Detection of sparse differential dependent functional brain connectivity.

Functional brain connectivity analysis is an increasingly important technique in neuroscience, psychiatry, and autism research. Functional connectivity can be measured by considering co-activation of brain regions in resting-state functional magnetic resonance imaging (rs-fMRI). We propose a novel Bayesian model to detect differential connections in cross-correlated functional connectivity between region of interest (ROI) pairs. The proposed sparse clustered neighborhood model induces a lower-dimensional sparsity and clustering based on a nonparametric Bayesian approach to model sparse differentially connected ROI pairs. Second, it induces a structured dependence model for modeling potential dependence among ROI pairs. We demonstrate Bayesian inference and performance of the proposed model in simulation studies and compare with a standard model. We utilize the proposed model to contrast functional connectivities between participants with autism spectrum disorder and neurotypical participants using cross-correlated rs-fMRI data from four sites of the Autism Brain Image Data Exchange.

An Initial miRNA Profile of Persons With Persisting Neurobehavioral Impairments and States of Disordered Consciousness After Severe Traumatic Brain Injury.

OBJECTIVE: To examine the merits of using microRNAs (miRNAs) as biomarkers of disorders of consciousness (DoC) due to traumatic brain injury (TBI). SETTINGS: Acute and subacute beds. PARTICIPANTS: Patients remaining in vegetative and minimally conscious states (VS, MCS), an average of 1.5 years after TBI, and enrolled in a randomized clinical trial ( n = 6). Persons without a diagnosed central nervous system disorder, neurotypical controls ( n = 5). DESIGN: Comparison of whole blood miRNA profiles between patients and age/gender-matched controls. For patients, correlational analyses between miRNA profiles and measures of neurobehavioral function. MAIN MEASURES: Baseline measures of whole blood miRNAs isolated from the cellular and fluid components of blood and measured using miRNA-seq and real-time polymerase chain reaction (RT-PCR). Baseline neurobehavioral measures derived from 7 tests. RESULTS: For patients, relative to controls, 48 miRNA were significantly ( P < .05)/differentially expressed. Cluster analysis showed that neurotypical controls were most similar to each other and with 2 patients (VS: n = 1; and MCS: n = 1). Three patients, all in MCS, clustered separately. The only female in the sample, also in MCS, formed an independent group. For the 48 miRNAs, the enriched pathways identified are implicated in secondary brain damage and 26 miRNAs were significantly ( P < .05) correlated with measures of neurobehavioral function. CONCLUSIONS: Patients remaining in states of DoC an average of 1.5 years after TBI showed a different and reproducible pattern of miRNA expression relative to age/gender-matched neurotypical controls. The phenotypes, defined by miRNA profiles relative to persisting neurobehavioral impairments, provide the basis for future research to determine the miRNA profiles differentiating states of DoC and the basis for future research using miRNA to detect treatment effects, predict treatment responsiveness, and developing targeted interventions. If future research confirms and advances reported findings, then miRNA profiles will provide the foundation for patient-centric DoC neurorehabilitation.

Vaginal and Penile Microbiome Associations With Herpes Simplex Virus Type 2 in Women and Their Male Sex Partners.

BACKGROUND: We determined how the vaginal and penile microbiomes contribute to herpes simplex virus type 2 (HSV-2) serostatus within sexual partnerships. METHODS: Microbiomes were characterized in cervicovaginal lavage and penile meatal swab specimens through high-throughput 16s ribosomal RNA gene amplicon sequencing. HSV-2 antibody was detected in serum specimens. We modeled vaginal and penile taxa and covariates contributing to HSV-2 status in women and men using bivariate probit analysis. RESULTS: Among 231 couples, HSV-2 was detected in both partners in 78 couples (33.8%), in the woman only in 52 (22.5%),in the man only in 27 (11.7%), and in neither in 74 (32.0%). Among the women (median age, 22 years) 10.9% had human immunodeficiency virus (HIV), and 21.4% had Bacterial vaginosis. Among men (median age, 26 years), 11.8% had HIV, and 55.0% circumcised. In an analysis with adjustment for sociodemographics and Bacterial vaginosis, enrichment of vaginal Gardnerella vaginalis and Lactobacillus iners was associated with increased likelihood of HSV-2 in both partners. Penile taxa (including Ureaplasma and Aerococcus) were associated with HSV-2 in women. CONCLUSIONS: We demonstrate that penile taxa are associated with HSV-2 in female partners, and vaginal taxa are associated with HSV-2 in male partners. Our findings suggest that couples-level joint consideration of genital microbiome and sexually transmitted infection or related outcomes could lead to new avenues for prevention.

Generalized confidence interval for an agreement between raters.

Estimation and inference are two key components toward the solution of any statistical problem; however, the inferential issues of statistical assessment of agreement among two or more raters have not been well developed as compared to the development of estimation procedures in this area. The fundamental reason for this gap is the complex expression of the concordance correlation coefficient (CCC) that is frequently used in assessing agreement among raters. Large sample-based statistical tests for CCC often fail to produce desired results for small samples. Hence, inferential procedures for small samples are urgently needed to evaluate agreement between raters. We argue that hypothesis testing of CCC has little value in practice due to the absence of a gold standard of agreement. In this article, we construct the generalized confidence interval (GCI) for CCC utilizing a bivariate normal distribution of measurements, and also develop a large sample-based confidence interval (LSCI). We establish satisfactory performance of GCI by providing the desired coverage probability (CP) via simulation. Results of GCI and LSCI are illustrated and compared with a data set of a recent study performed at U.S. Department of Veterans Affairs, Hines.

miRNAs as Potential Biomarkers for Traumatic Brain Injury: Pathway From Diagnosis to Neurorehabilitation.

BACKGROUND: Biomarkers that can advance precision neurorehabilitation of the traumatic brain injury (TBI) are needed. MicroRNAs (miRNAs) have biological properties that could make them well suited for playing key roles in differential diagnoses and prognoses and informing likelihood of responsiveness to specific treatments. OBJECTIVE: To review the evidence of miRNA alterations after TBI and evaluate the state of science relative to potential neurorehabilitation applications of TBI-specific miRNAs. METHODS: This scoping review includes 57 animal and human studies evaluating miRNAs after TBI. PubMed, Scopus, and Google Scholar search engines were used. RESULTS: Gold standard analytic steps for miRNA biomarker assessment are presented. Published studies evaluating the evidence for miRNAs as potential biomarkers for TBI diagnosis, severity, natural recovery, and treatment-induced outcomes were reviewed including statistical evaluation. Growing evidence for specific miRNAs, including miR21, as TBI biomarkers is presented. CONCLUSIONS: There is evidence of differential miRNA expression in TBI in both human and animal models; however, gaps need to be filled in terms of replication using rigorous, standardized methods to isolate a consistent set of miRNA changes. Longitudinal studies in TBI are needed to understand how miRNAs could be implemented as biomarkers in clinical practice.

After-School Programs and Children's Mental Health: Organizational Social Context, Program Quality, and Children's Social Behavior.

OBJECTIVE: The current study examined associations among organizational social context, after-school program (ASP) quality, and children's social behavior in a large urban park district. METHOD: Thirty-two park-based ASPs are included in the final sample, including 141 staff and 593 children. Staff reported on organizational culture (rigidity, proficiency, resistance) and climate (engagement, functionality, stress), and children's social skills and problem behaviors. Children and their parents reported on program quality indicators (e.g., activities, routines, relationships). Parents also completed a children's mental health screener. RESULTS: A series of Hierarchical Linear Models revealed that proficiency and stress were the only organizational predictors of program quality; associations between stress and program quality were moderated by program enrollment and aggregated children's mental health need. Higher child- and parent-perceived program quality related to fewer staff-reported problem behaviors, while overall higher enrollment and higher aggregated mental health need were associated with fewer staff-reported social skills. CONCLUSIONS: Data are informing ongoing efforts to improve organizational capacity of urban after-school programs to support children's positive social and behavior trajectories.

Characteristics of Women and Their Male Sex Partners Predict Bacterial Vaginosis Among a Prospective Cohort of Kenyan Women With Nonoptimal Vaginal Microbiota.

BACKGROUND: Up to 50% of women with nonoptimal vaginal microbial community state type (CST) have bacterial vaginosis (BV). Little is known about what distinguishes women with and without BV diagnosis within nonoptimal CST. We identified features of women and their male sex partners associated with BV among women with nonoptimal vaginal CST. METHODS: In this prospective study, 252 heterosexual couples were observed at 1, 6, and 12 months after baseline. Microbiomes were characterized in cervicovaginal lavage and penile meatal swabs through high-throughput 16s ribosomal RNA gene amplicon sequencing. Nonoptimal CST was defined as CST-IV. Bacterial vaginosis was defined as a Nugent score of 7 to 10. Generalized estimating equation analysis estimated adjusted odds ratios (aORs) for BV among women with nonoptimal CST. RESULTS: At baseline, women with nonoptimal CST were a median age of 22 years, 44% had BV, 16% had HIV, and 66% had herpes simplex virus (HSV) type 2. Male partners were a median age of 27 years, 12% had HIV, 48% had HSV-2, and 55% were circumcised. Within nonoptimal CST, Sneathia sanguinegens, Prevotella species, Prevotella amnii, and Clostridiales, BV-associated bacteria-2 were statistically significantly enriched in observations with BV. In multivariable generalized estimating equation controlling for CST, HIV, and HSV-2, BV was increased among women with CST-IVA (aOR, 1.91; P = 0.087), HIV (aOR, 2.30; P = 0.051), HSV-2 (aOR, 1.75; P = 0.065), and enrichment of male partner penile taxa: Dialister (aOR, 1.16; P = 0.034), Megasphaera (aOR, 1.22; P = 0.001), and Brevibacterium (aOR, 1.13; P = 0.019).These results provide insights into factors differentiating women with BV among those with nonoptimal vaginal CST. Interrupting the sexual exchange of penile and vaginal taxa may be beneficial for preventing pathologic state of vaginal microbiome.

A Pilot Trial Examining the Merits of Combining Amantadine and Repetitive Transcranial Magnetic Stimulation as an Intervention for Persons With Disordered Consciousness After TBI.

OBJECTIVE: Report pilot findings of neurobehavioral gains and network changes observed in persons with disordered consciousness (DoC) who received repetitive transcranial magnetic stimulation (rTMS) or amantadine (AMA), and then rTMS+AMA. PARTICIPANTS: Four persons with DoC 1 to 15 years after traumatic brain injury (TBI). DESIGN: Alternate treatment-order, within-subject, baseline-controlled trial. MAIN MEASURES: For group and individual neurobehavioral analyses, predetermined thresholds, based on mixed linear-effects models and conditional minimally detectable change, were used to define meaningful neurobehavioral change for the Disorders of Consciousness Scale-25 (DOCS) total and Auditory-Language measures. Resting-state functional connectivity (rsFC) of the default mode and 6 other networks was examined. RESULTS: Meaningful gains in DOCS total measures were observed for 75% of treatment segments and auditory-language gains were observed after rTMS, which doubled when rTMS preceded rTMS+AMA. Neurobehavioral changes were reflected in rsFC for language, salience, and sensorimotor networks. Between networks interactions were modulated, globally, after all treatments. CONCLUSIONS: For persons with DoC 1 to 15 years after TBI, meaningful neurobehavioral gains were observed after provision of rTMS, AMA, and rTMS+AMA. Sequencing and combining of treatments to modulate broad-scale neural activity, via differing mechanisms, merits investigation in a future study powered to determine efficacy of this approach to enabling neurobehavioral recovery.

Quantification of intrinsic subtype ambiguity in Luminal A breast cancer and its relationship to clinical outcomes.

BACKGROUND: PAM50 gene profiling assigns each cancer to a single intrinsic subtype. However, individual cancers vary in their adherence to a prototype, and due to bulk tissue sampling, some may exhibit expression patterns that indicate intra-tumor admixture of multiple subtypes. Our objective was to develop admixture metrics from PAM50 gene expression profiles in order to stratify Luminal A (LumA) cases according to their degree of subtype admixture, and then relate such admixture to clinical and molecular variables. METHODS: We re-constructed scaled, normalized PAM50 profiles for 1980 cases (674 LumA) in the METABRIC cohort and for each case computed its Mahalanobis (M-) distance from its assigned centroid and M-distance from all other centroids. We used t-SNE plots to visualize overlaps in subtype clustering. With Normal-like cases excluded, we developed two metrics: Median Distance Criteria (MDC) classified pure cases as those located within the 50th percentile of the LumA centroid and > =50th percentile from any other centroid. Distance Ratio Criteria (DRC) was computed as the ratio of M-distances from the LumA centroid to the nearest non-assigned centroid. Pure and admixed LumA cases were compared on clinical/molecular traits. TCGA LumA cases (n = 509) provided independent validation. RESULTS: Compared to pure cases in METABRIC, admixed ones had older age at diagnosis, larger tumor size, and higher grade and stage. These associations were stronger for the DRC metric compared to MDC. Admixed cases were associated with HER2 gain, high proliferation, higher PAM50 recurrence scores, more frequent TP53 mutation, and less frequent PIK3CA mutation. Similar results were observed in the TCGA validation cohort, which also showed a positive association between admixture and number of clonal populations estimated by PyClone. LumA-LumB confusion predominated, but other combinations were also present. Degree of admixture was associated with overall survival in both cohorts, as was disease-free survival in TCGA, independent of age, grade and stage (HR = 2.85, Tertile 3 vs.1). CONCLUSIONS: Luminal A breast cancers subgrouped based on PAM50 subtype purity support the hypothesis that admixed cases have worse clinical features and survival. Future analyses will explore more extensive genomic metrics for admixture and their spatial significance within a single tumor.

Sample size determination for multilevel hierarchical designs using generalized linear mixed models.

A unified statistical methodology of sample size determination is developed for hierarchical designs that are frequently used in many areas, particularly in medical and health research studies. The solid foundation of the proposed methodology opens a new horizon for power analysis in presence of various conditions. Important features such as joint significance testing, unequal allocations of clusters across intervention groups, and differential attrition rates over follow up time points are integrated to address some useful questions that investigators often encounter while conducting such studies. Proposed methodology is shown to perform well in terms of maintaining type I error rates and achieving the target power under various conditions. Proposed method is also shown to be robust with respect to violation of distributional assumptions of random-effects.

Divergent Influences of Cardiovascular Disease Risk Factor Domains on Cognition and Gray and White Matter Morphology.

OBJECTIVE: Hypertension, diabetes, dyslipidemia, and obesity are associated with preclinical alterations in cognition and brain structure; however, this often comes from studies of comprehensive risk scores or single isolated factors. We examined associations of empirically derived cardiovascular disease risk factor domains with cognition and brain structure. METHODS: A total of 124 adults (age, 59.8 [13.1] years; 41% African American; 50% women) underwent neuropsychological and cardiovascular assessments and structural magnetic resonance imaging. Principal component analysis of nine cardiovascular disease risk factors resulted in a four-component solution representing 1, cholesterol; 2, glucose dysregulation; 3, metabolic dysregulation; and 4, blood pressure. Separate linear regression models for learning, memory, executive functioning, and attention/information processing were performed, with all components entered at once, adjusting for age, sex, and education. MRI analyses included whole-brain cortical thickness and tract-based fractional anisotropy adjusted for age and sex. RESULTS: Higher blood pressure was associated with poorer learning (B = -0.19; p = .019), memory (B = -0.22; p = .005), and executive functioning performance (B = -0.14; p = .031), and lower cortical thickness within the right lateral occipital lobe. Elevated glucose dysregulation was associated with poorer attention/information processing performance (B = -0.21; p = .006) and lower fractional anisotropy in the right inferior and bilateral superior longitudinal fasciculi. Cholesterol was associated with higher cortical thickness within left caudal middle frontal cortex. Metabolic dysfunction was positively associated with right superior parietal lobe, left inferior parietal lobe, and left precuneus cortical thickness. CONCLUSIONS: Cardiovascular domains were associated with distinct cognitive, gray, and white matter alterations and distinct age groups. Future longitudinal studies may assist in identifying vulnerability profiles that may be most important for individuals with multiple cardiovascular disease risk factors.

Emerging Role of One-Carbon Metabolism and DNA Methylation Enrichment on δ-Containing GABAA Receptor Expression in the Cerebellum of Subjects with Alcohol Use Disorders (AUD).

Background: Cerebellum is an area of the brain particularly sensitive to the effects of acute and chronic alcohol consumption. Alcohol exposure decreases cerebellar Purkinje cell output by increasing GABA release from Golgi cells onto extrasynaptic α6/δ-containing GABAA receptors located on glutamatergic granule cells. Here, we studied whether chronic alcohol consumption induces changes in GABAA receptor subunit expression and whether these changes are associated with alterations in epigenetic mechanisms via DNA methylation. Methods: We used a cohort of postmortem cerebellum from control and chronic alcoholics, here defined as alcohol use disorders subjects (n=25/group). S-adenosyl-methionine/S-adenosyl-homocysteine were measured by high-performance liquid chromatography. mRNA levels of various genes were assessed by reverse transcriptase-quantitative polymerase chain reaction. Promoter methylation enrichment was assessed using methylated DNA immunoprecipitation and hydroxy-methylated DNA immunoprecipitation assays. Results: mRNAs encoding key enzymes of 1-carbon metabolism that determine the S-adenosyl-methionine/S-adenosyl-homocysteine ratio were increased, indicating higher "methylation index" in alcohol use disorder subjects. We found that increased methylation of the promoter of the δ subunit GABAA receptor was associated with reduced mRNA and protein levels in the cerebellum of alcohol use disorder subjects. No changes were observed in α1- or α6-containing GABAA receptor subunits. The expression of DNA-methyltransferases (1, 3A, and 3B) was unaltered, whereas the mRNA level of TET1, which participates in the DNA demethylation pathway, was decreased. Hence, increased methylation of the δ subunit GABAA receptor promoter may result from alcohol-induced reduction of DNA demethylation. Conclusion: Together, these results support the hypothesis that aberrant DNA methylation pathways may be involved in cerebellar pathophysiology of alcoholism. Furthermore, this work provides novel evidence for a central role of DNA methylation mechanisms in the alcohol-induced neuroadaptive changes of human cerebellar GABAA receptor function.

Detection and prediction limits for identifying highly confusable drug names from experimental data.

Confusions between drug names that look and sound alike are common, costly, harmful, and difficult to prevent. One prevention strategy is to screen proposed new drug names for confusability before approving them. Widespread acceptance of preapproval tests of confusability is compromised by the lack of experimental designs and statistical methods to support valid inferences about whether a proposed new name is unacceptably confusing. One way of identifying confusing names is to conduct memory and perception experiments on a set of drug names which would include both the new name and a set of control names (e.g., names already on the market). The experiment would yield an observed error rate for every name. Inferences about the acceptability of the new name can be made by comparing the error rate of the new name to the distribution of error rates of the control names. We describe four memory and perception experiments on drug names, carried out using clinicians as participants. Each experiment included drug names designated as test and control names. We demonstrate how to use a combination of logistic regression, Poisson prediction limits, and highly assured credible intervals to identify and apply a threshold for identifying unacceptably confusing names. Our models show an excellent fit to the data. These experimental designs and analytic methods should be useful in the preapproval testing of proposed new drug names and in similar regulatory scenarios where it is necessary to draw inferences about the comparative safety or effectiveness of new vs. old products.

Likelihood-Based Random-Effect Meta-Analysis of Binary Events.

Meta-analysis has been used extensively for evaluation of efficacy and safety of medical interventions. Its advantages and utilities are well known. However, recent studies have raised questions about the accuracy of the commonly used moment-based meta-analytic methods in general and for rare binary outcomes in particular. The issue is further complicated for studies with heterogeneous effect sizes. Likelihood-based mixed-effects modeling provides an alternative to moment-based methods such as inverse-variance weighted fixed- and random-effects estimators. In this article, we compare and contrast different mixed-effect modeling strategies in the context of meta-analysis. Their performance in estimation and testing of overall effect and heterogeneity are evaluated when combining results from studies with a binary outcome. Models that allow heterogeneity in both baseline rate and treatment effect across studies have low type I and type II error rates, and their estimates are the least biased among the models considered.

Optimal Design and Purposeful Sampling: Complementary Methodologies for Implementation Research.

Optimal design has been an under-utilized methodology. However, it has significant real-world applications, particularly in mixed methods implementation research. We review the concept and demonstrate how it can be used to assess the sensitivity of design decisions and balance competing needs. For observational studies, this methodology enables selection of the most informative study units. For experimental studies, it entails selecting and assigning study units to intervention conditions in the most informative manner. We blend optimal design methods with purposeful sampling to show how these two concepts balance competing needs when there are multiple study aims, a common situation in implementation research.

Sample size determination for longitudinal designs with binary response.

In this article, we develop appropriate statistical methods for determining the required sample size while comparing the efficacy of an intervention to a control with repeated binary response outcomes. Our proposed methodology incorporates the complexity of the hierarchical nature of underlying designs and provides solutions when varying attrition rates are present over time. We explore how the between-subject variability and attrition rates jointly influence the computation of sample size formula. Our procedure also shows how efficient estimation methods play a crucial role in power analysis. A practical guideline is provided when information regarding individual variance component is unavailable. The validity of our methods is established by extensive simulation studies. Results are illustrated with the help of two randomized clinical trials in the areas of contraception and insomnia.

A novel biomarker selection method using multimodal neuroimaging data.

Identifying biomarkers is essential to obtain the optimal therapeutic benefit while treating patients with late-life depression (LLD). We compare LLD patients with healthy controls (HC) using resting-state functional magnetic resonance and diffusion tensor imaging data to identify neuroimaging biomarkers that may be potentially associated with the underlying pathophysiology of LLD. We implement a Bayesian multimodal local false discovery rate approach for functional connectivity, borrowing strength from structural connectivity to identify disrupted functional connectivity of LLD compared to HC. In the Bayesian framework, we develop an algorithm to control the overall false discovery rate of our findings. We compare our findings with the literature and show that our approach can better detect some regions never discovered before for LLD patients. The Hub of our discovery related to various neurobehavioral disorders can be used to develop behavioral interventions to treat LLD patients who do not respond to antidepressants.

DNA methylation/demethylation network expression in psychotic patients with a history of alcohol abuse.

BACKGROUND: Recent studies suggest that protracted and excessive alcohol use induces an epigenetic dysregulation in human and rodent brains. We recently reported that DNA methylation dynamics are altered in brains of psychotic (PS) patients, including schizophrenia and bipolar disorder patients. Because PS patients are often comorbid with chronic alcohol abuse, we examined whether the altered expression of multiple members of the DNA methylation/demethylation network observed in postmortem brains of PS patients was modified in PS patients with a history of chronic alcohol abuse. METHODS: DNA-methyltransferase-1 (DNMT1) mRNA-positive neurons were counted in situ in prefrontal cortex samples obtained from the Harvard Brain Tissue Resource Center, Belmont, MA. 10-11-translocation (TETs 1, 2, 3), apolipoprotein B editing complex enzyme (APOBEC-3C), growth and DNA-damage-inducible protein 45ß (GADD45ß), and methyl-binding domain protein-4 (MBD4) mRNAs were measured by quantitative real-time polymerase chain reaction in inferior parietal cortical lobule samples obtained from the Stanley Foundation Neuropathology Consortium, Bethesda, MD. RESULTS: We observed an increase in DNMT1 mRNA-positive neurons in PS patients compared with non-PS subjects. In addition, there was a pronounced decrease in APOBEC-3C and a pronounced increase in GADD45ß and TET1 mRNAs in PS patients with no history of alcohol abuse. In PS patients with a history of chronic alcohol abuse, the numbers of DNMT1-positive neurons were not increased significantly. Furthermore, the decrease in APOBEC-3C mRNA was less pronounced, while the increase in TET1 mRNA had a tendency to be potentiated in those PS patients that were chronic alcohol abusers. GADD45ß and MBD4 mRNAs were not influenced by alcohol abuse. The effect of chronic alcohol abuse on DNA methylation/demethylation network enzymes cannot be attributed to confounding demographic variables or to the type and dose of medication used. CONCLUSIONS: Based on these results, we hypothesize that PS patients may abuse alcohol as a potential attempt at self-medication to normalize altered DNA methylation/demethylation network pathways. However, before accepting this conclusion, we need to study alterations in the DNA methylation/demethylation pathways and the DNA methylation dynamics in a substantial number of alcoholic PS and non-PS patients. Additional investigation may also be necessary to determine whether the altered DNA methylation dynamics are direct or the consequence of an indirect interaction of alcohol with the neuropathogenetic mechanisms underlying psychosis.

Sample size determination for clustered count data.

We consider the problem of sample size determination for count data. Such data arise naturally in the context of multicenter (or cluster) randomized clinical trials, where patients are nested within research centers. We consider cluster-specific and population-averaged estimators (maximum likelihood based on generalized mixed-effect regression and generalized estimating equations, respectively) for subject-level and cluster-level randomized designs, respectively. We provide simple expressions for calculating the number of clusters when comparing event rates of two groups in cross-sectional studies. The expressions we derive have closed-form solutions and are based on either between-cluster variation or intercluster correlation for cross-sectional studies. We provide both theoretical and numerical comparisons of our methods with other existing methods. We specifically show that the performance of the proposed method is better for subject-level randomized designs, whereas the comparative performance depends on the rate ratio for the cluster-level randomized designs. We also provide a versatile method for longitudinal studies. Three real data examples illustrate the results.

Development of a Bayesian multimodal model to detect biomarkers in neuroimaging studies.

In this article, we developed a Bayesian multimodal model to detect biomarkers (or neuromarkers) using resting-state functional and structural data while comparing a late-life depression group with a healthy control group. Biomarker detection helps determine a target for treatment intervention to get the optimal therapeutic benefit for treatment-resistant patients. The borrowing strength of the structural connectivity has been quantified for functional activity while detecting the biomarker. In the biomarker searching process, thousands of hypotheses are generated and tested simultaneously using our novel method to control the false discovery rate for small samples. Several existing statistical approaches, frequently used in analyzing neuroimaging data have been investigated and compared via simulation with the proposed approach to show its excellent performance. Results are illustrated with a live data set generated in a late-life depression study. The role of detected biomarkers in terms of cognitive function has been explored.