Contemporary symptom-based diagnosis of post-traumatic stress disorder (PTSD) largely overlooks related neurobehavioral mechanisms and relies entirely on subjective interpersonal reporting. Previous studies associating biomarkers with PTSD have mostly used symptom-based diagnosis as the main outcome measure, disregarding the wide variability and richness of PTSD phenotypical features. Here, we aimed to computationally derive potential biomarkers that could efficiently differentiate PTSD subtypes among recent trauma survivors. A three-staged semiunsupervised method ("3C") was used to firstly categorize individuals by current PTSD symptom severity, then derive clusters based on clinical features related to PTSD (e.g. anxiety and depression), and finally to classify participants' cluster membership using objective multi-domain features. A total of 256 features were extracted from psychometrics, cognitive functioning, and both structural and functional MRI data, obtained from 101 adult civilians (age = 34.80 ± 11.95; 51 females) evaluated within 1 month of trauma exposure. The features that best differentiated cluster membership were assessed by importance analysis, classification tree, and ANOVA. Results revealed that entorhinal and rostral anterior cingulate cortices volumes (structural MRI domain), in-task amygdala's functional connectivity with the insula and thalamus (functional MRI domain), executive function and cognitive flexibility (cognitive testing domain) best differentiated between two clusters associated with PTSD severity. Cross-validation established the results' robustness and consistency within this sample. The neural and cognitive potential biomarkers revealed by the 3C analytics offer objective classifiers of post-traumatic morbidity shortly following trauma. They also map onto previously documented neurobehavioral mechanisms associated with PTSD and demonstrate the usefulness of standardized and objective measurements as differentiating clinical sub-classes shortly after trauma.

Rational choice theory assumes optimality in decision-making. Violations of a basic axiom of economic rationality known as “Independence of Irrelevant Alternatives” (IIA) have been demonstrated in both humans and animals and could stem from common neuronal constraints. Here we develop tests for IIA in the nematode Caenorhabditis elegans , an animal with only 302 neurons, using olfactory chemotaxis assays. We find that in most cases C. elegans make rational decisions. However, by probing multiple neuronal architectures using various choice sets, we show that violations of rationality arise when the circuit of olfactory sensory neurons is asymmetric. We further show that genetic manipulations of the asymmetry between the AWC neurons can make the worm irrational. Last, a context-dependent normalization-based model of value coding and gain control explains how particular neuronal constraints on information coding give rise to irrationality. Thus, we demonstrate that bounded rationality could arise due to basic neuronal constraints.

How convincing is current evidence for unconscious processing? Recently, a major criticism suggested that this evidence might be fully explained by a mere statistical phenomenon: regression to the mean (RttM). Since excluding participants based on an awareness assessment is a common practice in such studies, this post-hoc data selection might evoke RttM and lead to false effects that are driven by aware participants wrongfully classified as unaware. Here, we examined this criticism using both simulations and data from 15 studies probing unconscious processing (43 effects overall). In line with the original criticism, we confirmed that the reliability of awareness measures in the field is concerningly low. Yet using simulations we showed that reliability measures might be unsuitable for estimating error in awareness measures. Furthermore, we examined three proposed ways to assess whether an effect is genuine or reflects RttM; all suffered from substantial limitations, such as a lack of power or an unjustified linearity assumption. Accordingly, we suggest a new nonparametric solution, which enjoys high sensitivity and relatively high power. Together, this work emphasizes the need to account for the contribution of measurement error to effects of unconscious processing. It further suggests a way to meet the important challenge posed by RttM, in an attempt to establish a reliable and robust corpus of knowledge in studying unconscious processing.

Rational choice theory in economics assumes optimality indecision-making.One of the basic axioms of economic rationality is "Independence of Irrelevant Alternatives" (IIA), according to which a preference ratio between two options should be unaffected by introducing additional alternatives to the choice set.Violations of IIA have been demonstrated in both humans and in various animals, and could therefore stem from common neuronal constraints. We . CC-BY-NC-ND 4.0 International license not peer-reviewed) is the author/funder. It is made available under aThe copyright holder for this preprint (which was . http://dx.doi.org/10.1101/257535 doi: bioRxiv preprint first posted online Jan. 31, 2018; used the nematode Caenorhabditis elegans, an animal with only 302 neurons and a fully mapped connectome, to examine when and why economic rationality and violations of rationality occur. We developed tests for IIA violations by characterizing the choices that C. elegans make in olfactory chemotaxis assays. In each assay, we exposed the worm to different odors that activate only specific neurons, thus involving in the choice process only defined neuronal networks, and tested whether particular neuronal architectures are prone to producing irrational choices. We found that C. elegans are capable of maintaining robust binary olfactory preferences irrespectively of the presence of a third attractive odor. However, in very specific olfactory contexts, which we term asymmetric overlaps, the preference ratio between the two odors was altered due to the addition of a third inferior odor, in a manner that violates IIA, and in certain cases can be considered "irrational" based on the economic definition of rationality. Our results suggest that different network configurations vary in their propensity to give rise to inconsistent decision making. Thus, non-optimal choices, assumed to be an outcome of high-order cognitive and mental processes, could result from much more basic attributes of neuronal activity and constrained computational mechanisms.