Neuroinflammation and Its Resolution: From Molecular Mechanisms to Therapeutic Perspectives

Neuroinflammation, the complex immune response of the central nervous system (CNS), when sustained, is a common denominator in the etiology and course of all major neurological diseases, including neurodevelopmental, neurodegenerative, and psychiatric disorders (e.g., Alzheimer's disease, AD; Parkinson's disease, PD; multiple sclerosis, MS; motor neuron disease; depression; autism spectrum disorder; and schizophrenia). Cellular (microglia and mast cells, two brain-resident immune cells, together with astrocytes) and molecular immune components (e.g., cytokines, complement and patternrecognition receptors) act as key regulators of neuroinflammation (Skaper et al., 2012). In response to pathological triggers or neuronal damage, immune cells start an innate immune response with the aim to eliminate the initial cause of injury. However, when the cellular activity becomes dysregulated, it results in an inappropriate immune response that can be injurious and affect CNS functions. Thus, limiting neuroinflammation and microglia activity represents a potential strategy to alleviate neuroinflammationrelated diseases. The Research Topic collects 20 manuscripts, divided into five sections, that include both original research articles and reviews of the emerging literature and explore the role of neuroinflammation in various neurological diseases. There is particular attention dedicated to the relevant research exploring the mechanisms and mediators involved in the resolution of neuroinflammation. Our aim was to generate a valuable discussion contributing to identify new therapeutic targets in brain damage and providing new drug development opportunities for the prevention and treatment of CNS diseases involving neuroinflammation

Neuro-cognitive processes as mediators of psychological treatment effects

Psychological interventions are first-line treatments of depression. Despite a rich theoretical background, the mediators of treatment effects remain only partially understood: it has been difficult to precisely delineate the targets psychological interventions engage, and even more difficult to differentiate amongst the targets engaged by different psychological interventions. Here, we outline these issues and discuss a surprisingly understudied approach, namely the study of cognitive and computational tasks to measure psychological treatment targets. Such tasks benefit from substantial advances in cognitive neuroscience over the past two decades, and have excellent face validity. We discuss two candidate tasks for back-translation and conclude with a critical evaluation of potential problems associated with this neuro-cognitive approach

Low predictive power of clinical features for relapse prediction after antidepressant discontinuation in a naturalistic setting

The risk of relapse after antidepressant medication (ADM) discontinuation is high. Predictors of relapse could guide clinical decision-making, but are yet to be established. We assessed demographic and clinical variables in a longitudinal observational study before antidepressant discontinuation. State-dependent variables were re-assessed either after discontinuation or before discontinuation after a waiting period. Relapse was assessed during 6 months after discontinuation. We applied logistic general linear models in combination with least absolute shrinkage and selection operator and elastic nets to avoid overfitting in order to identify predictors of relapse and estimated their generalisability using cross-validation. The final sample included 104 patients (age: 34.86 (11.1), 77% female) and 57 healthy controls (age: 34.12 (10.6), 70% female). 36% of the patients experienced a relapse. Treatment by a general practitioner increased the risk of relapse. Although within-sample statistical analyses suggested reasonable sensitivity and specificity, out-of-sample prediction of relapse was at chance level. Residual symptoms increased with discontinuation, but did not relate to relapse. Demographic and standard clinical variables appear to carry little predictive power and therefore are of limited use for patients and clinicians in guiding clinical decision-making

A social inference model of idealization and devaluation

People often form polarized beliefs, imbuing objects (e.g., themselves or others) with unambiguously positive or negative qualities. In clinical settings, this is referred to as dichotomous thinking or "splitting" and is a feature of several psychiatric disorders. Here, we introduce a Bayesian model of splitting that parameterizes a tendency to rigidly categorize objects as either entirely "Bad" or "Good," rather than to flexibly learn dispositions along a continuous scale. Distinct from the previous descriptive theories, the model makes quantitative predictions about how dichotomous beliefs emerge and are updated in light of new information. Specifically, the model addresses how splitting is context-dependent, yet exhibits stability across time. A key model feature is that phases of devaluation and/or idealization are consolidated by rationally attributing counter-evidence to external factors. For example, when another person is idealized, their less-than-perfect behavior is attributed to unfavorable external circumstances. However, sufficient counter-evidence can trigger switches of polarity, producing bistable dynamics. We show that the model can be fitted to empirical data, to measure individual susceptibility to relational instability. For example, we find that a latent categorical belief that others are "Good" accounts for less changeable, and more certain, character impressions of benevolent as opposed to malevolent others among healthy participants. By comparison, character impressions made by participants with borderline personality disorder reveal significantly higher and more symmetric splitting. The generative framework proposed invites applications for modeling oscillatory relational and affective dynamics in psychotherapeutic contexts. (PsycInfo Database Record (c) 2023 APA, all rights reserved)

The relationship between resting-state functional connectivity, antidepressant discontinuation and depression relapse

The risk of relapsing into depression after stopping antidepressants is high, but no established predictors exist. Resting-state functional magnetic resonance imaging (rsfMRI) measures may help predict relapse and identify the mechanisms by which relapses occur. rsfMRI data were acquired from healthy controls and from patients with remitted major depressive disorder on antidepressants. Patients were assessed a second time either before or after discontinuation of the antidepressant, and followed up for six months to assess relapse. A seed-based functional connectivity analysis was conducted focusing on the left subgenual anterior cingulate cortex and left posterior cingulate cortex. Seeds in the amygdala and dorsolateral prefrontal cortex were explored. 44 healthy controls (age: 33.8 (10.5), 73% female) and 84 patients (age: 34.23 (10.8), 80% female) were included in the analysis. 29 patients went on to relapse and 38 remained well. The seed-based analysis showed that discontinuation resulted in an increased functional connectivity between the right dorsolateral prefrontal cortex and the parietal cortex in non-relapsers. In an exploratory analysis, this functional connectivity predicted relapse risk with a balanced accuracy of 0.86. Further seed-based analyses, however, failed to reveal diferences in functional connectivity between patients and controls, between relapsers and non-relapsers before discontinuation and changes due to discontinuation independent of relapse. In conclusion, changes in the connectivity between the dorsolateral prefrontal cortex and the posterior default mode network were associated with and predictive of relapse after open-label antidepressant discontinuation. This fnding requires replication in a larger dataset

Implementing precision methods in personalizing psychological therapies: barriers and possible ways forward

This is the final version. Available on open access from Elsevier via the DOI in this recordData availability: No data was used for the research described in the article.Highlights: • Personalizing psychological treatments means to customize treatment for individuals to enhance outcomes. • The application of precision methods to clinical psychology has led to data-driven psychological therapies. • Applying data-informed psychological therapies involves clinical, technical, statistical, and contextual aspects

Experimental Investigation and Modelling of the Formation Kinetics of CuInSe2-based Semiconductor Thin-Films for Solar Cell Production

Die Chalkopyrit-Halbleiter CuInSe2 bzw. Cu(In,Ga)Se2 werden als Absorbermaterialien bei der industriellen Produktion der sog. CIS-Solarzellen eingesetzt. Die stark lichtabsorbierenden Dünnschichten werden dabei im sog. Stacked-Elemental-Layer (SEL) Prozess hergestellt. Dieses Verfahren ist dadurch gekennzeichnet, dass zunächst metallische Schichtstapel und Selen durch physikalische Depositionsverfahren auf ein Substrat aufgebracht werden. Aus diesem sog. Precursor wird anschließend durch thermische Prozessierung (bis etwa 550 °C) der Halbleiterabsorber synthetisiert. In dieser Arbeit wurden die chemischen Reaktionswege bei der SEL-Synthese dieser Chalkopyrite aufgeklärt. Dazu wurden Teilsysteme des CuInSe2 bzw. Cu(In,Ga)Se2 mittels dynamischer Differenz-Kalorimetrie, Röntgendiffraktometrie und in-situ Leitfähigkeitsmessung untersucht. Darüber hinaus konnten mit Hilfe der kalorimetrischen und Leitfähigkeits-Analysen die ratenbestimmenden Schritte, d.h. die kinetischen Mechanismen, bei der Selenisierung der metallischen Schichten und der Umwandlungen binärer Indium- und Galliumselenide ermittelt werden. Diese kinetischen Analysen wurden durch thermodynamische Modelle der binären Selenide ergänzt. Die Reaktionswege und Mechanismen bei der Synthese von CuInSe2 bzw. Cu(In,Ga)Se2 im SEL-Verfahren sind im Folgenden kurz zusammengefasst: CuInSe2: Aus den metallischen Schichten entsteht zunächst eine Cu11In9-Phase, die durch Selen in einer gerichteten, von der Kinetik der Grenzfläche kontrollierten Reaktion abgebaut wird. Der dominierende Vorgang ist dabei die Herauslösung des Kupfers aus dem Cu11In9, wobei CuSe2 entsteht. Dabei wird aus der intermetallischen Verbindung Cu11In9 elementares Indium freigesetzt, welches rasch zu In4Se3 weiterreagiert. Dessen anschließende Umwandlung in InSe wird durch Keimbildungs- und Kornwachstumsprozesse bestimmt. Bei der Bildung dieser Indiumselenide wird der Selenvorrat des Precursors vollständig verbraucht, weswegen die Umwandlung des CuSe2 zu CuSe erzwungen wird. CuSe wandelt sich schließlich peritektisch in Cu(2-x)Se um. Sowohl aus CuSe als auch aus Cu(2-x)Se entsteht mit InSe im Temperaturbereich von 340-550 °C schließlich CuInSe2. Cu(In,Ga)Se2: Die intermetallischen Reaktionen im quaternären Precursor bilden evtl. Cu9Ga4 und schließlich Cu11(In,Ga)9. Diese Phase wird durch Reaktion mit Selen abgebaut, wobei sich wiederum Cu9Ga4 am Substrat anreichert. Cu9Ga4 wird schließlich unter Bildung von Cu(Ga)Se2 selenisiert, welches in GaSe und CuSe zerfällt. Erst nach Umwandlung des CuSe in Cu(2-x)Se kann der Chalkopyrit CuGaSe2 entstehen. Parallel zu diesen Reaktionen verlaufen identisch zum ternären System die Reaktionen des Indiums unter Synthese von CuInSe2. Dies bildet mit CuGaSe2 am Ende des Reaktionsprozesses die Halbleiter-Mischkristallphase Cu(In,Ga)Se2 bei Temperaturen von 430-550 °C. Zur Identifikation der kinetischen Mechanismen der einzelnen Reaktionen wurden halbquantitative numerische Modellierungen genutzt. Sie erlauben u.a. eine Vorhersage der Reaktionsraten bei beliebigen Temperatur-Zeit Verläufen des Syntheseprozesses. Mit diesen Reaktionsmodellen wurde auch der Einfluss des Precursoraufbaus auf die Synthesereaktionen und deren Reproduzierbarkeit untersucht. Damit können verschiedene Überlegungen zur Optimierung des industriellen SEL-Prozesses abgeleitet werden. Es zeigt sich, dass die Morphologie der Metallschichten des Precursors nur geringen Einfluss auf die Synthesekinetik der Absorber hat. Dagegen ist die Verteilung der Phasen in den Metallschichten des Precursors von großer Bedeutung für die Reproduzierbarkeit des SEL-Prozesses. Insbesondere sind langsam reagierende oder entnetzende Zwischenschichten zu vermeiden. Situationen, in denen verschiedene konkurrierende Reaktionen einer Phase möglich sind, führen ebenfalls nicht zu reproduzierbaren Reaktionsabläufen. Daher sollte beispielsweise im Prozess vermieden werden, dass sich Ga-reiche Phasen wie Cu9Ga4 oder elementares Ga zwischen Ga-armen Phasen und Selen befinden. Als weitere Erkenntnis konnten die Ursachen für die im SEL-Prozess auftretende Segregation des Galliums an der Rückseite des Absorbermaterials aufgeklärt werden. Die Chalkopyritsynthese läuft, wie diese Arbeit zeigt, von der Precursoroberfläche in Richtung des Substrats ab. Dabei kommt es zum einen durch eine Anreicherung des Galliums am Substrat in den metallischen Precursorschichten bereits in einem frühen Stadium des Syntheseprozesses zu einer inhomogenen Ga-Verteilung. Zum anderen verzögern verschiedene Mechanismen bei der Selenisierung des Galliums die Bildung von CuGaSe2. Somit kristallisiert erst am Ende des SEL-Prozesses, und damit an der Rückseite des Absorbers, Ga-reicher Chalkopyrit.The chalcopyrite semiconductors CuInSe2 or Cu(In,Ga)Se2 are used as an absorber material for the industrial production of the so-called CIS solar cells. The thin-films are manufactured by the so-called stacked-elemental-layer (SEL) process. This technique is characterized by the physical deposition of metallic layers and selenium on a substrate. From this so-called precursor the semiconducting absorber is then synthesized by thermal processing (up to about 550 °C). In this work the chemical reaction pathways of the SEL-synthesis of these chalcopyrites were determined. For this purpose subsystems of CuInSe2 and Cu(In,Ga)Se2, respectively, were examined by dynamic scanning calorimetry, X-ray diffraction and in-situ conductivity measurements. Moreover, the rate-controlling reaction steps, i.e. the kinetic mechanisms, of the selenization of the metallic thin-films and the transformations of binary indium- and gallium-selenides could be determined by these calorimetric and conductivity measurements. These kinetic analyses were complemented by thermodynamic models of the binary selenides. The chemical reaction pathways and mechanisms of the synthesis of CuInSe2 and Cu(In,Ga)Se2, respectively, by the SEL method are summarized below: CuInSe2: First, from the metallic thin-films a Cu11In9 phase is formed, which is decomposed by selenium in a directional process kinetically controlled by a phase boundary reaction. The extraction of the Cu-atoms from the Cu11In9 crystal structure and the formation of CuSe2 dominate this process. Thereby the intermetallic compound Cu11In9 releases elemental Indium which quickly forms In4Se3. Its subsequent transformation into InSe is kinetically controlled by nucleation and growth processes. By the formation of the indium selenides the selenium supply of the precursor is completely consumed which forces CuSe2 to transform into CuSe. This phase decomposes peritectically into Cu(2-x)Se. Together with InSe, CuSe as well as Cu(2-x)Se finally synthesize CuInSe2 at temperatures of 340-550 °C. Cu(In,Ga)Se2: The intermetallic reactions in the quaternary precursor eventually form Cu9Ga4 and finally Cu11(In,Ga)9. Latter phase is decomposed by a reaction with selenium, during which Cu9Ga4 is again accumulated at the substrate. Cu9Ga4 is finally selenized forming Cu(Ga)Se2, which decomposes to GaSe and CuSe. Only after the transformation of CuSe into Cu(2-x)Se the chalcopyrite CuGaSe2 can synthesize. At the same time the reactions of the indium take place, which are identical to the ternary system and form CuInSe2. At the end of the reaction process CuInSe2, together with CuGaSe2, finally forms the solid solution phase Cu(In,Ga)Se2 at temperatures of 430-550 °C. Semiquantitative numeric models were used to identify the kinetic mechanisms of the various reactions. These models e.g. allow for a prediction of the reaction rates in arbitrary temperature-time characteristics of the synthesis process. Moreover, with these reaction models the influence of the precursor structure on the chalcopyrite synthesis reactions and their reproducibility was investigated. Several conclusions for the optimization of the industrial SEL process can be drawn. It can be seen that the morphology of the metallic precursor thin-films only has a small influence on the kinetics of the absorber synthesis. On the other hand, the distribution of the phases in the metallic precursor thin-films is crucial for the reproducibility of the SEL process. Especially slowly reacting or dewetting intermediate layers must be avoided. Situations in which a phase can participate in several competing reactions also cannot lead to reproducible reaction kinetics. Therefore it should e.g. be avoided that during the process Ga-rich phases like Cu9Ga4 or elemental Ga are situated in between Ga-poor phases and selenium. As a further result the reasons for the occurring segregation of gallium at the lower surface of the SEL-processed absorber material were clarified. As this work shows, the synthesis of the chalcopyrite in the SEL process is directional from the surface of the precursor to the substrate. Thereby, gallium already accumulates at the substrate during an early state of the thermal process, causing an inhomogeneous Ga-distribution in the metallic precursor thin-films. Second, several mechanisms during the selenization of gallium delay the formation of CuGaSe2 compared to CuInSe2. Therefore, a Ga-rich chalcopyrite can only crystallize at the very end of the SEL process, i.e. near the substrate