Inference Test Research Articles

The effect of the implementation of the international code of marketing of breast-milk substitutes on child mortality in Ghana and Tanzania.

The International Code of Marketing of Breast-Milk Substitutes is an important instrument to protect and promote appropriate infant and young child feeding and the safe use of commercial milk formulas. Ghana and Tanzania implemented the Code into national legislation in 2000 and 1994, respectively. We aimed to estimate the effects of the Code implementation on child mortality (CM) in both countries. The countries analysed were Ghana and Tanzania. For CM and HIV rates, data from the Institute for Health Metrics and Evaluation from up to 2019 were used. Data for income and skilled birth rates were retrieved from the World Bank, for fertility from the World Population Prospects, for vaccination from the Global Health Observatory and for employment from the International Labour Organization. We used the synthetic control group method and performed placebo tests to assess statistical inference. The primary outcomes were CM by lower respiratory infections, mainly pneumonia, and diarrhoea and the secondary outcome was overall CM. One-sided inference tests showed statistically significant treatment effects for child deaths by lower respiratory infections in Ghana (P = 0·0476) and Tanzania (P = 0·0476) and for diarrhoea in Tanzania (P = 0·0476). More restrictive two-sided inference tests showed a statistically significant treatment effect for child deaths by lower respiratory infections in Ghana (P = 0·0476). No statistically significant results were found for overall CM. The results suggest that the implementation of the Code in both countries had a potentially beneficial effect on CM due to infectious diseases; however, further research is needed to corroborate these findings.

Machine-learned exclusion limits without binning

Machine-learned likelihoods (MLL) combines machine-learning classification techniques with likelihood-based inference tests to estimate the experimental sensitivity of high-dimensional data sets. We extend the MLL method by including kernel density estimators (KDE) to avoid binning the classifier output to extract the resulting one-dimensional signal and background probability density functions. We first test our method on toy models generated with multivariate Gaussian distributions, where the true probability distribution functions are known. Later, we apply the method to two cases of interest at the LHC: a search for exotic Higgs bosons, and a Z′\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$Z'$$\\end{document} boson decaying into lepton pairs. In contrast to physical-based quantities, the typical fluctuations of the ML outputs give non-smooth probability distributions for pure-signal and pure-background samples. The non-smoothness is propagated into the density estimation due to the good performance and flexibility of the KDE method. We study its impact on the final significance computation, and we compare the results using the average of several independent ML output realizations, which allows us to obtain smoother distributions. We conclude that the significance estimation turns out to be not sensible to this issue.

Diversity Patterns and Determinants of Soil Microorganisms and Nematodes along Elevation Gradients in a Temperate Forest in South Korea

The elevational patterns of soil microbial and nematodes diversity (SMND) and the determinants remain controversial. Moreover, how the SMND are modified simultaneously with an elevational gradient has not yet been established. In this study, we investigated the elevational patterns of the SMND and the relative importance among/within tree factors (i.e., tree diversity, identity, and quantity) and environmental factors (i.e., climate and soil) on the SMND. For this purpose, we analyzed datasets from 27 plots across nine elevation bands in the temperate forests of Mt. Gariwang, South Korea. We performed multimodel inference tests and subsequently conducted a variance partitioning to determine the most prominent factors controlling each SMND and compare the relative contribution of the trees and environmental effects. Our results revealed that bacterial and fungal diversity decreased along the elevation gradient. However, nematode diversity did not change significantly, indicating that site-specific environmental conditions may be more influential than the elevation per se. Moreover, this indicates that bacterial diversity was affected by the pH and functional dispersion of the leaf size, and that fungal diversity was governed only by the pH. However, nematode diversity was driven by aboveground biomass, ammonium-nitrogen, and tree size diversity. In summary, the soil microbial diversity was more strongly controlled by the environmental factors, whereas the tree factors were more important for nematodes. Our results show that the elevational patterns and determinants of SMND differed among the taxonomic groups in the common micro-food web. These findings provide new insights into the factors controlling the SMND in a temperate forest and expand the local knowledge of soil biodiversity which is necessary for promoting its mainstreaming. Thus, our results contribute to establishing a basis for more targeted and effective biodiversity conservation and management practices in forest ecosystems.

A novel method for real-time object-based copy-move tampering localization in videos using fine-tuned YOLO V8

The research community faces challenges for video forgery detection techniques as advancements in multimedia technology have made it easy to alter the original video content and share it on electronic and social media with false propaganda. The copy-move attack is the most commonly practiced type of attack in videos/images, where an object is copied and moved into the current frame or any other frame of the video. Hence an illusion of recreation can be created to forge the content. It is very difficult to differentiate to uncover the forgery traces by the naked eye. Hence, a passive method-based algorithm is proposed to scientifically investigate the statistical properties of the video by normalizing the median difference of the frames at the pixel level, and graphical analysis successfully shows the clear peak in the forged region. After that, a new deep learning approach, “You Only Look at Once”, the latest eighth version of YOLO, is tuned and trained for the localization of forged objects in the real-time domain. The validation and testing results obtained from the trained YOLO V8 are successfully able to detect and localize the forged objects in the videos with mean average precision (mAP) of 0.99, recall is 0.99, precision is 0.99, and highest confidence score. The proposed YOLO V8 is fine-tuned in three different ways, and the performance of the proposed method outperforms existing state-of-the-art techniques in terms of inference speed, accuracy, precision, recall, testing, and training time.

High-resolution DNA methylation screening of the major histocompatibility complex in multiple sclerosis.

The HLA-DRB1 gene in the major histocompatibility complex (MHC) region in chromosome 6p21 is the strongest genetic factor identified as influencing multiple sclerosis (MS) susceptibility. DNA methylation changes associated with MS have been consistently detected at the MHC region. However, understanding the full scope of epigenetic regulations of the MHC remains incomplete, due in part to the limited coverage of this region by standard whole genome bisulfite sequencing or array-based methods. We developed and validated an MHC capture protocol coupled with bisulfite sequencing and conducted a comprehensive analysis of the MHC methylation landscape in blood samples from 147 treatment naïve MS study participants and 129 healthy controls. We identified 132 differentially methylated region (DMRs) within MHC region associated with disease status. The DMRs overlapped with established MS risk loci. Integration of the MHC methylome with human leukocyte antigen (HLA) genetic data indicate that the methylation changes are significantly associated with HLA genotypes. Using DNA methylation quantitative trait loci (mQTL) mapping and the causal inference test (CIT), we identified 643 cis-mQTL-DMRs paired associations, including 71 DMRs possibly mediating causal relationships between 55 single nucleotide polymorphisms (SNPs) and MS risk. The results describe MS-associated methylation changes in MHC region and highlight the association between HLA genotypes and methylation changes. Results from the mQTL and CIT analyses provide evidence linking MHC region variations, methylation changes, and disease risk for MS.

Methylation and transcriptomic expression profiles of HUVEC in the oxygen and glucose deprivation model and its clinical implications in AMI patients.

The obstructed coronary artery undergoes a series of pathological changes due to ischemic-hypoxic shocks during acute myocardial infarction (AMI). However, the altered DNA methylation levels in endothelial cells under these conditions and their implication for the etiopathology of AMI have not been investigated in detail. This study aimed to explore the relationship between DNA methylation and pathologically altered gene expression profile in human umbilical vein endothelial cells (HUVECs) subjected to oxygen-glucose deprivation (OGD), and its clinical implications in AMI patients. The Illumina Infinium MethylationEPIC BeadChip assay was used to explore the genome-wide DNA methylation profile using the Novaseq6000 platform for mRNA sequencing in 3 pairs of HUVEC-OGD and control samples. GO and KEGG pathway enrichment analyses, as well as correlation, causal inference test (CIT), and protein-protein interaction (PPI) analyses identified 22 hub genes that were validated by MethylTarget sequencing as well as qRT-PCR. ELISA was used to detect four target molecules associated with the progression of AMI. A total of 2,524 differentially expressed genes (DEGs) and 22,148 differentially methylated positions (DMPs) corresponding to 6,642 differentially methylated genes (DMGs) were screened (|Δβ|>0.1 and detection p < 0.05). After GO, KEGG, correlation, CIT, and PPI analyses, 441 genes were filtered. qRT-PCR confirmed the overexpression of VEGFA, CCL2, TSP-1, SQSTM1, BCL2L11, and TIMP3 genes, and downregulation of MYC, CD44, BDNF, GNAQ, RUNX1, ETS1, NGFR, MME, SEMA6A, GNAI1, IFIT1, and MEIS1. DNA fragments BDNF_1_ (r = 0.931, p < 0.0001) and SQSTM1_2_NEW (r = 0.758, p = 0.0043) were positively correlated with the expressions of corresponding genes, and MYC_1_ (r = -0.8245, p = 0.001) was negatively correlated. Furthermore, ELISA confirmed TNFSF10 and BDNF were elevated in the peripheral blood of AMI patients (p = 0.0284 and p = 0.0142, respectively). Combined sequencing from in vitro cellular assays with clinical samples, aiming to establish the potential causal chain of the causal factor (DNA methylation) - mediator (mRNA)-cell outcome (endothelial cell ischemic-hypoxic injury)-clinical outcome (AMI), our study identified promising OGD-specific genes, which provided a solid basis for screening fundamental diagnostic and prognostic biomarkers of coronary endothelial cell injury of AMI. Moreover, it furnished the first evidence that during ischemia and hypoxia, the expression of BNDF was regulated by DNA methylation in endothelial cells and elevated in peripheral blood.

Maternal obesity and childhood asthma risk: Exploring mediating pathways.

Growing evidence for the effect of maternal obesity on childhood asthma motivates investigation of mediating pathways. To investigate if childhood body mass index (BMI), gestational weight gain (GWG) and preterm birth mediate the association of maternal obesity on childhood asthma risk. We used electronic medical records from mother-child pairs enrolled in Kaiser Permanente Northern California integrated healthcare system. Children were followed from their birth (2005-2014) until at least age 4 (n = 95,723), age 6 (n = 59,230) or age 8 (n = 25,261). Childhood asthma diagnosis at each age was determined using ICD-9/10 codes and medication dispensings. Prepregnancy BMI (underweight [<18.5], normal [18.5-24.9], overweight [25-29.9], obese [≥30] kg/m2) were defined using height and weight measurements close to the last menstrual period date. Child's BMI (Centers for Disease Control and Prevention BMI-for-age percentiles: underweight [<5th], normal [5th-85th], overweight [85th-95th], obese [>95th]) were obtained using anthropometric measurements taken the year preceding each follow-up age. GWG (delivery weight-prepregnancy weight) was categorised based on Institutes of Medicine recommendations (inadequate, adequate, excessive). Implementing first causal inference test (CIT) then causal mediator models (to decompose the natural direct and indirect effects), we examined the potential mediating effect of childhood BMI, GWG, and preterm birth on the association between prepregnancy BMI (continuous and categorical) and childhood asthma. Overall, risk of childhood asthma increased as prepregnancy BMI increased (age 4 risk ratio: 1.07, 95% confidence interval: 1.04, 1.09, per 5 kg/m2 increase in BMI; similar for age 6 and 8). CIT identified childhood BMI and preterm birth, but not GWG as potential mediators. Causal mediation models confirmed childhood BMI, but not preterm birth, as having a partial mediating effect. Results were similar for age 6 and 8, and when continuous mediators (instead of binary) were assessed. Childhood overweight/obesity has a modest mediating effect on the association between prepregnancy BMI and childhood asthma.

Rejoinder on: statistical inference and large-scale multiple testing for high-dimensional regression models

Rejoinder on: statistical inference and large-scale multiple testing for high-dimensional regression models

Both clinically informed and brain region agnostic approaches identify neuroimaging derived phenotypes associated with genetically regulated gene expression of late‐onset Alzheimer’s disease genes

AbstractBackgroundGenetically regulated gene expression (GReX) data leverages expression quantitative trait loci to investigate the genetic mechanism of Alzheimer’s disease (AD). This study utilized the GReX‐mediated neuro‐imaging derived phenotypes (NIDPs), summary features derived from brain imaging modalities, as an endophenotype to identify brain regions associated with late‐onset AD (LOAD) via gene expression.MethodThis study contained brain region‐agnostic and region‐informed approaches. Both approaches utilized a set of 25 genes causally associated with LOAD in our previous cross‐tissue transcriptome‐wide association studies (TWAS) (Chen et al., 2021) to identify LOAD‐associated GReX‐mediated NIDPs. We leveraged the UK Biobank (UKBB) neuroimaging TWAS resource to provide GReX‐mediated NIDP information. Region‐agnostic: We assessed associations between GReX of LOAD genes and T1‐derived cortical and subcortical grey matter NIDPs from the UKBB neuroimaging TWAS. Region‐informed: We identified a set of AD‐associated cortical grey matter NIDPs from diagnosed AD patients (Westman et al., 2013). Next, we subset the UKBB neuroimaging TWAS data to those describing associations between GReX of LOAD genes and the selected AD NIDPs.A Benjamini‐Hochberg FDR p‐value threshold of 0.05 was used to establish statistical significance, and causal inference testing on associations between GReX of LOAD genes and NIDPs was conducted by implementing MR‐JTI in both approaches.Result Region‐agnostic: This approach found associations between GReX of 8 LOAD‐associated genes and 14 NIDPs (Figure 1). The most significant association is the GreX of ZNF296 and the right pars opercularis of the inferior frontal cortex, which replicated across 16 different tissues. Region‐informed: A single highly significant causal association between the GReX of PSMC3 and the ventral diencephalon was identified and replicated in gene expression models of 3 different bansal ganglia‐derived tissue models.ConclusionOur study shows the highly significant causal association between the GReX of ZNF296 and the right pars opercularis on LOAD through the region‐agnostic approach and the strong causal association between the GReX of PSMC3 and the ventral diencephalon on LOAD in AD‐associated region‐informed analyses. Our methodologies provide opportunities to explore brain regions associated with LOAD that are mediated by GReX of AD‐associated genes illuminating the impact of LOAD‐associated genes on gene expression and brain physiology.

Integrated genetic and epigenetic analyses uncovered GLP1R association with metabolically healthy obesity.

Both genetic and epigenetic variations of GLP1R influence the development and progression of obesity. However, the underlying mechanism remains elusive. This study aims to explore the mediation roles of obesity-related methylation sites in GLP1R gene variants-obesity association. A total of 300 Chinese adult participants were included in this study and classified into two groups: 180 metabolically healthy obesity (MHO) cases and 120 metabolically healthy normal-weight (MHNW) controls. Questionnaire investigation, physical measurement and laboratory examination were assessed in all participants. 18 single nucleotide polymorphisms (SNPs) and 31 CpG sites were selected for genotype and methylation assays. Causal inference test (CIT) was performed to evaluate the associations between GLP1R genetic variation, DNA methylation and MHO. The study found that rs4714211 polymorphism of GLP1R gene was significantly associated with MHO. Additionally, methylation sites in the intronic region of GLP1R (GLP1R-68-CpG 7.8.9; GLP1R-68-CpG 12.13; GLP1R-68-CpG 17; GLP1R-68-CpG 21) were associated with MHO, and two of these methylation sites (GLP1R-68-CpG 7.8.9; GLP1R-68-CpG 17) partially mediated the association between genotypes and MHO. Not only the gene polymorphism, but also the DNA methylation of GLP1R was associated with MHO. Epigenetic changes in the methylome may in part explain the relationship between genetic variants and MHO.

Using a penalized likelihood to detect mortality deceleration.

We suggest a novel method for detecting mortality deceleration by adding a penalty to the log-likelihood function in a gamma-Gompertz setting. This is an alternative to traditional likelihood inference and hypothesis testing. The main advantage of the proposed method is that it does not involve using a p-value, hypothesis testing, and asymptotic distributions. We evaluate the performance of our approach by comparing it with traditional likelihood inference on both simulated and real mortality data. Results have shown that our method is more accurate in detecting mortality deceleration and provides more reliable estimates of the underlying parameters. The proposed method is a significant contribution to the literature as it offers a powerful tool for analyzing mortality patterns.

Comments on: Statistical inference and large-scale multiple testing for high-dimensional regression models

Comments on: Statistical inference and large-scale multiple testing for high-dimensional regression models

A conditional linear combination test with many weak instruments

We consider a linear combination of jackknife Anderson-Rubin (AR), jackknife Lagrangian multiplier (LM), and orthogonalized jackknife LM tests for inference in IV regressions with many weak instruments and heteroskedasticity. Following I.Andrews (2016), we choose the weights in the linear combination based on a decision-theoretic rule that is adaptive to the identification strength. Under both weak and strong identifications, the proposed test controls asymptotic size and is admissible among certain class of tests. Under strong identification, our linear combination test has optimal power against local alternatives among the class of invariant or unbiased tests which are constructed based on jackknife AR and LM tests. Simulations and an empirical application to Angrist and Krueger’s (1991) dataset confirm the good power properties of our test.

Comments on: Statistical inference and large-scale multiple testing for high-dimensional regression models

Comments on: Statistical inference and large-scale multiple testing for high-dimensional regression models

46 Moderating Impact of Trauma on Brain Regions Underlying Social Cognition in Early Onset Psychosis

Objective:Previous research has found that trauma is a risk factor for developing early-onset psychosis (EOP), both exhibiting widespread structural abnormalities and social cognitive dysfunction (Hoy et al., 2012; Nair et al., 2020; Rotiker et al., 2018). However, few studies have investigated the association between trauma, neural architecture, and social behaviors. The current study examines whether trauma exposure moderates the association between cortical volume and thickness and social cognition in EOP.Participants and Methods:T1-weighted whole-brain magnetic resonance data were acquired on a 3T Siemens scanner for 23 adolescents with EOP aged 12-21 years (M = 16.12), and 20 age-matched controls (M = 17.22). Cortical volume and thickness were calculated using the Freesurfer software suite (v5.3; Reuter et al., 2012). Based on prior research, bilateral structures of interest included the rostral anterior cingulate cortex (rACC), insula, precuneus, and superior frontal cortex. Social measures included the WebCNP Emotion Recognition (KER40) and Emotion Differentiation Test (MED36) accuracy score (Gur et al., 2010), The Awareness of Social Inference Test Total Score (TASIT; McDonald et al., 2003), and Social Responsiveness Scale, 2nd Edition (SRS-2; Constantino &amp; Gruber, 2012). Trauma exposure was assessed using the Structured Clinical Interview Diagnostic (controls n = 5; EOP n = 9; First et al., 2015). Pearson’s correlations and independent t-tests were used to examine the relationship between cortical measurements and social cognition. Additionally, PROCESS macro (Hayes, 2018) was used to examine if trauma history statistically moderated the relationship between cortical measurements and social cognition performance.Results:Significant group differences in SRS-2 scores were observed, as EOP participants scored 24.272 points higher than controls (t = 20.724, p &lt; .001). Across both groups, there was a negative correlation between the SRS-2 score and precuneus volume (r = -.438, p = .011) and thickness (r = -.383, p = .028), TASIT total and superior frontal volume (r = -.349, p = .023), and KER40 and insular volume (r = -.437, p = .20). Further, the moderation analysis revealed that the relationships between precuneus volume and SRS-2 scores, precuneus thickness and MED36 scores, and rACC thickness and KER40 scores depended on experiencing trauma across both groups. Participants with trauma across groups had increased precuneus volume associated with higher SRS-2 scores (p = .0442). Experiencing trauma was also associated with lower precuneus cortical thickness and lower MED36 scores (p = .0172). Conversely, lack of trauma experience was associated with greater rACC thickness and higher KER40 scores (p = .0119).Conclusions:Our findings indicate that past traumatic experiences may be a moderating factor in the relationship between atypical volume and thickness of social brain regions and social cognition. Overall, the significant interactions between trauma exposure and increased volume and thickness in both EOP and control participants were associated with increased impairment on social cognition measures. These findings emphasize the importance of accounting for the impact of early life adversities on brain development and how it may be relevant to social impairments, especially in individuals experiencing psychosis.

The impact of the international code of marketing of breast-milk substitutes in Ghana and Tanzania

Abstract Background Measures that promote and protect breastfeeding have a major impact on child survival rates, as it was estimated that increasing breastfeeding rates can reduce child mortality by 13% in Africa. The International Code of Marketing of Breast-Milk Substitutes is an important measure to protect and promote breastfeeding and the safe use of commercial milk formulas. Ghana and Tanzania adopted measures that are substantially aligned with the Code into national legislation in 2000 and 1994, respectively. Methods In this study, we estimate the effects of the Code implementation through national laws on child mortality in Ghana and Tanzania by using the synthetic control group method and performing placebo tests to assess statistical inference. The main outcome is the effect of the national laws on overall child mortality and secondary outcomes are the effect of the laws on child mortality by lower respiratory infections and diarrhea in both countries. Results Two-sided inference tests showed a statistically significant treatment effect for deaths by lower respiratory infections in Ghana (p = 0.0476; LTE = - 0.8423) and one-sided inference tests showed statistically significant treatment effects for deaths by lower respiratory infections in Ghana (p = 0.0476; LTE = -0.8423) and Tanzania (p = 0.0476; LTE = - 5.1627), and for diarrhea in Tanzania (p = 0.0476; LTE = - 8.1694). To corroborate our results, we also performed the analysis for two major causes of child mortality that are not affected by the intervention, namely congenital diseases and birth asphyxia and trauma birth, for which we did not find significant treatment effects. Conclusions The results demonstrate that the Code had a potentially beneficial impact on child mortality in Ghana and Tanzania and corroborate previous research that the Code is a necessary but insufficient step to alone improve child health outcomes. Key messages • The results show that the implementation of the Code had a potentially beneficial impact on child mortality in Ghana and Tanzania. • Policymakers should consider implementing or strengthening the Code within countries in order to improve child and health outcomes.

Flexible nonlinear inference and change-point testing of high-dimensional spectral density matrices

This paper studies a flexible approach to analyze high-dimensional nonlinear time series of unconstrained dimension based on linear statistics calculated from spectral average statistics of bilinear forms and nonlinear transformations of lag-window (i.e. band-regularized) spectral density matrix estimators. That class of statistics includes, among others, smoothed periodograms, nonlinear statistics such as coherency, long-run-variance estimators and contrast statistics related to factorial effects as special cases. Especially, we introduce the class of nonlinear spectral averages of the spectral density matrix. Having in mind big data settings, we study a sampling design which includes a sparse sampling scheme. Gaussian approximations with optimal rate are derived for nonlinear time series of growing dimension for these frequency domain statistics and the underlying lag-window (cross-) spectral estimator under non-stationarity. For change-testing (self-standardized) CUSUM statistics are examined. Further, a specific wild bootstrap procedure is proposed to estimate critical values. Simulation studies and an application to SP500 financial returns are provided in a supplement to this paper.

A performance test of archaeological similarity-based network inference using New Guinean ethnographic data

Network analysis has become increasingly common within archaeological practice, yet little consensus exists as to what networks based on material culture actually reveal about ancient social life. One common approach to archaeological network inference relies on constructing similarity networks based on shared material types or stylistic categories between archaeological sites or contexts. Many studies implicitly or explicitly assume that the topology of similarity networks is a useful proxy for underlying patterns in ancient social networks, yet this basic assumption has not been rigorously evaluated. Here, I present a preliminary test of how well network measures inferred from material culture—in this case, bone daggers made on the island of New Guinea between 1845 and 2002—predict network measures derived from ethnographic accounts of social engagement between 1720 New Guinea communities. In this case study network distance partially predicts material similarity, and neighborhood/cluster identification algorithms partially identify similar patterning in underlying patterns of inter-community engagement. However, most commonly applied network measures of centrality are not strongly predicted by material cultural similarity. Similarity based network analysis is a powerful means of visualizing and exploring data, and can help in formulating archaeological hypotheses, but may be problematic as a direct inference procedure.

A - 51 The Interaction of Social Cognition Deficits and Personality Change in Progressive Supranuclear Palsy.

Small studies have suggested individuals with progressive supranuclear palsy (PSP) may have socioemotional deficits (i.e., poor emotion recognition and mentalizing). However, consideration has not been given to the interplay of social cognition and personality changes. The present study used multiple personality and social cognition measures to identify neurologically-driven socioemotional changes in PSP. Data was collected from 177 individuals: 77 with PSP and 100 older healthy controls (HC). The Awareness of Social Inference Test (TASIT) measures patients' comprehension of emotional videos (Emotion Evaluation Test: EET) and everyday social interactions (Social Inference Enriched: SI-E). Premorbid and current personality was reported by informants using the Interpersonal Adjectives Scale (IAS) and the Big Five Inventory (BFI). Groups were compared controlling for age and sex; statistical significance was set at p < 0.05. PSPs displayed impairment in reading emotions on the TASIT-EET (M:9.1 ± SD:2.66) and inferring others' intentions on the TASIT-SIE (44.8 ± 6.12) compared to HCs (11.5 ± 1.48; 53.8 ± 4.94). PSPs showed clinically (-1.5 SD) and statistically significant drops from premorbid levels in Extraversion (IAS current T: 37.0 ± 16.28; BFI: 20.1 ± 5.80) and IAS Dominance (39.9 ± 13.54). PSP patients showed significant deficits at multiple levels of emotion reading and theory of mind, impairing their comprehension of social communication. This may in turn explain the observed declines in interpersonal dominance and extraversion, possibly reflecting loss of agency and sociableness with disease progression. These results reveal more clinically significant socioemotional deficits in PSP than are generally recognized, and suggest face-to-face social testing and informant reports of personality change can be valuable testing approaches.

A - 132 Social Cognition Predicts Functional Outcomes in Serious Mental Illness.

Schizophrenia and bipolar disorder are leading causes of functional disability worldwide. Difficulty completing instrumental and more routine activities of daily living is one of the most limiting and overlooked features of these disorders. The current study investigated whether deficits in social cognition are associated with increased functional disability in overall serious mental illness (SMI). Individuals with schizophrenia (SZ; N = 22), bipolar disorder with psychotic features (BP+; N = 20) and without psychotic features (BP-; N = 21), and healthy controls (HC; N = 22) completed The Awareness of Social Inference Test (TASIT) to examine emotion identification and social inference, and The University of California, San Diego Performance-Based Skills Assessment (UPSA) to assess functional capacity. A mixed model ANOVA was performed to examine group differences and multiple regression was used to determine if social cognition predicted social functioning. The SZ group showed significantly worse UPSA functional performance compared to all other groups. The three diagnostic groups (SZ, BP+, BP-) were merged to yield an overall SMI group (N = 63). Multiple regressions indicated TASIT emotion identification and social inference predicted UPSA functioning in household maintenance, communication, finance management, and planning. Social inference was the strongest predictor of overall UPSA functioning, compared to emotion identification. TASIT scores predicted performance across several domains of functioning in SMI. Since functional outcomes are a strong indicator of clinical recovery throughout the course of SMI, additional research may investigate the role that social cognitive abilities play in this recovery.