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.

Influenza A virus during pregnancy disrupts maternal intestinal immunity and fetal cortical development in a dose- and time-dependent manner.

Epidemiological studies link exposure to viral infection during pregnancy, including influenza A virus (IAV) infection, with increased incidence of neurodevelopmental disorders (NDDs) in offspring. Models of maternal immune activation (MIA) using viral mimetics demonstrate that activation of maternal intestinal T helper 17 (TH17) cells, which produce effector cytokine interleukin (IL)-17, leads to aberrant fetal brain development, such as neocortical malformations. Fetal microglia and border-associated macrophages (BAMs) also serve as potential cellular mediators of MIA-induced cortical abnormalities. However, neither the inflammation-induced TH17 cell pathway nor fetal brain-resident macrophages have been thoroughly examined in models of live viral infection during pregnancy. Here, we inoculated pregnant mice with two infectious doses of IAV and evaluated peak innate and adaptive immune responses in the dam and fetus. While respiratory IAV infection led to dose-dependent maternal colonic shortening and microbial dysregulation, there was no elevation in intestinal TH17 cells nor IL-17. Systemically, IAV resulted in consistent dose- and time-dependent increases in IL-6 and IFN-γ. Fetal cortical abnormalities and global changes in fetal brain transcripts were observable in the high-but not the moderate-dose IAV group. Profiling of fetal microglia and BAMs revealed dose- and time-dependent differences in the numbers of meningeal but not choroid plexus BAMs, while microglial numbers and proliferative capacity of Iba1+ cells remained constant. Fetal brain-resident macrophages increased phagocytic CD68 expression, also in a dose- and time-dependent fashion. Taken together, our findings indicate that certain features of MIA are conserved between mimetic and live virus models, while others are not. Overall, we provide consistent evidence of an infection severity threshold for downstream maternal inflammation and fetal cortical abnormalities, which recapitulates a key feature of the epidemiological data and further underscores the importance of using live pathogens in NDD modeling to better evaluate the complete immune response and to improve translation to the clinic.

Self-reported hearing loss is associated with faster cognitive and functional decline but not diagnostic conversion in the ADNI cohort.

INTRODUCTION: Hearing loss is identified as one of the largest modifiable risk factors for cognitive impairment and dementia. Studies evaluating this relationship have yielded mixed results. METHODS: We investigated the longitudinal relationship between self-reported hearing loss and cognitive/functional performance in 695 cognitively normal (CN) and 941 participants with mild cognitive impairment (MCI) enrolled in the Alzheimer's Disease Neuroimaging Initiative. RESULTS: Within CN participants with hearing loss, there was a significantly greater rate of cognitive decline per modified preclinical Alzheimer's cognitive composite. Within both CN and MCI participants with hearing loss, there was a significantly greater rate of functional decline per the functional activities questionnaire (FAQ). In CN and MCI participants, hearing loss did not significantly contribute to the risk of progression to a more impaired diagnosis. DISCUSSION: These results confirm previous studies demonstrating a significant longitudinal association between self-reported hearing loss and cognition/function but do not demonstrate an increased risk of conversion to a more impaired diagnosis. CLINICAL TRIAL REGISTRATION INFORMATION: NCT00106899 (ADNI: Alzheimer's Disease Neuroimaging Initiative, clinicaltrials.gov), NCT01078636 (ADNI-GO: Alzheimer's Disease Neuroimaging Initiative Grand Opportunity, clinicaltrials.gov), NCT01231971 (ADNI2: Alzheimer's Disease Neuroimaging Initiative 2, clinicaltrials.gov), NCT02854033 (ADNI3: Alzheimer's Disease Neuroimaging Initiative 3, clinicaltrials.gov). HIGHLIGHTS: Hearing loss is a potential modifiable risk factor for dementia. We assessed the effect of self-reported hearing loss on cognition and function in the ADNI cohort. Hearing loss contributes to significantly faster cognitive and functional decline. Hearing loss was not associated with conversion to a more impaired diagnosis.

Understanding the role of subpopulations and reliability in between-group studies.

The replication crisis has led to a renewed discussion about the impacts of measurement quality on the precision of psychology research. High measurement quality is associated with low measurement error, yet the role of reliability in the quality of experimental research is not always well understood. In this study, we attempt to understand the role of reliability through its relationship with power while focusing on between-group designs for experimental studies. We outline a latent variable framework to investigate this nuanced relationship through equations. An under-evaluated aspect of the relationship is the variance and homogeneity of the subpopulation from which the study sample is drawn. Higher homogeneity implies a lower reliability, but yields higher power. We proceed to demonstrate the impact of this relationship between reliability and power by imitating different scenarios of large-scale replications with between-group designs. We find negative correlations between reliability and power when there are sizable differences in the latent variable variance and negligible differences in the other parameters across studies. Finally, we analyze the data from the replications of the ego depletion effect (Hagger et al., 2016) and the replications of the grammatical aspect effect (Eerland et al., 2016), each time with between-group designs, and the results align with previous findings. The applications show that a negative relationship between reliability and power is a realistic possibility with consequences for applied work. We suggest that more attention be given to the homogeneity of the subpopulation when study-specific reliability coefficients are reported in between-group studies.

Establishing group-level brain structural connectivity incorporating anatomical knowledge under latent space modeling.

Brain structural connectivity, capturing the white matter fiber tracts among brain regions inferred by diffusion MRI (dMRI), provides a unique characterization of brain anatomical organization. One fundamental question to address with structural connectivity is how to properly summarize and perform statistical inference for a group-level connectivity architecture, for instance, under different sex groups, or disease cohorts. Existing analyses commonly summarize group-level brain connectivity by a simple entry-wise sample mean or median across individual brain connectivity matrices. However, such a heuristic approach fully ignores the associations among structural connections and the topological properties of brain networks. In this project, we propose a latent space-based generative network model to estimate group-level brain connectivity. Within our modeling framework, we incorporate the anatomical information of brain regions as the attributes of nodes to enhance the plausibility of our estimation and improve biological interpretation. We name our method the attributes-informed brain connectivity (ABC) model, which compared with existing group-level connectivity estimations, (1) offers an interpretable latent space representation of the group-level connectivity, (2) incorporates the anatomical knowledge of nodes and tests its co-varying relationship with connectivity and (3) quantifies the uncertainty and evaluates the likelihood of the estimated group-level effects against chance. We devise a novel Bayesian MCMC algorithm to estimate the model. We evaluate the performance of our model through extensive simulations. By applying the ABC model to study brain structural connectivity stratified by sex among Alzheimer's Disease (AD) subjects and healthy controls incorporating the anatomical attributes (volume, thickness and area) on nodes, our method shows superior predictive power on out-of-sample structural connectivity and identifies meaningful sex-specific network neuromarkers for AD.

The microbiota of Amblyomma americanum reflects known westward expansion.

Amblyomma americanum, a known vector of multiple tick-borne pathogens, has expanded its geographic distribution across the United States in the past decades. Tick microbiomes may play a role shaping their host's life history and vectorial capacity. Bacterial communities associated with A. americanum may reflect, or enable, geographic expansion and studying the microbiota will improve understanding of tick-borne disease ecology. We examined the microbiota structure of 189 adult ticks collected in four regions encompassing their historical and current geographic distribution. Both geographic region of origin and sex were significant predictors of alpha diversity. As in other tick models, within-sample diversity was low and uneven given the presence of dominant endosymbionts. Beta diversity analyses revealed that bacterial profiles of ticks of both sexes collected in the West were significantly different from those of the Historic range. Biomarkers were identified for all regions except the historical range. In addition, Bray-Curtis dissimilarities overall increased with distance between sites. Relative quantification of ecological processes showed that, for females and males, respectively, drift and dispersal limitation were the primary drivers of community assembly. Collectively, our findings highlight how microbiota structural variance discriminates the western-expanded populations of A. americanum ticks from the Historical range. Spatial autocorrelation, and particularly the detection of non-selective ecological processes, are indicative of geographic isolation. We also found that prevalence of Ehrlichia chaffeensis, E. ewingii, and Anaplasma phagocytophilum ranged from 3.40-5.11% and did not significantly differ by region. Rickettsia rickettsii was absent from our samples. Our conclusions demonstrate the value of synergistic analysis of biogeographic and microbial ecology data in investigating range expansion in A. americanum and potentially other tick vectors as well.

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.

Medial amygdalar tau is associated with mood 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 the 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, and tau binding in these regions was associated with mood symptoms. Across amygdalar divisions, amyloid-positive individuals had relatively higher regional connectivity from amygdala to other temporal regions, insula, and orbitofrontal cortex, but medial amygdala to retrosplenial cortex was lower. Medial amygdala to retrosplenial connectivity was negatively associated with anxiety symptoms, as was retrosplenial tau. CONCLUSIONS: Our findings suggest that preclinical tau deposition in the amygdala and associated changes in functional connectivity may relate to early mood symptoms in AD.

Heywood Cases in Unidimensional Factor Models and Item Response Models for Binary Data.

Heywood cases are known from linear factor analysis literature as variables with communalities larger than 1.00, and in present day factor models, the problem also shows in negative residual variances. For binary data, factor models for ordinal data can be applied with either delta parameterization or theta parametrization. The former is more common than the latter and can yield Heywood cases when limited information estimation is used. The same problem shows up as non convergence cases in theta parameterized factor models and as extremely large discriminations in item response theory (IRT) models. In this study, we explain why the same problem appears in different forms depending on the method of analysis. We first discuss this issue using equations and then illustrate our conclusions using a small simulation study, where all three methods, delta and theta parameterized ordinal factor models (with estimation based on polychoric correlations and thresholds) and an IRT model (with full information estimation), are used to analyze the same datasets. The results generalize across WLS, WLSMV, and ULS estimators for the factor models for ordinal data. Finally, we analyze real data with the same three approaches. The results of the simulation study and the analysis of real data confirm the theoretical conclusions.