Validation of the face-name pairs task in major depression: impaired recall but not recognition

Major depression can be associated with neurocognitive deficits which are believed in part to be related to medial temporal lobe pathology. The purpose of this study was to investigate this impairment using a hippocampal-dependent neuropsychological task. The face-name pairs task was used to assess associative memory functioning in 19 patients with major depression. When compared to age-sex-and-education matched controls, patients with depression showed impaired learning, delayed cued-recall, and delayed free-recall. However, they also showed preserved recognition of the verbal and nonverbal components of this task. Results indicate that the face-name pairs task is sensitive to neurocognitive deficits in major depression.Thisresearchwasfundedbya4-yearHealthResearch Board grant

Cosmogenic-neutron activation of TeO2 and implications for neutrinoless double- β decay experiments

Flux-averaged cross sections for cosmogenic-neutron activation of natural tellurium were measured using a neutron beam containing neutrons of kinetic energies up to ∼800 MeV and having an energy spectrum similar to that of cosmic-ray neutrons at sea level. Analysis of the radioisotopes produced reveals that Ag110m will be a dominant contributor to the cosmogenic-activation background in experiments searching for neutrinoless double-β decay of Te130, such as the Cryogenic Underground Observatory for Rare Events (CUORE) and the Sudbury Neutrino Observatory Plus (SNO+). An estimate of the cosmogenic-activation background in the CUORE experiment has been obtained using the results of this measurement and cross-section measurements of proton activation of tellurium. Additionally, the measured cross sections in this work are also compared with results from semiempirical cross-section calculations

Axonal protection achieved by blockade of sodium/calcium exchange in a new model of ischemia in vivo.

Ischemic white matter injury has been relatively little studied despite its importance to the outcome of stroke. To aid such research a new rat model has been developed in vivo and used to assess whether blockade of the sodium/calcium exchanger is effective in protecting central axons from ischemic injury. Vasoconstrictive agent endothelin-1 was injected into the rat spinal cord to induce ischemia. KB-R7943 or SEA0400 was administered systemically to block the operation of the sodium/calcium exchanger. Endothelin-1 caused profound reduction of local blood perfusion and resulted in a prompt loss of axonal conduction. Whereas recovery of conduction following vehicle administration was only to 10.5 ± 9% of baseline (n = 8) 4.5 h after endothelin-1 injection, recovery following KB-R7943 (30 mg/kg, i.a.) administration was increased to 35 ± 9% of baseline (n = 6; P < 0.001). SEA0400 (30 mg/kg, i.a.) was also protective (33.2 ± 6% of baseline, n = 4; P < 0.001). Neither drug improved conduction by diminishing the severity of the ischemia. The protective effect of KB-R7943 persisted for at least 3 days after ischemia, as it improved axonal conduction (76.3 ± 11% for KB-R7943 vs. 51.0 ± 19% for vehicle; P < 0.01) and reduced lesion area (55.6 ± 15% for KB-R7943 vs. 77.9 ± 9% for vehicle; P < 0.01) at this time. In conclusion, a new model of white matter ischemia has been introduced suitable for both structural and functional studies in vivo. Blocking the sodium/calcium exchanger protects central axons from ischemic injury in vivo

Experimental autoimmune encephalomyelitis from a tissue energy perspective

Increasing evidence suggests a key role for tissue energy failure in the pathophysiology of multiple sclerosis (MS). Studies in experimental autoimmune encephalomyelitis (EAE), a commonly used model of MS, have been instrumental in illuminating the mechanisms that may be involved in compromising energy production. In this article, we review recent advances in EAE research focussing on factors that conspire to impair tissue energy metabolism, such as tissue hypoxia, mitochondrial dysfunction, production of reactive oxygen/nitrogen species, and sodium dysregulation, which are directly affected by energy insufficiency, and promote cellular damage. A greater understanding of how inflammation affects tissue energy balance may lead to novel and effective therapeutic strategies that ultimately will benefit not only people affected by MS but also people affected by the wide range of other neurological disorders in which neuroinflammation plays an important role

Simulating Emotions: An Active Inference Model of Emotional State Inference and Emotion Concept Learning

The ability to conceptualize and understand one’s own affective states and responses – or “Emotional awareness” (EA) – is reduced in multiple psychiatric populations; it is also positively correlated with a range of adaptive cognitive and emotional traits. While a growing body of work has investigated the neurocognitive basis of EA, the neurocomputational processes underlying this ability have received limited attention. Here, we present a formal Active Inference (AI) model of emotion conceptualization that can simulate the neurocomputational (Bayesian) processes associated with learning about emotion concepts and inferring the emotions one is feeling in a given moment. We validate the model and inherent constructs by showing (i) it can successfully acquire a repertoire of emotion concepts in its “childhood”, as well as (ii) acquire new emotion concepts in synthetic “adulthood,” and (iii) that these learning processes depend on early experiences, environmental stability, and habitual patterns of selective attention. These results offer a proof of principle that cognitive-emotional processes can be modeled formally, and highlight the potential for both theoretical and empirical extensions of this line of research on emotion and emotional disorders

Statins, bugs and prophylaxis: intriguing possibilities

Statin therapy may represent a potential prophylactic intervention in certain high-risk scenarios, for example in pandemic influenza and in those undergoing aggressive medical treatments. Emerging data indicate a potential prophylactic role in these high-risk groups

An Active Inference Approach to Modeling Structure Learning: Concept Learning as an Example Case

Within computational neuroscience, the algorithmic and neural basis of structure learning remains poorly understood. Concept learning is one primary example, which requires both a type of internal model expansion process (adding novel hidden states that explain new observations), and a model reduction process (merging different states into one underlying cause and thus reducing model complexity via meta-learning). Although various algorithmic models of concept learning have been proposed within machine learning and cognitive science, many are limited to various degrees by an inability to generalize, the need for very large amounts of training data, and/or insufficiently established biological plausibility. Using concept learning as an example case, we introduce a novel approach for modeling structure learning—and specifically state-space expansion and reduction—within the active inference framework and its accompanying neural process theory. Our aim is to demonstrate its potential to facilitate a novel line of active inference research in this area. The approach we lay out is based on the idea that a generative model can be equipped with extra (hidden state or cause) “slots” that can be engaged when an agent learns about novel concepts. This can be combined with a Bayesian model reduction process, in which any concept learning—associated with these slots—can be reset in favor of a simpler model with higher model evidence. We use simulations to illustrate this model’s ability to add new concepts to its state space (with relatively few observations) and increase the granularity of the concepts it currently possesses. We also simulate the predicted neural basis of these processes. We further show that it can accomplish a simple form of “one-shot” generalization to new stimuli. Although deliberately simple, these simulation results highlight ways in which active inference could offer useful resources in developing neurocomputational models of structure learning. They provide a template for how future active inference research could apply this approach to real-world structure learning problems and assess the added utility it may offer

Neurocomputational mechanisms underlying emotional awareness: Insights afforded by deep active inference and their potential clinical relevance

Emotional awareness (EA) is recognized as clinically relevant to the vulnerability to, and maintenance of, psychiatric disorders. However, the neurocomputational processes that underwrite individual variations remain unclear. In this paper, we describe a deep (active) inference model that reproduces the cognitive-emotional processes and self-report behaviors associated with EA. We then present simulations to illustrate (seven) distinct mechanisms that (either alone or in combination) can produce phenomena – such as somatic misattribution, coarse-grained emotion conceptualization, and constrained reflective capacity – characteristic of low EA. Our simulations suggest that the clinical phenotype of impoverished EA can be reproduced by dissociable computational processes. The possibility that different processes are at work in different individuals suggests that they may benefit from distinct clinical interventions. As active inference makes particular predictions about the underlying neurobiology of such aberrant inference, we also discuss how this type of modelling could be used to design neuroimaging tasks to test predictions and identify which processes operate in different individuals – and provide a principled basis for personalized precision medicine