Medial amygdalar tau is associated with anxiety symptoms in preclinical Alzheimer's disease.

BACKGROUND: While the amygdala receives early tau deposition in Alzheimer's disease (AD) and is involved in social and emotional processing, the relationship between amygdalar tau and early neuropsychiatric symptoms in AD is unknown. We sought to determine whether focal tau binding in the amygdala and abnormal amygdalar connectivity were detectable in a preclinical AD cohort and identify relationships between these and self-reported mood symptoms. METHODS: We examined n=598 individuals (n=347 amyloid-positive (58% female), n=251 amyloid-negative (62% female); subset into tau PET and fMRI cohorts) from the A4 Study. In our tau PET cohort, we used amygdalar segmentations to examine representative nuclei from three functional divisions of the amygdala. We analyzed between-group differences in division-specific tau binding in the amygdala in preclinical AD. We conducted seed-based functional connectivity analyses from each division in the fMRI cohort. Finally, we conducted exploratory post-hoc correlation analyses between neuroimaging biomarkers of interest and anxiety and depression scores. RESULTS: Amyloid-positive individuals demonstrated increased tau binding in medial and lateral amygdala (F(4,442)=14.61, p=0.00045; F(4,442)=5.83, p=0.024, respectively). Across amygdalar divisions, amyloid-positive individuals had relatively increased regional connectivity from amygdala to other temporal regions, insula, and orbitofrontal cortex. There was an interaction by amyloid group between tau binding in the medial and lateral amygdala and anxiety. Medial amygdala to retrosplenial connectivity negatively correlated with anxiety symptoms (rs=-0.103, p=0.015). CONCLUSIONS: Our findings suggest that preclinical tau deposition in the amygdala may result in meaningful changes in functional connectivity which may predispose patients to mood symptoms.

Topology-based Clustering of Functional Brain Networks in an Alzheimer's Disease Cohort.

Alzheimer's disease is a progressive neurodegenerative disease with many identifying biomarkers for diagnosis. However, whole-brain phenomena, particularly in functional MRI modalities, are not fully understood nor characterized. Here we employ the novel application of topological data analysis (TDA)-based methods of persistent homology to functional brain networks from ADNI-3 cohort to perform a subtyping experiment using unsupervised clustering techniques. We then investigate variations in QT-PAD challenge features across the identified clusters. Using a Wasserstein distance kernel with a variety of clustering algorithms, we found that the 0th-homology Wasserstein distance kernel and spectral clustering yielded clusters with significant differences in whole brain and medial temporal lobe (MTL) volume, thus demonstrating an intrinsic link between whole brain functional topology and brain morphometric structure. These findings demonstrate the importance of MTL in functional connectivity and the efficacy of using TDA-based machine learning methods in network neuroscience and neurodegenerative disease subtyping.

Rhizobacterial Bacillus enrichment in soil enhances smoke tree resistance to Verticillium wilt.

Verticillium wilt, caused by the soilborne fungus Verticillium dahliae, poses a serious threat to the health of more than 200 plant species worldwide. Although plant rhizosphere-associated microbiota can influence plant resistance to V. dahliae, empirical evidence underlying Verticillium wilt resistance of perennial trees is scarce. In this study, we systemically investigated the effect of the soil microbiota on the resistance of smoke trees (Cotinus coggygria) to Verticillium wilt using field, greenhouse and laboratory experiments. Comparative analysis of the soil microbiota in the two stands of smoke trees suggested that Bacillus represented the most abundant and key microbial genus related to potential disease suppression. Smoke tree seedlings were inoculated with isolated Bacillus strains, which exhibited disease suppressiveness and plant growth-promoting properties. Furthermore, repletion of Bacillus agents to disease conducive soil significantly resulted in reduced incidence of smoke tree wilt and increased resistance of the soil microbiota to V. dahliae. Finally, we explored a more effective combination of Bacillus agents with the fungicide propiconazole to combat Verticillium wilt. The results establish a foundation for the development of an effective control for this disease. Overall, this work provides a direct link between Bacillus enrichment and disease resistance of smoke trees, facilitating the development of green control strategies and measurements of soil-borne diseases.

Knowledge-guided learning methods for integrative analysis of multi-omics data.

Integrative analysis of multi-omics data has the potential to yield valuable and comprehensive insights into the molecular mechanisms underlying complex diseases such as cancer and Alzheimer's disease. However, a number of analytical challenges complicate multi-omics data integration. For instance, -omics data are usually high-dimensional, and sample sizes in multi-omics studies tend to be modest. Furthermore, when genes in an important pathway have relatively weak signal, it can be difficult to detect them individually. There is a growing body of literature on knowledge-guided learning methods that can address these challenges by incorporating biological knowledge such as functional genomics and functional proteomics into multi-omics data analysis. These methods have been shown to outperform their counterparts that do not utilize biological knowledge in tasks including prediction, feature selection, clustering, and dimension reduction. In this review, we survey recently developed methods and applications of knowledge-guided multi-omics data integration methods and discuss future research directions.

Identifying Shared Neuroanatomic Architecture between Cognitive Traits through Multiscale Morphometric Correlation Analysis.

We introduce an informative metric, called morphometric correlation, as a measure of shared neuroanatomic similarity between two cognitive traits. Traditional estimates of trait correlations can be confounded by factors beyond brain morphology. To exclude these confounding factors, we adopt a Gaussian kernel to measure the morphological similarity between individuals and compare pure neuroanatomic correlations among cognitive traits. In our empirical study, we employ a multiscale strategy. Given a set of cognitive traits, we first perform morphometric correlation analysis for each pair of traits to reveal their shared neuroanatomic correlation at the whole brain (or global) level. After that, we extend our whole brain concept to regional morphometric correlation and estimate shared neuroanatomic similarity between two cognitive traits at the regional (or local) level. Our results demonstrate that morphometric correlation can provide insights into shared neuroanatomic architecture between cognitive traits. Furthermore, we also estimate the morphometricity of each cognitive trait at both global and local levels, which can be used to better understand how neuroanatomic changes influence individuals' cognitive status.

Bayesian mixed model inference for genetic association under related samples with brain network phenotype.

Genetic association studies for brain connectivity phenotypes have gained prominence due to advances in noninvasive imaging techniques and quantitative genetics. Brain connectivity traits, characterized by network configurations and unique biological structures, present distinct challenges compared to other quantitative phenotypes. Furthermore, the presence of sample relatedness in the most imaging genetics studies limits the feasibility of adopting existing network-response modeling. In this article, we fill this gap by proposing a Bayesian network-response mixed-effect model that considers a network-variate phenotype and incorporates population structures including pedigrees and unknown sample relatedness. To accommodate the inherent topological architecture associated with the genetic contributions to the phenotype, we model the effect components via a set of effect network configurations and impose an inter-network sparsity and intra-network shrinkage to dissect the phenotypic network configurations affected by the risk genetic variant. A Markov chain Monte Carlo (MCMC) algorithm is further developed to facilitate uncertainty quantification. We evaluate the performance of our model through extensive simulations. By further applying the method to study, the genetic bases for brain structural connectivity using data from the Human Connectome Project with excessive family structures, we obtain plausible and interpretable results. Beyond brain connectivity genetic studies, our proposed model also provides a general linear mixed-effect regression framework for network-variate outcomes.

Comparison of clinical outcomes of supercapsular percutaneously-assisted approach total hip arthroplasty versus conventional posterior approach for total hip arthroplasty in adults: a systematic review and meta-analysis.

OBJECTIVE: This meta-analysis was aimed to compare the postoperative clinical outcomes between the supercapsular percutaneously assisted total hip (SuperPATH, SP) and conventional posterior/posterolateral approach (PA) for total hip arthroplasty in patients who have failed conservative treatment for hip-related disorders. METHODS: PRISMAP guidelines were followed in this systematic review. CNKI, Wanfang, PubMed, Embase, Cochrane, Web of Science databases and the reference list grey literature were searched for studies according to the search strategy. Endnote (version 20) was used to screen the searched studies according to the inclusion and exclusion criterias and extract the data from the eligible studied. RR and 95% CI were used for dichotomous variables and MD and 95% CI were used for continuous variables. All analyses and heterogeneity of outcomes were analysed by Review Manage (version 5.4). Publication bias of included studies was analysed by Stata (version 16.0). RESULTS: Thirty-six randomized control studies were included. Compared to PA group, SP group had a shorter incision length, less intraoperative blood loss, a shorter length of hospital stay and do activities earlier. Hip function (HHS) was significantly improved within three months postoperatively. Pain of hip (VAS) was significantly reduced within one month postoperatively. The state of daily living (BI) was significantly improved within three months. Patients' overall health status (SF-36) improved significantly postoperatively. There was no difference in postoperative complications between the two approaches. PA had a shorter operative time and a higher accuracy of prosthesis placement. CONCLUSION: The advantages of SuperPATH include accelerated functional recovery and less trauma associated with surgery. However, it required a longer operative time and implantation of the prosthesis was less accurate than that of PA.

Clinical characteristics and outcomes of patients with post-stroke epilepsy: protocol for an individual patient data meta-analysis from the International Post-stroke Epilepsy Research Repository (IPSERR).

INTRODUCTION: Despite significant advances in managing acute stroke and reducing stroke mortality, preventing complications like post-stroke epilepsy (PSE) has seen limited progress. PSE research has been scattered worldwide with varying methodologies and data reporting. To address this, we established the International Post-stroke Epilepsy Research Consortium (IPSERC) to integrate global PSE research efforts. This protocol outlines an individual patient data meta-analysis (IPD-MA) to determine outcomes in patients with post-stroke seizures (PSS) and develop/validate PSE prediction models, comparing them with existing models. This protocol informs about creating the International Post-stroke Epilepsy Research Repository (IPSERR) to support future collaborative research. METHODS AND ANALYSIS: We utilised a comprehensive search strategy and searched MEDLINE, Embase, PsycInfo, Cochrane, and Web of Science databases until 30 January 2023. We extracted observational studies of stroke patients aged ≥18 years, presenting early or late PSS with data on patient outcome measures, and conducted the risk of bias assessment. We did not apply any restriction based on the date or language of publication. We will invite these study authors and the IPSERC collaborators to contribute IPD to IPSERR. We will review the IPD lodged within IPSERR to identify patients who developed epileptic seizures and those who did not. We will merge the IPD files of individual data and standardise the variables where possible for consistency. We will conduct an IPD-MA to estimate the prognostic value of clinical characteristics in predicting PSE. ETHICS AND DISSEMINATION: Ethics approval is not required for this study. The results will be published in peer-reviewed journals. This study will contribute to IPSERR, which will be available to researchers for future PSE research projects. It will also serve as a platform to anchor future clinical trials. TRIAL REGISTRATION NUMBER: NCT06108102.

Genetic underpinnings of brain structural connectome for young adults.

With distinct advantages in power over behavioral phenotypes, brain imaging traits have become emerging endophenotypes to dissect molecular contributions to behaviors and neuropsychiatric illnesses. Among different imaging features, brain structural connectivity (i.e., structural connectome) which summarizes the anatomical connections between different brain regions is one of the most cutting edge while under-investigated traits; and the genetic influence on the structural connectome variation remains highly elusive. Relying on a landmark imaging genetics study for young adults, we develop a biologically plausible brain network response shrinkage model to comprehensively characterize the relationship between high dimensional genetic variants and the structural connectome phenotype. Under a unified Bayesian framework, we accommodate the topology of brain network and biological architecture within the genome; and eventually establish a mechanistic mapping between genetic biomarkers and the associated brain sub-network units. An efficient expectation-maximization algorithm is developed to estimate the model and ensure computing feasibility. In the application to the Human Connectome Project Young Adult (HCP-YA) data, we establish the genetic underpinnings which are highly interpretable under functional annotation and brain tissue eQTL analysis, for the brain white matter tracts connecting the hippocampus and two cerebral hemispheres. We also show the superiority of our method in extensive simulations.

Porphyrin-Regulated Heterostructured Hydrogel Ionic Diode with a High Rectification Ratio and Output Voltage.

Nanochannel ionic diodes require extremely complex and expensive fabrication processes. Polyelectrolyte ionic diodes attracted widespread attention among ionic rectification systems due to their simplicity of development and the ability to break the size limits of the nanochannel. However, enhancement of their rectification ratio is still in the exploratory stage. In this study, chitosan (CS) hydrogels and sodium polyacrylate (PAAs) hydrogels were prepared as the substrates for the heterostructured ionic diodes. 5,10,15,20-Tetrakis(4-aminophenyl)-21H,23H-porphyrin (TAPP) was selected to regulate the rectification ratio of ionic diodes. By adding 0.05 wt % TAPP to the CS hydrogel, the rectification ratio of the ionic diode can be increased to 10, which is 4 times larger than that of the undoped ionic diode. In contrast, the rectification ratio of the ionic diodes with TAPP added in the PAAs hydrogel decreases to 2. In addition, the ionic diode composed of the TAPP-doped CS hydrogel and PAAs hydrogel has the characteristics of a high open-circuit voltage. The open-circuit voltage of the 10 mm × 10 mm × 4 mm heterojunction hydrogel reached 370 mV. The ionic diodes can be used as a self-powered power supply device.

Sleep-Body Composition Relationship: Roles of Sleep Behaviors in General and Abdominal Obesity in Chinese Adolescents Aged 17-22 Years.

This study aimed to investigate the association between sleep behaviors and body composition, which was measured by bioelectrical impedance analysis (BIA) among Chinese adolescents. Overall, 444 students (65.3% females, 19.12 ± 1.177 years) completed questionnaires describing sleep characteristics. Sleep characteristics were derived from subjective means. Body composition was obtained from BIA by InBody 720 (Biospace Co. Ltd., Seoul, Republic of Korea). Regression models tested relationships between sleep and body composition after adjustment for covariates. Students with weekday nap duration (>30 min/d) exerted higher waist-height ratio (WHtR) (B = 0.013, FDR-corrected p = 0.080). Average sleep duration (≤7 h/d) was linked to more WHtR (B = 0.016, FDR-corrected p = 0.080). People with high social jetlag showed gained visceral fat area (B = 7.475), WHtR (B = 0.015), waist to hip ratio (B = 0.012), fat mass index (B = 0.663) and body fat percentage (B = 1.703) (all FDR-corrected p < 0.1). Individuals with screen time before sleep (>0.5 h) exhibited higher visceral fat area (B = 7.934, FDR-corrected p = 0.064), WHtR (B = 0.017, FDR-corrected p = 0.080), waist to hip ratio (B = 0.016, FDR-corrected p = 0.090), fat mass index (B = 0.902, FDR-corrected p = 0.069) and body fat percentage (B = 2.892, FDR-corrected p = 0.018). We found poor sleep characteristics were closely related to general and abdominal obesity.

Outcomes in Patients With Poststroke Seizures: A Systematic Review and Meta-Analysis.

Importance: Published data about the impact of poststroke seizures (PSSs) on the outcomes of patients with stroke are inconsistent and have not been systematically evaluated, to the authors' knowledge. Objective: To investigate outcomes in people with PSS compared with people without PSS. Data Sources: MEDLINE, Embase, PsycInfo, Cochrane, LILACS, LIPECS, and Web of Science, with years searched from 1951 to January 30, 2023. Study Selection: Observational studies that reported PSS outcomes. Data Extraction and Synthesis: The Preferred Reporting Items for Systematic Reviews and Meta-Analyses checklist was used for abstracting data, and the Joanna Briggs Institute tool was used for risk-of-bias assessment. Data were reported as odds ratio (OR) and standardized mean difference (SMD) with a 95% CI using a random-effects meta-analysis. Publication bias was assessed using funnel plots and the Egger test. Outlier and meta-regression analyses were performed to explore the source of heterogeneity. Data were analyzed from November 2022 to January 2023. Main Outcomes and Measures: Measured outcomes were mortality, poor functional outcome (modified Rankin scale [mRS] score 3-6), disability (mean mRS score), recurrent stroke, and dementia at patient follow-up. Results: The search yielded 71 eligible articles, including 20 110 patients with PSS and 1 166 085 patients without PSS. Of the participants with PSS, 1967 (9.8%) had early seizures, and 10 605 (52.7%) had late seizures. The risk of bias was high in 5 studies (7.0%), moderate in 35 (49.3%), and low in 31 (43.7%). PSSs were associated with mortality risk (OR, 2.1; 95% CI, 1.8-2.4), poor functional outcome (OR, 2.2; 95% CI, 1.8-2.8), greater disability (SMD, 0.6; 95% CI, 0.4-0.7), and increased dementia risk (OR, 3.1; 95% CI, 1.3-7.7) compared with patients without PSS. In subgroup analyses, early seizures but not late seizures were associated with mortality (OR, 2.4; 95% CI, 1.9-2.9 vs OR, 1.2; 95% CI, 0.8-2.0) and both ischemic and hemorrhagic stroke subtypes were associated with mortality (OR, 2.2; 95% CI, 1.8-2.7 vs OR, 1.4; 95% CI, 1.0-1.8). In addition, early and late seizures (OR, 2.4; 95% CI, 1.6-3.4 vs OR, 2.7; 95% CI, 1.8-4.1) and stroke subtypes were associated with poor outcomes (OR, 2.6; 95% CI, 1.9-3.7 vs OR, 1.9; 95% CI, 1.0-3.6). Conclusions and Relevance: Results of this systematic review and meta-analysis suggest that PSSs were associated with significantly increased mortality and severe disability in patients with history of stroke. Unraveling these associations is a high clinical and research priority. Trials of interventions to prevent seizures may be warranted.

Dynamic Prediction of Outcomes for Youth at Clinical High Risk for Psychosis: A Joint Modeling Approach.

Importance: Leveraging the dynamic nature of clinical variables in the clinical high risk for psychosis (CHR-P) population has the potential to significantly improve the performance of outcome prediction models. Objective: To improve performance of prediction models and elucidate dynamic clinical profiles using joint modeling to predict conversion to psychosis and symptom remission. Design, Setting, and Participants: Data were collected as part of the third wave of the North American Prodrome Longitudinal Study (NAPLS 3), which is a 9-site prospective longitudinal study. Participants were individuals aged 12 to 30 years who met criteria for a psychosis-risk syndrome. Clinical, neurocognitive, and demographic variables were collected at baseline and at multiple follow-up visits, beginning at 2 months and up to 24 months. An initial feature selection process identified longitudinal clinical variables that showed differential change for each outcome group across 2 months. With these variables, a joint modeling framework was used to estimate the likelihood of eventual outcomes. Models were developed and tested in a 10-fold cross-validation framework. Clinical data were collected between February 2015 and November 2018, and data were analyzed from February 2022 to December 2023. Main Outcomes and Measures: Prediction models were built to predict conversion to psychosis and symptom remission. Participants met criteria for conversion if their positive symptoms reached the fully psychotic range and for symptom remission if they were subprodromal on the Scale of Psychosis-Risk Symptoms for a duration of 6 months or more. Results: Of 488 included NAPLS 3 participants, 232 (47.5%) were female, and the mean (SD) age was 18.2 (3.4) years. Joint models achieved a high level of accuracy in predicting conversion (balanced accuracy [BAC], 0.91) and remission (BAC, 0.99) compared with baseline models (conversion: BAC, 0.65; remission: BAC, 0.60). Clinical variables that showed differential change between outcome groups across a 2-month span, including measures of symptom severity and aspects of functioning, were also identified. Further, intra-individual risks for each outcome were more negatively correlated when using joint models (r = -0.92; P < .001) compared with baseline models (r = -0.50; P < .001). Conclusions and Relevance: In this study, joint models significantly outperformed baseline models in predicting both conversion and remission, demonstrating that monitoring short-term clinical change may help to parse heterogeneous dynamic clinical trajectories in a CHR-P population. These findings could inform additional study of targeted treatment selection and could move the field closer to clinical implementation of prediction models.

Unhealthy lifestyles and clusters status among 3637 adolescents aged 11-23 years: a school-based cross-sectional study in China.

BACKGROUND: Unhealthy lifestyles are risk factors for non-communicable diseases (NCDs) and tend to be clustered, with a trajectory that extends from adolescence to adulthood. This study investigated the association of diets, tobacco, alcohol, physical activity (PA), screen time (ST) and sleep duration (SD) in a total of six lifestyles, separately and as cumulative lifestyle scores, with sociodemographic characteristics among school-aged adolescents in the Chinese city of Zhengzhou. METHODS: In the aggregate, 3,637 adolescents aged 11-23 years were included in the study. The questionnaire collected data on socio-demographic characteristics and lifestyles. Healthy and unhealthy lifestyles were identified and scored, depending on the individual score (0 and 1 for healthy and unhealthy lifestyles respectively), with a total score between 0 and 6. Based on the sum of the dichotomous scores, the number of unhealthy lifestyles was calculated and divided into three clusters (0-1, 2-3, 4-6). Chi-square test was used to analyze the group difference of lifestyles and demographic characteristics, and multivariate logistic regression was used to explore the associations between demographic characteristics and the clustering status of unhealthy lifestyles. RESULTS: Among all participants, the prevalence of unhealthy lifestyles was: 86.4% for diet, 14.5% for alcohol, 6.0% for tobacco, 72.2% for PA, 42.3% for ST and 63.9% for SD. Students who were in university, female, lived in country (OR = 1.725, 95% CI: 1.241-2.398), had low number of close friends (1-2: OR = 2.110, 95% CI: 1.428-3.117; 3-5: OR = 1.601, 95% CI: 1.168-2.195), and had moderate family income (OR = 1.771, 95% CI: 1.208-2.596) were more likely to develop unhealthy lifestyles. In total, unhealthy lifestyles remain highly prevalent among Chinese adolescents. CONCLUSION: In the future, the establishment of an effective public health policy may improve the lifestyle profile of adolescents. Based on the lifestyle characteristics of different populations reported in our findings, lifestyle optimization can be more efficiently integrated into the daily lives of adolescents. Moreover, it is essential to conduct well-designed prospective studies on adolescents.

Mining Correlation between Fluid Intelligence and Whole-brain Large Scale Structural Connectivity.

Exploring the neural basis of intelligence and the corresponding associations with brain network has been an active area of research in network neuroscience. Up to now, the majority of explorations mining human intelligence in brain connectomics leverages whole-brain functional connectivity patterns. In this study, structural connectivity patterns are instead used to explore relationships between brain connectivity and different behavioral/cognitive measures such as fluid intelligence. Specifically, we conduct a study using the 397 unrelated subjects from Human Connectome Project (Young Adults) dataset to estimate individual level structural connectivity matrices. We show that topological features, as quantified by our proposed measurements: Average Persistence (AP) and Persistent Entropy (PE), has statistically significant associations with different behavioral/cognitive measures. We also perform a parallel study using traditional graph-theoretical measures, provided by Brain Connectivity Toolbox, as benchmarks for our study. Our findings indicate that individual's structural connectivity indeed offers reliable predictive power of different behavioral/cognitive measures, including but not limited to fluid intelligence. Our results suggest that structural connectomes provide complementary insights (compared to using functional connectomes) in predicting human intelligence and warrants future studies on human intelligence and/or other behavioral/cognitive measures involving multi-modal approach.

Impact of genetic polymorphisms on the risk of epilepsy amongst patients with acute brain injury: A systematic review.

BACKGROUND AND PURPOSE: The genetics of late seizure or epilepsy secondary to traumatic brain injury (TBI) or stroke are poorly understood. We undertook a systematic review to test the association of single-nucleotide polymorphisms (SNPs) with the risk of post-traumatic epilepsy (PTE) and post-stroke epilepsy (PSE). METHODS: We followed methods from our prespecified protocol on PROSPERO to identify indexed articles for this systematic review. We collated the association statistics from the included articles to assess the association of SNPs with the risk of epilepsy amongst TBI or stroke patients. We assessed study quality using the Q-Genie tool. We report odds ratios (OR) and hazard ratios with 95% confidence intervals (CIs). RESULTS: The literature search yielded 420 articles. We included 16 studies in our systematic review, of which seven were of poor quality. We examined published data on 127 SNPs from 32 genes identified in PTE and PSE patients. Eleven SNPs were associated with a significantly increased risk of PTE. Three SNPs, TRMP6 rs2274924, ALDH2 rs671, and CD40 -1C/T, were significantly associated with an increased risk of PSE, while two, AT1R rs12721273 and rs55707609, were significantly associated with reduced risk. The meta-analysis for the association of the APOE ɛ4 with PTE was nonsignificant (OR 1.8, CI 0.6-5.6). CONCLUSIONS: The current evidence on the association of genetic polymorphisms in epilepsy secondary to TBI or stroke is of low quality and lacks validation. A collaborative effort to pool genetic data linked to epileptogenesis in stroke and TBI patients is warranted.

Bayesian interaction selection model for multimodal neuroimaging data analysis.

Multimodality or multiconstruct data arise increasingly in functional neuroimaging studies to characterize brain activity under different cognitive states. Relying on those high-resolution imaging collections, it is of great interest to identify predictive imaging markers and intermodality interactions with respect to behavior outcomes. Currently, most of the existing variable selection models do not consider predictive effects from interactions, and the desired higher-order terms can only be included in the predictive mechanism following a two-step procedure, suffering from potential misspecification. In this paper, we propose a unified Bayesian prior model to simultaneously identify main effect features and intermodality interactions within the same inference platform in the presence of high-dimensional data. To accommodate the brain topological information and correlation between modalities, our prior is designed by compiling the intermediate selection status of sequential partitions in light of the data structure and brain anatomical architecture, so that we can improve posterior inference and enhance biological plausibility. Through extensive simulations, we show the superiority of our approach in main and interaction effects selection, and prediction under multimodality data. Applying the method to the Adolescent Brain Cognitive Development (ABCD) study, we characterize the brain functional underpinnings with respect to general cognitive ability under different memory load conditions.

Simulating time-to-event data subject to competing risks and clustering: A review and synthesis.

Simulation studies play an important role in evaluating the performance of statistical models developed for analyzing complex survival data such as those with competing risks and clustering. This article aims to provide researchers with a basic understanding of competing risks data generation, techniques for inducing cluster-level correlation, and ways to combine them together in simulation studies, in the context of randomized clinical trials with a binary exposure or treatment. We review data generation with competing and semi-competing risks and three approaches of inducing cluster-level correlation for time-to-event data: the frailty model framework, the probability transform, and Moran's algorithm. Using exponentially distributed event times as an example, we discuss how to introduce cluster-level correlation into generating complex survival outcomes, and illustrate multiple ways of combining these methods to simulate clustered, competing and semi-competing risks data with pre-specified correlation values or degree of clustering.

Homological landscape of human brain functional sub-circuits.

Human whole-brain functional connectivity networks have been shown to exhibit both local/quasilocal (e.g., set of functional sub-circuits induced by node or edge attributes) and non-local (e.g., higher-order functional coordination patterns) properties. Nonetheless, the non-local properties of topological strata induced by local/quasilocal functional sub-circuits have yet to be addressed. To that end, we proposed a homological formalism that enables the quantification of higher-order characteristics of human brain functional sub-circuits. Our results indicated that each homological order uniquely unravels diverse, complementary properties of human brain functional sub-circuits. Noticeably, the H1 homological distance between rest and motor task were observed at both whole-brain and sub-circuit consolidated level which suggested the self-similarity property of human brain functional connectivity unraveled by homological kernel. Furthermore, at the whole-brain level, the rest-task differentiation was found to be most prominent between rest and different tasks at different homological orders: i) Emotion task H0, ii) Motor task H1, and iii) Working memory task H2. At the functional sub-circuit level, the rest-task functional dichotomy of default mode network is found to be mostly prominent at the first and second homological scaffolds. Also at such scale, we found that the limbic network plays a significant role in homological reconfiguration across both task- and subject- domain which sheds light to subsequent Investigations on the complex neuro-physiological role of such network. From a wider perspective, our formalism can be applied, beyond brain connectomics, to study non-localized coordination patterns of localized structures stretching across complex network fibers.