Family-wise Error Research Articles

Gray Matter Volumes Mediate the Relationship Between Disease Duration and Balance Control Performance in Chronic Ankle Instability.

The relationship between structural changes in the cerebral gray matter and diminished balance control performance in patients with chronic ankle instability (CAI) has remained unclear. This paper aimed to assess the difference in gray matter volume (GMV) between participants with CAI and healthy controls (HC) and to characterize the role of GMV in the relationship between disease duration and balance performance in CAI. 42 participants with CAI and 33 HC completed the structural brain MRI scans, one-legged standing test, and Y-balance test. Regional GMV was measured by applying voxel-based morphometry methods. The result showed that, compared with HC, participants with CAI exhibited lower GMV in multiple brain regions (familywise error [FWE] corrected p < 0.021). Within CAI only, but not in HC, lower GMV in the thalamus (β = -0.53, p = 0.003) and hippocampus (β = -0.57, p = 0.001) was associated with faster sway velocity of the center of pressure (CoP) in eyes closed condition (i.e., worse balance control performance). The GMV in the thalamus (percentage mediated [PM] = 32.02%; indirect effect β = 0.119, 95% CI = 0.003 to 0.282) and hippocampus (PM = 33.71%; indirect effect β = 0.122, 95% CI = 0.005 to 0.278) significantly mediated the association between the disease duration and balance performance. These findings suggest that the structural characteristics of the supraspinal elements is critical to the maintenance of balance control performance in individuals suffering from CAI, which deserve careful consideration in the management and rehabilitation programs in this population.

Metabolites Link Intake of a Healthy Diet to Better Insulin and Glucose Homeostasis in the Microbiome and Insulin Longitudinal Evaluation Study

BackgroundDietary quality has been linked to better glycemic control, but the precise molecular mechanisms giving rise to these associations are not fully understood. ObjectivesTo examine the association of metabolites associated with the intake of a healthy diet with measures of insulin/glucose homeostasis. MethodsUsing cross-sectional data from 295 United States adults, the associations between 3 diet pattern scores and metabolome-wide metabolites were estimated via linear regression models, which controlled for demographic factors and health behaviors. Subsequently, the associations between the diet-related metabolites with 6 measures of glucose/insulin homeostasis were examined in similar models. A Bonferroni correction was applied to control the familywise error rate at 5%. ResultsFifty-five metabolites were significantly associated with ≥1 diet score (all P < 1.7∗10–5). When these were summed into each of the 3 diet-specific metabolite summary scores, all 3 aggregate measures showed strong associations with 5 out of 6 measures of glucose/insulin homeostasis (P = 9.7∗10–5–4.1∗10–13). ConclusionsAdherence to a priori-defined “healthy diet” is associated with the plasma metabolites that, in turn, are associated with better glycemia. If the associations between replicated in future studies and examined using large-scale longitudinal data, the identified molecules could yield insights into mechanisms by which diet may support glucose and insulin homeostasis.

Extent of Myometrial Resection With Various Surgical Methods for Endometrial Polypectomy Procedures

OBJECTIVE: To assess whether the frequency and extent of myometrial resection differs among surgical methods commonly used for endometrial polypectomy. METHODS: We conducted a retrospective cohort study of pathology samples from polypectomy procedures performed on patients 18–50 years of age. Samples were reevaluated by a blinded pathologist to assess the following primary outcome measures: presence and percentage of myometrium on the pathology sample, prevalence of isolated myometrium, and depth of myometrial resection. Data were evaluated using Fisher exact test and Kruskal-Wallis test, followed by multiple comparisons analysis. To maintain a familywise error rate of 5% across all four primary analyses, the Bonferroni correction method was applied. RESULTS: Of 458 pathology samples, 21.8% were obtained using hysteroscopic morcellators, 11.1% were obtained with hysteroscopic scissors, and 67.0% were obtained with hysteroscopy with dilation and curettage (D&amp;C). Hysteroscopic morcellation demonstrated a higher prevalence of myometrium (58.0% vs 9.8% and 15.3%, for hysteroscopic scissors and hysteroscopy with D&amp;C, respectively; P&lt;.001), a larger percentage of pathology samples with more than 25% myometrium (26.0% vs 4.0% and 0.6%, respectively; P&lt;.001), and a higher prevalence of isolated myometrium compared with hysteroscopy with D&amp;C (11.0% vs 0.7%; P&lt;.001). CONCLUSION: The presence and proportion of myometrium in polypectomy samples obtained using hysteroscopic morcellators was significantly higher compared with hysteroscopic scissors and hysteroscopy with D&amp;C.

Surgical control time estimation variability: Implications for medical systems and the future integration of AI and ML models

BackgroundAccurate estimation of surgical procedure times, crucial for optimizing healthcare access, patient outcomes, and cost-effectiveness, is essential for operating room efficiency. Surgical control time (SCT) is a preoperative estimate by surgeons representing their predicted time to complete the surgery, spanning from completion of anesthesia induction to surgical site closure. MethodsIn this within-subjects, longitudinal study, we examined the differences between predicted surgical control times versus actual SCTs and determined variability by surgical specialty. We included cases regardless of classification (i.e., outpatient or inpatient), type of surgery (i.e., elective, urgent, or emergent), or level of complexity (i.e., major or minor). We ran Shapiro–Wilk tests to assess the normality of the difference in actual versus predicted surgical control times (dSCT) by surgical specialty. We used a generalized linear model (GLM) with robust clustered variance and pairwise comparisons of surgical specialties (with Bonferroni adjustment for family-wise error rate) to assess differences in the prediction accuracy of SCTs by specialty. ResultsWe analyzed 14,438 surgical cases performed by 168 surgeons across 13 specialties from January 2019 to January 2023. 11 of 13 specialties had higher actual than predicted times, suggesting an overall pattern of underestimating SCTs. On average, surgeries took 12.3 % longer than predicted, with surgeons underestimating SCTs by an average of 10.4 min. SCTs comprised 78 % of the total operative time. The four specialties with the largest underestimations of SCTs were neurosurgery (27.04 min), orthopedics (22.75 min), urology (19.4 min, and plastic surgery (18.67 min), while two specialties exhibited overestimations, namely ear nose and throat (11.14 min) and pediatrics (–3.21 min). GLM results and pairwise comparisons showed that surgeons significantly differed in their SCT prediction by surgical specialty. ConclusionsOur findings showed significant differences across surgical specialties in the accuracy of predicting surgical control times. These results have implications for integrating evolving technologies such as artificial intelligence and machine learning models to assist surgical administrators in accurately predicting surgical case durations and optimizing resource allocation.

Neurocognitive Dysfunction After Short (

BackgroundIt has long been held that the safe duration of hypothermic circulatory arrest (HCA) is at least 25 to 30 minutes. However, this belief is based primarily on clinical outcomes research and has not been systematically investigated using more sensitive brain imaging and neurocognitive assessments. MethodsThis exploratory substudy of the randomized Cognitive Effects of Body Temperature During Hypothermic Circulatory Arrest (GOT ICE) trial, which compared outcomes for deep vs moderate hypothermia during aortic arch surgery, investigated the frequency of neurocognitive and structural and functional magnetic resonance imaging (MRI) deficits with HCA of short (<20 minutes) duration. Neurocognitive deficit was defined as ≥1 SD decline in ≥1 of 5 cognitive domains on neurocognitive testing. ResultsOf 228 GOT ICE participants with complete 4-week cognitive data, 74.6% (n = 170 of 228) had HCA durations <20 minutes, including 59 patients randomized to deep hypothermia (<20.0 °C), 55 patients randomized to low-moderate (20.1-24.0 °C) hypothermia, and 56 randomized to high-moderate (24.1-28.0 °C) hypothermia. Of these participants, cognitive deficit was detected 4 weeks postoperatively in ∼40% of patients in all 3 groups (deep hypothermia, 22 of 59 [37.3%]; low-moderate hypothermia, 23 of 55 [41.8%]; and high-moderate hypothermia, 24 of 56 [42.9%]). Furthermore, in a subset of patients with complete MRI data (n = 43), baseline to 4-week postoperative right frontal lobe functional connectivity change was inversely associated with HCA duration (range, 8-17 minutes; P for familywise error rate < .01). ConclusionsEven short durations of HCA result in cognitive deficits in ∼40% of patients, independent of systemic hypothermia temperature. HCA duration was inversely associated with frontal lobe functional MRI connectivity, a finding suggesting that this brain region may be preferentially sensitive to HCA. Surgeons should be aware that even short durations of HCA may not provide complete neuroprotection after aortic arch surgery.

A general consonance principle for closure tests based on -values.

The closure principle is a powerful approach to constructing efficient testing procedures controlling the familywise error rate in the strong sense. For small numbers of hypotheses and the setting of independent elementary -values we consider closed tests where each intersection hypothesis is tested with a -value combination test. Examples of such combination tests are the Fisher combination test, the Stouffer test, the Omnibus test, the truncated test, or the Wilson test. Some of these tests, such as the Fisher combination, the Stouffer, or the Omnibus test, are not consonant and rejection of the global null hypothesis does not always lead to rejection of at least one elementary null hypothesis. We develop a general principle to uniformly improve closed tests based on -value combination tests by modifying the rejection regions such that the new procedure becomes consonant. For the Fisher combination test and the Stouffer test, we show by simulations that this improvement can lead to a substantial increase in power.

Comparison of approaches for local testing with functional test statistics

Several new methods have been recently developed for the problem of domain selection when the test statistic is functional. That is, the aim is to distinguish between the two domains where the tested hypothesis should be rejected and where it should not. This work aims to compare these new methods through an extensive simulation study that consists of 1584 scenarios. The error rate, consisting of type I and type II errors, was chosen as a measure to compare the methods, regarding the two types of errors equally important. The false discovery rate methods consistently showed the best performance of all methods. When higher importance is given to type I error rate, the family-wise error rate methods should be preferred. From these methods, the global envelope test performed the best for smoother functions. None of the tested methods was consistently the best for very wiggly functions.

Spontaneous Brain Activity Abnormalities in Patients With Temporal Lobe Epilepsy: A Meta-Analysis of 1474 Patients.

Abnormalities in resting-state functional brain activity have been detected in patients with temporal lobe epilepsy (TLE). The results of individual neuroimaging studies of TLE, however, are frequently inconsistent due to small and heterogeneous samples, analytical flexibility, and publication bias toward positive findings. To investigate the most consistent regions of resting-state functional brain activity abnormality in patients with TLE through a quantitative meta-analysis of published neuroimaging data. Meta-analysis. Exactly 1474 TLE patients (716 males and 758 females) from 31 studies on resting-state functional brain activity were included in this study. Studies utilizing 1.5 T or 3 T MR scanners were included for meta-analysis. Resting-state functional MRI using gradient echo-planar imaging, T1-weighted imaging. PubMed, Web of Science, Chinese National Knowledge Infrastructure, and WanFang databases were searched to identify studies investigating amplitude of low-frequency fluctuation (ALFF), fractional ALFF (fALFF), and regional homogeneity (ReHo) at the whole-brain level between patients with TLE and healthy controls (HCs). Seed-based d Mapping with Permutation of Subject Images, standard randomization tests and meta-regression analysis were used. Results were significant if P < 0.05 with family-wise error corrected. Patients with TLE displayed resting-state functional brain activity which was a significant increase in the right hippocampus, and significant decrease in the right angular gurus and right precuneus. Additionally, the meta-regression analysis demonstrated that age (P = 0.231), sex distribution (P = 0.376), and illness duration (P = 0.184), did not show significant associations with resting state functional brain activity in patients with TLE. Common alteration patterns of spontaneous brain activity were identified in the right hippocampus and default-model network regions in patients with TLE. These findings may contribute to understanding of the underlying mechanism for potentially effective intervention of TLE. Stage 2.

No significant alteration in white matter microstructure in first-degree relatives of patients with obsessive-compulsive disorder

Obsessive-compulsive disorder (OCD) is characterized by structural alteration within white matter tissues of cortico-striato-thalamo-cortical, temporal and occipital circuits. However, the presence of microstructural changes in the white matter tracts of unaffected first-degree relatives of patients with OCD as a vulnerability marker remains unclear. Therefore, here, diffusion-tensor magnetic resonance imaging (DTI) data were obtained from 29 first-degree relatives of patients with OCD and 59 healthy controls. We investigated the group differences in FA using whole-brain analysis (DTI analysis). For additional regions of interest (ROI) analysis, we focused on the posterior thalamic radiation and sagittal stratum, shown in recent meta-analysis of patients with OCD. In both whole-brain and ROI analyses, using a strict statistical threshold (family-wise error rate [FWE] corrected p<.05 for whole-brain analyses, and p<.0125 (0.05/4) with Bonferroni correction for ROI analyses), we found no significant group differences in FA. Subtle reductions were observed in the anterior corona radiata, forceps minor, cingulum bundle, and corpus callosum only when a lenient statistical was applied (FWE corrected p<.20). These findings suggest that alterations in the white matter microstructure of first-degree relatives, as potential vulnerability markers for OCD, are likely subtle.

Topological properties of white matter network in first untreated children and adolescents with obsessive-compulsive disorder

Objective: To investigate the topological properties of the white matter network in the drug-naive first-episode children and adolescent with obsessive-compulsive disorder (OCD). Methods: This study was a case-control study. First-episode OCD childhren and adolescents(OCD group) who were treated in the outpatient or inpatient department of the Second Affiliated Hospital of Xinxiang Medical University from July 2018 to October 2023 were collected as the research subjects. Healthy controls (control group)matched by gender, age and education level were used as controls. Deterministic tractography technique was used to construct the whole brain white matter structural network, and graph theory analysis method was used to analyze the topological attributes of the whole brain white matter structure network in OCD children and adolescents. A network-based statistical method was used to examine the inter-group differences in the functional connectivity strength of the whole brain network. Results: Finally, 31 cases were included in the obsessive-compulsive disorder group, including 22 males and 9 females, with an age of (13.5±1.6) years; There were 34 cases in the control group, including 22 males and 12 females, with an age of (12.7±1.4) years. The global efficiency and local efficiency of the OCD group (0.62±0.03, 0.70±0.07) were significantly higher than those of the control group (0.50±0.06, 0.54±0.21) [both P<0.01, false discovery rate(FDR)correction]; while the characteristic path length (1.77±0.08) was significantly smaller than that of the control group (2.10±0.23) (P<0.01, FDR correction).The centrality comparison of nodal betweenness centrality showed that in the OCD group, the connections were enhanced in the left lateral orbitofrontal gyrus, right dorsal agranular insula, left dorsal granular insula, right granular insula, right posterior central gyrus main area of parietal lobe, left ventral granular insula, granular insula, left ventral granulosa, left granular insula, and left dorsal agranular insula [all P<0.001, family wise error (FWE) correction], while the connection of right thalamic was weakened (P<0.001, FWE correction), There were sub-networks characterized by significantly enhanced connection strength of relevant nodes in subcortical, visual network, and default mode network (P<0.05, permutation test 5 000 times). Conclusions: The topological properties of the brain's functional network in children and adolescents with OCD exhibit abnormalities, indicating an immature state of brain functional connectivity.

Precision fMRI and cluster-failure in the individual brain.

Advances in neuroimaging acquisition protocols and denoising techniques, along with increasing magnetic field strengths, have dramatically improved the temporal signal-to-noise ratio (tSNR) in functional magnetic resonance imaging (fMRI). This permits spatial resolution with submillimeter voxel sizes and ultrahigh temporal resolution and opens a route toward performing precision fMRI in the brains of individuals. Yet ultrahigh spatial and temporal resolution comes at a cost: it reduces tSNR and, therefore, the sensitivity to the blood oxygen level-dependent (BOLD) effect and other functional contrasts across the brain. Here we investigate the potential of various smoothing filters to improve BOLD sensitivity while preserving the spatial accuracy of activated clusters in single-subject analysis. We introduce adaptive-weight smoothing with optimized metrics (AWSOM), which addresses this challenge extremely well. AWSOM employs a local inference approach that is as sensitive as cluster-corrected inference of data smoothed with large Gaussian kernels, but it preserves spatial details across multiple tSNR levels. This is essential for examining whole-brain fMRI data because tSNR varies across the entire brain, depending on the distance of a brain region from the receiver coil, the type of setup, acquisition protocol, preprocessing, and resolution. We found that cluster correction in single subjects results in inflated family-wise error and false positive rates. AWSOM effectively suppresses false positives while remaining sensitive even to small clusters of activated voxels. Furthermore, it preserves signal integrity, that is, the relative activation strength of significant voxels, making it a valuable asset for a wide range of fMRI applications. Here we demonstrate these features and make AWSOM freely available to the research community for download.

Dementia with Lewy bodies and gait neural basis: a cross-sectional study

BackgroundDementia with Lewy Bodies (DLB) is responsible for cognitive-behavioural disorders but also for gait disorders. The latter are thought to be related to parkinsonism, but the neural bases of these disorders are not well known, especially in the early stages. The aim of this study was to investigate by volumetric Magnetic Resonance Imaging the neuronal basis of gait disorders in DLB patients, compared to Healthy Elderly Controls and Alzheimer’s Disease patients.MethodsClinical examination with motor assessment including 10-meter walking speed, one-leg balance and Timed Up and Go test, a comprehensive neuropsychological evaluation and 3D brain Magnetic Resonance Imaging were performed on 84 DLB patients, 39 Alzheimer’s Disease patients and 22 Healthy Elderly Controls. We used Statistical Parametric Mapping 12 to perform a one-sample t-test to investigate the correlation between each gait score and gray matter volume (P ≤ 0.05 corrected for family-wise error).ResultsWe found a correlation for DLB patients between walking speed and gray matter decrease (P < 0.05, corrected for family-wise error) in caudate nuclei, anterior cingulate cortex, mid-cingulate cortex, hippocampi, supplementary motor area, right cerebellar cortex and left parietal operculum. We found no correlation with Timed Up and Go test and one-leg balance.ConclusionGait disorders are underpinned by certain classical regions such as the cerebellum and the supplementary motor area. Our results suggest there may be a motivational and emotional component of voluntary gait in DLB subjects, underpinned by the cingulate cortex, a spatial orientation component, underpinned by hippocampi and suggest the involvement of brain processing speed and parkinsonism, underpinned by the caudate nuclei.Trial registrationThe study protocol has been registered on ClinicalTrials.gov. (NCT01876459) on June 12, 2013.

Association Between Hypothalamic Volume and Metabolism, Cognition, and Behavior in Patients With Amyotrophic Lateral Sclerosis.

Dysfunction of energy metabolism, cognition, and behavior are important nonmotor symptoms of amyotrophic lateral sclerosis (ALS), negatively affecting survival and quality of life, but poorly understood. Neuroimaging is ideally suited to studying nonmotor neurodegeneration in ALS, but few studies have focused on the hypothalamus, a key region for regulating energy homeostasis, cognition, and behavior. We evaluated, therefore, hypothalamic neurodegeneration in ALS and explored the relationship between hypothalamic volumes and dysregulation of energy metabolism, cognitive and behavioral changes, disease progression, and survival. Patients with ALS and population-based controls were included for this cross-sectional and longitudinal MRI study. The hypothalamus was segmented into 5 subregions and their volumes were calculated. Linear (mixed) models, adjusted for age, sex, and total intracranial volume, were used to compare hypothalamic volumes between groups and to analyze associations with metabolism, cognition, behavior, and disease progression. Cox proportional hazard models were used to investigate the relationship of hypothalamic volumes with survival. Permutation-based corrections for multiple hypothesis testing were applied to all analyses to control the family-wise error rate. Data were available for 564 patients with ALS and 356 controls. The volume of the anterior superior subregion of the hypothalamus was smaller in patients with ALS than in controls (β = -0.70 [-1.15 to -0.25], p = 0.013). Weight loss, memory impairments, and behavioral disinhibition were associated with a smaller posterior hypothalamus (β = -4.79 [-8.39 to -2.49], p = 0.001, β = -10.14 [-15.88 to -4.39], p = 0.004, and β = -12.09 [-18.83 to -5.35], p = 0.003, respectively). Furthermore, the volume of this subregion decreased faster over time in patients than in controls (β = -0.25 [0.42 to -0.09], p = 0.013), and a smaller volume of this structure was correlated with shorter survival (hazard ratio = 0.36 [0.21-0.61], p = 0.029). We obtained evidence for hypothalamic involvement in ALS, specifically marked by atrophy of the anterior superior subregion. Moreover, we found that atrophy of the posterior hypothalamus was associated with weight loss, memory dysfunction, behavioral disinhibition, and survival, and that this subregion deteriorated faster in patients with ALS than in controls. These findings improve our understanding of nonmotor involvement in ALS and could contribute to the identification of new treatment targets for this devastating disease.

Addressing researcher degrees of freedom through minP adjustment

When different researchers study the same research question using the same dataset they may obtain different and potentially even conflicting results. This is because there is often substantial flexibility in researchers’ analytical choices, an issue also referred to as “researcher degrees of freedom”. Combined with selective reporting of the smallest p-value or largest effect, researcher degrees of freedom may lead to an increased rate of false positive and overoptimistic results. In this paper, we address this issue by formalizing the multiplicity of analysis strategies as a multiple testing problem. As the test statistics of different analysis strategies are usually highly dependent, a naive approach such as the Bonferroni correction is inappropriate because it leads to an unacceptable loss of power. Instead, we propose using the “minP” adjustment method, which takes potential test dependencies into account and approximates the underlying null distribution of the minimal p-value through a permutation-based procedure. This procedure is known to achieve more power than simpler approaches while ensuring a weak control of the family-wise error rate. We illustrate our approach for addressing researcher degrees of freedom by applying it to a study on the impact of perioperative paO2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$paO_2$$\\end{document} on post-operative complications after neurosurgery. A total of 48 analysis strategies are considered and adjusted using the minP procedure. This approach allows to selectively report the result of the analysis strategy yielding the most convincing evidence, while controlling the type 1 error—and thus the risk of publishing false positive results that may not be replicable.

Multiple testing of interval composite null hypotheses using randomized p-values

Equivalence tests are statistical hypothesis testing procedures that aim to establish practical equivalence rather than the usual statistical significant difference. These testing procedures are frequent in “bioequivalence studies," where one would wish to show that, for example, an existing drug and a new one under development have comparable therapeutic effects. In this article, we propose a two-stage randomized (RAND2) p-value that depends on a uniformly most powerful (UMP) p-value and an arbitrary tuning parameter c∈[0,1]\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$c\\in [0,1]$$\\end{document} for testing an interval composite null hypothesis. We investigate the behavior of the distribution function of the two p-values under the null hypothesis and alternative hypothesis for a fixed significance level t∈(0,1)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$t\\in (0,1)$$\\end{document} and varying sample sizes. We evaluate the performance of the two p-values in estimating the proportion of true null hypotheses in multiple testing. We conduct a family-wise error rate control using an adaptive Bonferroni procedure with a plug-in estimator to account for the multiplicity that arises from our multiple hypotheses under consideration. The various claims in this research are verified using a simulation study and real-world data analysis.

The benefits of permutation-based genome-wide association studies.

Linear mixed models (LMMs) are a commonly used method for genome-wide association studies (GWAS) that aim to detect associations between genetic markers and phenotypic measurements in a population of individuals while accounting for population structure and cryptic relatedness. In a standard GWAS, hundreds of thousands to millions of statistical tests are performed, requiring control for multiple hypothesis testing. Typically, static corrections that penalize the number of tests performed are used to control for the family-wise error rate, which is the probability of making at least one false positive. However, it has been shown that in practice this threshold is too conservative for normally distributed phenotypes and not stringent enough for non-normally distributed phenotypes. Therefore, permutation-based LMM approaches have recently been proposed to provide a more realistic threshold that takes phenotypic distributions into account. In this work, we discuss the advantages of permutation-based GWAS approaches, including new simulations and results from a re-analysis of all publicly available Arabidopsis phenotypes from the AraPheno database.

Eigenvector Centrality Mapping Reveals Volatility of Functional Brain Dynamics in Anti-NMDA Receptor Encephalitis

BackgroundAnti-NMDA receptor encephalitis (NMDARE) causes long-lasting cognitive deficits associated with altered functional connectivity. Eigenvector centrality (EC) mapping represents a powerful new method for data-driven voxelwise and time-resolved estimation of network importance—beyond changes in classical static functional connectivity. MethodsTo assess changes in functional brain network organization, we applied EC mapping in 73 patients with NMDARE and 73 matched healthy control participants. Areas with significant group differences were further investigated using 1) spatial clustering analyses, 2) time series correlation to assess synchronicity between the hippocampus and cortical brain regions, and 3) correlation with cognitive and clinical parameters. ResultsDynamic, time-resolved EC showed significantly higher variability in 13 cortical areas (familywise error p < .05) in patients with NMDARE compared with healthy control participants. Areas with dynamic EC group differences were spatially organized in centrality clusters resembling resting-state networks. Importantly, variability of dynamic EC in the frontotemporal cluster was associated with impaired verbal episodic memory in patients (r = −0.25, p = .037). EC synchronicity between the hippocampus and the medial prefrontal cortex was reduced in patients compared with healthy control participants (familywise error p < .05, tmax = 3.76) and associated with verbal episodic memory in patients (r = 0.28, p = .019). Static EC analyses showed group differences in only one brain region (left intracalcarine cortex). ConclusionsWidespread changes in network dynamics and reduced hippocampal-medial prefrontal synchronicity were associated with verbal episodic memory deficits and may thus represent a functional neural correlate of cognitive dysfunction in NMDARE. Importantly, dynamic EC detected substantially more network alterations than traditional static approaches, highlighting the potential of this method to explain long-term deficits in NMDARE.

Summary statistics knockoffs inference with family-wise error rate control.

Testing multiple hypotheses of conditional independence with provable error rate control is a fundamental problem with various applications. To infer conditional independence with family-wise error rate (FWER) control when only summary statistics of marginal dependence are accessible, we adopt GhostKnockoff to directly generate knockoff copies of summary statistics and propose a new filter to select features conditionally dependent on the response. In addition, we develop a computationally efficient algorithm to greatly reduce the computational cost of knockoff copies generation without sacrificing power and FWER control. Experiments on simulated data and a real dataset of Alzheimer's disease genetics demonstrate the advantage of the proposed method over existing alternatives in both statistical power and computational efficiency.

Simultaneous Inference of Multiple Binary Endpoints in Biomedical Research: Small Sample Properties of Multiple Marginal Models and a Resampling Approach.

In biomedical research, the simultaneous inference of multiple binary endpoints may be of interest. In such cases, an appropriate multiplicity adjustment is required that controls the family-wise error rate, which represents the probability of making incorrect test decisions. In this paper, we investigate two approaches that perform single-step -value adjustments that also take into account the possible correlation between endpoints. A rather novel and flexible approach known as multiple marginal models is considered, which is based on stacking of the parameter estimates of the marginal models and deriving their joint asymptotic distribution. We also investigate a nonparametric vector-based resampling approach, and we compare both approaches with the Bonferroni method by examining the family-wise error rate and power for different parameter settings, including low proportions and small sample sizes. The results show that the resampling-based approach consistently outperforms the other methods in terms of power, while still controlling the family-wise error rate. The multiple marginal models approach, on the other hand, shows a more conservative behavior. However, it offers more versatility in application, allowing for more complex models or straightforward computation of simultaneous confidence intervals. The practical application of the methods is demonstrated using a toxicological dataset from the National ToxicologyProgram.

Adjusting effective multiplicity ( ) for family-wise error rate in functional near-infrared spectroscopy data with a small sample size.

The advancement of multichannel functional near-infrared spectroscopy (fNIRS) has enabled measurements across a wide range of brain regions. This increase in multiplicity necessitates the control of family-wise errors in statistical hypothesis testing. To address this issue, the effective multiplicity ( ) method designed for channel-wise analysis, which considers the correlation between fNIRS channels, was developed. However, this method loses reliability when the sample size is smaller than the number of channels, leading to a rank deficiency in the eigenvalues of the correlation matrix and hindering the accuracy of calculations. We aimed to reevaluate the effectiveness of the method for fNIRS data with a small sample size. In experiment 1, we used resampling simulations to explore the relationship between sample size and values. Based on these results, experiment 2 employed a typical exponential model to investigate whether valid could be predicted from a small sample size. Experiment 1 revealed that the values were underestimated when the sample size was smaller than the number of channels. However, an exponential pattern was observed. Subsequently, in experiment 2, we found that valid values can be derived from sample sizes of 30 to 40 in datasets with 44 and 52 channels using a typical exponential model. The findings from these two experiments indicate the potential for the effective application of correction in fNIRS studies with sample sizes smaller than the number of channels.