In the current study, we used a sample of predominantly African-American women with high rates of trauma exposure (N = 434) to examine psychometric properties of the Personality Inventory for DSM-5-Brief Form (PID-5-BF). We compared model fit between a model with five correlated latent factors and a higher-order model in which the five latent factors were used to estimate a single "general pathology" factor. Additionally, we computed estimates of internal consistency and domain interrelations and examined indices of convergent/discriminant validity of the PID-5-BF domains by examining their relations to relevant criterion variables. The expected five-factor structure demonstrated good fit indices in a confirmatory factor analysis, and the more parsimonious, higher-order model was retained. Within this higher-order model, the first-order factors accounted for more variance in the criterion variables than the general pathology factor in most instances. The PID-5-BF domains were highly interrelated (s = .38 to .66), and convergent/discriminant validity of the domains varied: and generally showed the hypothesized pattern of relations with external criteria, while and displayed less consistent and discriminant relations. Results are discussed in terms of the costs and benefits of using brief pathological trait measures in samples characterized by high levels of psychopathology.
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http://dx.doi.org/10.1080/00223891.2020.1713138 | DOI Listing |
iScience
January 2025
Department of Internal Medicine, Carver College of Medicine, University of Iowa, Iowa City, IA, USA.
We and others previously found that a misannotated long noncoding RNA encodes for a conserved mitochondrial transmembrane microprotein named Mitoregulin (Mtln). Beyond an established role for Mtln in lipid metabolism, Mtln has been shown to broadly influence mitochondria, boosting respiratory efficiency and Ca retention capacity, while lowering ROS, yet the underlying mechanisms remain unresolved. Prior studies have identified possible Mtln protein interaction partners; however, a lack of consensus persists, and no claims have been made about Mtln's structure.
View Article and Find Full Text PDFJ Neurosci
January 2025
Department of Electrical and Computer Engineering, University of Maryland, College Park, MD, 20742
When we listen to speech, our brain's neurophysiological responses "track" its acoustic features, but it is less well understood how these auditory responses are enhanced by linguistic content. Here, we recorded magnetoencephalography (MEG) responses while subjects of both sexes listened to four types of continuous-speech-like passages: speech-envelope modulated noise, English-like non-words, scrambled words, and a narrative passage. Temporal response function (TRF) analysis provides strong neural evidence for the emergent features of speech processing in cortex, from acoustics to higher-level linguistics, as incremental steps in neural speech processing.
View Article and Find Full Text PDFBiological memory networks are thought to store information by experience-dependent changes in the synaptic connectivity between assemblies of neurons. Recent models suggest that these assemblies contain both excitatory and inhibitory neurons (E/I assemblies), resulting in co-tuning and precise balance of excitation and inhibition. To understand computational consequences of E/I assemblies under biologically realistic constraints we built a spiking network model based on experimental data from telencephalic area Dp of adult zebrafish, a precisely balanced recurrent network homologous to piriform cortex.
View Article and Find Full Text PDFMach Learn Appl
June 2024
McGill University Department of Biostatistics, 805 rue Sherbrooke O, Montréal, H3A 0B9, Quebec, Canada.
In the context of survival analysis, data-driven neural network-based methods have been developed to model complex covariate effects. While these methods may provide better predictive performance than regression-based approaches, not all can model time-varying interactions and complex baseline hazards. To address this, we propose Case-Base Neural Networks (CBNNs) as a new approach that combines the case-base sampling framework with flexible neural network architectures.
View Article and Find Full Text PDFNeuroimage
January 2025
Academy of Wellness and Human Development, Hong Kong Baptist University, Hong Kong, China.
The ability to infer a speaker's utterance within a particular context for the intended meaning is central to communication. Yet, little is known about the underlying neurocomputational mechanisms of pragmatic inference, let alone relevant differences among individuals. Here, using a reference game combined with model-based functional magnetic resonance imaging (fMRI), we showed that an individual-level pragmatic inference model was a better predictor of listeners' performance than a population-level model.
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