuation process should also consider the complexity of associated billed patient encounters, as indicated by E&M visit levels.

Neuroimaging of Adults with Headache: Appropriateness, Utilization, and an Economical Overview.

One of the most common reasons that a patient seeks out a health care provider for a neuroscience-related issue is headache. Not all patients can, or probably should, be imaged with headache. We must use an approach that attends to scientific evidence, accepted guidelines, and available resources. This approach should focus on quality, safety, appropriateness, and utilization. This article reviews and discusses the consideration of imaging adult patients with headache.

Handling Multiplicity in Neuroimaging Through Bayesian Lenses with Multilevel Modeling.

Here we address the current issues of inefficiency and over-penalization in the massively univariate approach followed by the correction for multiple testing, and propose a more efficient model that pools and shares information among brain regions. Using Bayesian multilevel (BML) modeling, we control two types of error that are more relevant than the conventional false positive rate (FPR): incorrect sign (type S) and incorrect magnitude (type M). BML also aims to achieve two goals: 1) improving modeling efficiency by having one integrative model and thereby dissolving the multiple testing issue, and 2) turning the focus of conventional null hypothesis significant testing (NHST) on FPR into quality control by calibrating type S errors while maintaining a reasonable level of inference efficiency. The performance and validity of this approach are demonstrated through an application at the region of interest (ROI) level, with all the regions on an equal footing: unlike the current approaches under NHST, small regions are not disadvantaged simply because of their physical size. In addition, compared to the massively univariate approach, BML may simultaneously achieve increased spatial specificity and inference efficiency, and promote results reporting in totality and transparency. The benefits of BML are illustrated in performance and quality checking using an experimental dataset. The methodology also avoids the current practice of sharp and arbitrary thresholding in the p-value funnel to which the multidimensional data are reduced. The BML approach with its auxiliary tools is available as part of the AFNI suite for general use.

A Retrospective Evaluation of Time to Obtain an MRI for English- Versus Non-English-Speaking Patients.

PURPOSE: Americans with limited English proficiency (LEP) face significant barriers to health care that result in health disparities in the LEP population. LEP could delay an MRI, potentially increasing morbidity and mortality in the LEP population. This study compares the time to obtain a neurological MRI in English versus non-English language preference patients. METHODS: 24,219 unique patients at a single health system who underwent inpatient neurological MRI were included in the study. Bivariate and multivariate analyses were used to identify characteristics predictive of time to examination (TTE) from the set: patient-preferred language, gender, race, age, performing hospital, and order priority (routine versus stat). RESULTS: Bivariate analysis showed a longer TTE is associated with increasing age category, non-English language preference, and routine priority. A multivariate analysis showed non-English language preference effect on TTE is reduced in magnitude and is no longer significant in a model that includes age group, priority, and hospital (P = .23, effect estimate = 4%, 95% CI: -2.5%, 11.0%). Routine order priority (P < .0001) and increasing age (P < .0001) were associated with increased TTE. In a model that included interactions, the effect of language preference did not depend on order priority (P = .59) or age group (P = .11). CONCLUSION: There is no significant difference in the time to obtain a neurological MRI in English versus non-English language preference patients when age, order priority, and performing hospital are accounted for. This finding supports the effectiveness of the protocols and resources in place to support patients with LEP at the sponsoring health system.

Characteristics of MR Neuroimaging Services Billed by Radiologists versus Nonradiologists.

BACKGROUND AND PURPOSE: Although most neuroimaging examinations are interpreted by radiologists, many nonradiologists provide interpretation services. We studied day of the week, site of service, and patient complexity differences for common Medicare MR neuroimaging examinations interpreted by radiologists versus nonradiologists. MATERIALS AND METHODS: Using carrier claims files for a 5% sample of Medicare beneficiaries from 2012 to 2014, we identified all claims for brain and lumbar spine MR imaging examinations. Services were categorized by physician specialty, day of the week, and the site of service. Patient complexity was calculated using Charlson Comorbidity Indices. The χ2 was performed to test statistical significance. RESULTS: A provider specialty could be identified for 568,423 brain and lumbar spine MR imaging examinations. Of weekday examinations, radiologists interpreted 475,288 (92.3%), and nonradiologists, 39,510 (7.7%). Of weekend examinations, radiologists interpreted 52,028 (97.0%) and nonradiologists 1597 (3.0%). Radiologists interpreted 145,904 (98.7%) examinations in the inpatient hospital and emergency department settings versus 1882 (1.3%) by nonradiologists. Of all examinations, 44,547 of those interpreted by radiologists (8.4%) were on the most clinically complex patients versus 2139 (5.2%) for nonradiologists. All interspecialty differences for day of the week, the site of service, and patient complexity were statistically significant (P < .001). CONCLUSIONS: Although radiologists interpret most common MR neuroimaging examinations for Medicare beneficiaries, in contrast to nonradiologists, they disproportionately render those services on weekends, in higher acuity sites, and on more complex patients. To optimize access and minimize disparities in necessary neuroimaging, quality metrics should consider such service characteristics.