Neuroimaging meta regression for coordinate based meta analysis data with a spatial model.

Coordinate-based meta-analysis combines evidence from a collection of neuroimaging studies to estimate brain activation. In such analyses, a key practical challenge is to find a computationally efficient approach with good statistical interpretability to model the locations of activation foci. In this article, we propose a generative coordinate-based meta-regression (CBMR) framework to approximate a smooth activation intensity function and investigate the effect of study-level covariates (e.g. year of publication, sample size). We employ a spline parameterization to model the spatial structure of brain activation and consider four stochastic models for modeling the random variation in foci. To examine the validity of CBMR, we estimate brain activation on 20 meta-analytic datasets, conduct spatial homogeneity tests at the voxel level, and compare the results to those generated by existing kernel-based and model-based approaches. Keywords: generalized linear models; meta-analysis; spatial statistics; statistical modeling.

Racial and Ethnic Differences in Hospital Admission and Diagnostic Evaluation for Febrile Seizures in the Emergency Department.

OBJECTIVE: To examine differences in hospital admission and diagnostic evaluation for febrile seizure by race and ethnicity. STUDY DESIGN: We conducted a cross-sectional study among children 6 months to 6 years with simple or complex febrile seizure between January 1, 2016, and December 31, 2021, using data from the Pediatric Health Information System. The primary outcome was hospital admission. Secondary outcomes included the proportion of encounters with neuroimaging or lumbar puncture. We used mixed-effects logistic regression model with random intercept for hospital and patient to estimate the association between outcomes and race and ethnicity after adjusting for covariates, including seizure type. RESULTS: In total, 94 884 encounters were included. Most encounters occurred among children of non-Hispanic White (37.0%), Black (23.9%), and Hispanic/Latino (24.6%) race and ethnicity. Black and Hispanic/Latino children had 29% (aOR 0.71; 95% CI 0.66-0.75) and 26% (aOR 0.74; 95% CI 0.69-0.80) lower odds of hospital admission compared with non-Hispanic White children, respectively. Black and Hispanic/Latino children had 21% (aOR 0.79; 95% CI 0.73-0.86) and 22% (aOR 0.78; 95% CI 0.71-0.85) lower adjusted odds of neuroimaging compared with non-Hispanic White children. For complex febrile seizure, the adjusted odds of lumbar puncture was significantly greater among Asian children (aOR 2.12; 95% CI 1.19-3.77) compared with non-Hispanic White children. There were no racial differences in the odds of lumbar puncture for simple febrile seizure. CONCLUSIONS: Compared with non-Hispanic White children, Black and Hispanic/Latino children with febrile seizures are less likely to be hospitalized or receive neuroimaging.

Bayesian inference for group-level cortical surface image-on-scalar regression with Gaussian process priors.

In regression-based analyses of group-level neuroimage data, researchers typically fit a series of marginal general linear models to image outcomes at each spatially referenced pixel. Spatial regularization of effects of interest is usually induced indirectly by applying spatial smoothing to the data during preprocessing. While this procedure often works well, the resulting inference can be poorly calibrated. Spatial modeling of effects of interest leads to more powerful analyses; however, the number of locations in a typical neuroimage can preclude standard computing methods in this setting. Here, we contribute a Bayesian spatial regression model for group-level neuroimaging analyses. We induce regularization of spatially varying regression coefficient functions through Gaussian process priors. When combined with a simple non-stationary model for the error process, our prior hierarchy can lead to more data-adaptive smoothing than standard methods. We achieve computational tractability through a Vecchia-type approximation of our prior that retains full spatial rank and can be constructed for a wide class of spatial correlation functions. We outline several ways to work with our model in practice and compare performance against standard vertex-wise analyses and several alternatives. Finally, we illustrate our methods in an analysis of cortical surface functional magnetic resonance imaging task contrast data from a large cohort of children enrolled in the adolescent brain cognitive development study.

Controlling false discovery rate for mediator selection in high-dimensional data.

The need to select mediators from a high dimensional data source, such as neuroimaging data and genetic data, arises in much scientific research. In this work, we formulate a multiple-hypothesis testing framework for mediator selection from a high-dimensional candidate set, and propose a method, which extends the recent development in false discovery rate (FDR)-controlled variable selection with knockoff to select mediators with FDR control. We show that the proposed method and algorithm achieved finite sample FDR control. We present extensive simulation results to demonstrate the power and finite sample performance compared with the existing method. Lastly, we demonstrate the method for analyzing the Adolescent Brain Cognitive Development (ABCD) study, in which the proposed method selects several resting-state functional magnetic resonance imaging connectivity markers as mediators for the relationship between adverse childhood events and the crystallized composite score in the NIH toolbox.

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.

A retrospective study of CT scan utilization in the emergency department for patients presenting with seizures.

BACKGROUND/AIM: The indications for neuroimaging in emergency department (ED) patients presenting with seizures have not been clearly defined. In this study, we aimed to investigate the findings that may influence the emergency management of patients with seizures undergoing brain computed tomography (CT) and the factors that influence these findings. MATERIAL AND METHODS: This is a retrospective, single-center study. Patients presenting to the ED with seizures-both patients with diagnosed epilepsy and patients with first-time seizures-who underwent brain CT were included. Demographic information and indications for CT scans were recorded. According to the CT findings, patients were classified as having or not having significant pathology, and comparisons were made. Intracranial mass, intraparenchymal, subdural, and subarachnoid hemorrhage, fracture, and cerebral edema were considered significant pathologies. RESULTS: This study included 404 patients. The most common reason for a CT scan was head trauma. A significant pathology was found on the CT scan in 5.4% of the patients. A regression analysis showed that hypertension, malignancy, and a prolonged postictal state were the predictive factors for significant pathology on CT. CONCLUSION: CT scanning of patients presenting to the ED with seizures has a limited impact on emergency patient management. Clinical decision-making guidelines for emergency CT scanning of patients with seizures need to be reviewed and improved to identify zero/near-zero risk patients for whom imaging can be deferred.

Multidimensional analysis and detection of informative features in human brain white matter.

The white matter contains long-range connections between different brain regions and the organization of these connections holds important implications for brain function in health and disease. Tractometry uses diffusion-weighted magnetic resonance imaging (dMRI) to quantify tissue properties along the trajectories of these connections. Statistical inference from tractometry usually either averages these quantities along the length of each fiber bundle or computes regression models separately for each point along every one of the bundles. These approaches are limited in their sensitivity, in the former case, or in their statistical power, in the latter. We developed a method based on the sparse group lasso (SGL) that takes into account tissue properties along all of the bundles and selects informative features by enforcing both global and bundle-level sparsity. We demonstrate the performance of the method in two settings: i) in a classification setting, patients with amyotrophic lateral sclerosis (ALS) are accurately distinguished from matched controls. Furthermore, SGL identifies the corticospinal tract as important for this classification, correctly finding the parts of the white matter known to be affected by the disease. ii) In a regression setting, SGL accurately predicts "brain age." In this case, the weights are distributed throughout the white matter indicating that many different regions of the white matter change over the lifespan. Thus, SGL leverages the multivariate relationships between diffusion properties in multiple bundles to make accurate phenotypic predictions while simultaneously discovering the most relevant features of the white matter.

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.

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.

Physics-based reconstruction methods for magnetic resonance imaging.

Conventional magnetic resonance imaging (MRI) is hampered by long scan times and only qualitative image contrasts that prohibit a direct comparison between different systems. To address these limitations, model-based reconstructions explicitly model the physical laws that govern the MRI signal generation. By formulating image reconstruction as an inverse problem, quantitative maps of the underlying physical parameters can then be extracted directly from efficiently acquired k-space signals without intermediate image reconstruction-addressing both shortcomings of conventional MRI at the same time. This review will discuss basic concepts of model-based reconstructions and report on our experience in developing several model-based methods over the last decade using selected examples that are provided complete with data and code. This article is part of the theme issue 'Synergistic tomographic image reconstruction: part 1'.

The association between intraoperative hyperglycemia and cerebrovascular markers.

BACKGROUND AND PURPOSE: Hyperglycemia can lead to an increased rate of apoptosis of microglial cells and to damaged neurons. The relation between hyperglycemia and cerebrovascular markers on MRI is unknown. Our aim was to study the association between intraoperative hyperglycemia and cerebrovascular markers. METHODS: In this further analysis of a subgroup investigation of the BIOCOG study, 65 older non-demented patients (median 72 years) were studied who underwent elective surgery of ≥ 60 minutes. Intraoperative blood glucose maximum was determined retrospectively in each patient. In these patients, preoperatively and at 3 months follow-up a MRI scan was performed and white matter hyperintensity (WMH) volume and shape, infarcts, and perfusion parameters were determined. Multivariable logistic regression analyses were performed to determine associations between preoperative cerebrovascular markers and occurrence of intraoperative hyperglycemia. Linear regression analyses were performed to assess the relation between intraoperative hyperglycemia and pre- to postoperative changes in WMH volume. Associations between intraoperative hyperglycemia and postoperative WMH volume at 3 months follow-up were also assessed by linear regression analyses. RESULTS: Eighteen patients showed intraoperative hyperglycemia (glucose maximum ≥ 150 mg/dL). A preoperative more smooth shape of periventricular and confluent WMH was related to the occurrence of intraoperative hyperglycemia [convexity: OR 33.318 (95 % CI (1.002 - 1107.950); p = 0.050]. Other preoperative cerebrovascular markers were not related to the occurrence of intraoperative hyperglycemia. Intraoperative hyperglycemia showed no relation with pre- to postoperative changes in WMH volume nor with postoperative WMH volume at 3 months follow-up. CONCLUSIONS: We found that a preoperative more smooth shape of periventricular and confluent WMH was related to the occurrence of intraoperative hyperglycemia. These findings may suggest that a similar underlying mechanism leads to a certain pattern of vascular brain abnormalities and an increased risk of hyperglycemia.

Diagnostic Error of Neuro-ophthalmologic Conditions: State of the Science.

BACKGROUND: Diagnostic error is prevalent and costly, occurring in up to 15% of US medical encounters and affecting up to 5% of the US population. One-third of malpractice payments are related to diagnostic error. A complex and specialized diagnostic process makes neuro-ophthalmologic conditions particularly vulnerable to diagnostic error. EVIDENCE ACQUISITION: English-language literature on diagnostic errors in neuro-ophthalmology and neurology was identified through electronic search of PubMed and Google Scholar and hand search. RESULTS: Studies investigating diagnostic error of neuro-ophthalmologic conditions have revealed misdiagnosis rates as high as 60%-70% before evaluation by a neuro-ophthalmology specialist, resulting in unnecessary tests and treatments. Correct performance and interpretation of the physical examination, appropriate ordering and interpretation of neuroimaging tests, and generation of a differential diagnosis were identified as pitfalls in the diagnostic process. Most studies did not directly assess patient harms or financial costs of diagnostic error. CONCLUSIONS: As an emerging field, diagnostic error in neuro-ophthalmology offers rich opportunities for further research and improvement of quality of care.

Association Between Adherence to Quality Indicators and 7-Day In-Hospital Mortality After Acute Ischemic Stroke.

BACKGROUND AND PURPOSE: Quality indicators (QI) are an accepted tool to measure performance of hospitals in routine care. We investigated the association between quality of acute stroke care defined by overall adherence to evidence-based QI and early outcome in German acute care hospitals. METHODS: Patients with ischemic stroke admitted to one of the hospitals cooperating within the ADSR (German Stroke Register Study Group) were analyzed. The ADSR is a voluntary network of 9 regional stroke registers monitoring quality of acute stroke care across 736 hospitals in Germany. Quality of stroke care was defined by adherence to 11 evidence-based indicators of early processes of stroke care. The correlation between overall adherence to QI with outcome was investigated by assessing the association between 7-day in-hospital mortality with the proportion of QI fulfilled from the total number of QI the individual patient was eligible for. Generalized linear mixed model analysis was performed adjusted for the variables age, sex, National Institutes of Health Stroke Scale and living will and as random effect for the variable hospital. RESULTS: Between 2015 and 2016, 388 012 patients with ischemic stroke were reported (median age 76 years, 52.4% male). Adherence to distinct QI ranged between 41.0% (thrombolysis in eligible patients) and 95.2% (early physiotherapy). Seven-day in-hospital mortality was 3.4%. The overall proportion of QI fulfilled was median 90% (interquartile range, 75%-100%). In multivariable analysis, a linear association between overall adherence to QI and 7-day in-hospital-mortality was observed (odds ratio adherence <50% versus 100%, 12.7 [95% CI, 11.8-13.7]; P<0.001). CONCLUSIONS: Higher quality of care measured by adherence to a set of evidence-based process QI for the early phase of stroke treatment was associated with lower in-hospital mortality.

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.

Sample size evolution in neuroimaging research: An evaluation of highly-cited studies (1990-2012) and of latest practices (2017-2018) in high-impact journals.

We evaluated 1038 of the most cited structural and functional (fMRI) magnetic resonance brain imaging papers (1161 studies) published during 1990-2012 and 270 papers (300 studies) published in top neuroimaging journals in 2017 and 2018. 96% of highly cited experimental fMRI studies had a single group of participants and these studies had median sample size of 12, highly cited clinical fMRI studies (with patient participants) had median sample size of 14.5, and clinical structural MRI studies had median sample size of 50. The sample size of highly cited experimental fMRI studies increased at a rate of 0.74 participant/year and this rate of increase was commensurate with the median sample sizes of neuroimaging studies published in top neuroimaging journals in 2017 (23 participants) and 2018 (24 participants). Only 4 of 131 papers in 2017 and 5 of 142 papers in 2018 had pre-study power calculations, most for single t-tests and correlations. Only 14% of highly cited papers reported the number of excluded participants whereas 49% of papers with their own data in 2017 and 2018 reported excluded participants. Publishers and funders should require pre-study power calculations necessitating the specification of effect sizes. The field should agree on universally required reporting standards. Reporting formats should be standardized so that crucial study parameters could be identified unequivocally.

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.

Hubs of long-distance co-alteration characterize brain pathology.

It is becoming clearer that the impact of brain diseases is more convincingly represented in terms of co-alterations rather than in terms of localization of alterations. In this context, areas characterized by a long mean distance of co-alteration may be considered as hubs with a crucial role in the pathology. We calculated meta-analytic transdiagnostic networks of co-alteration for the gray matter decreases and increases, and we evaluated the mean Euclidean, fiber-length, and topological distance of its nodes. We also examined the proportion of co-alterations between canonical networks, and the transdiagnostic variance of the Euclidean distance. Furthermore, disease-specific analyses were conducted on schizophrenia and Alzheimer's disease. The anterodorsal prefrontal cortices appeared to be a transdiagnostic hub of long-distance co-alterations. Also, the disease-specific analyses showed that long-distance co-alterations are more able than classic meta-analyses to identify areas involved in pathology and symptomatology. Moreover, the distance maps were correlated with the normative connectivity. Our findings substantiate the network degeneration hypothesis in brain pathology. At the same time, they suggest that the concept of co-alteration might be a useful tool for clinical neuroscience.

Human Positron Emission Tomography Neuroimaging.

Neuroimaging with positron emission tomography (PET) is the most powerful tool for understanding pharmacology, neurochemistry, and pathology in the living human brain. This technology combines high-resolution scanners to measure radioactivity throughout the human body with specific, targeted radioactive molecules, which allow measurements of a myriad of biological processes in vivo. While PET brain imaging has been active for almost 40 years, the pace of development for neuroimaging tools, known as radiotracers, and for quantitative analytical techniques has increased dramatically over the past decade. Accordingly, the fundamental questions that can be addressed with PET have expanded in basic neurobiology, psychiatry, neurology, and related therapeutic development. In this review, we introduce the field of human PET neuroimaging, some of its conceptual underpinnings, and motivating questions. We highlight some of the more recent advances in radiotracer development, quantitative modeling, and applications of PET to the study of the human brain.

A Bayesian approach to joint modeling of matrix-valued imaging data and treatment outcome with applications to depression studies.

In this paper, we propose a unified Bayesian joint modeling framework for studying association between a binary treatment outcome and a baseline matrix-valued predictor. Specifically, a joint modeling approach relating an outcome to a matrix-valued predictor through a probabilistic formulation of multilinear principal component analysis is developed. This framework establishes a theoretical relationship between the outcome and the matrix-valued predictor, although the predictor is not explicitly expressed in the model. Simulation studies are provided showing that the proposed method is superior or competitive to other methods, such as a two-stage approach and a classical principal component regression in terms of both prediction accuracy and estimation of association; its advantage is most notable when the sample size is small and the dimensionality in the imaging covariate is large. Finally, our proposed joint modeling approach is shown to be a very promising tool in an application exploring the association between baseline electroencephalography data and a favorable response to treatment in a depression treatment study by achieving a substantial improvement in prediction accuracy in comparison to competing methods.

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.