Can a Predictive Processing Framework Improve the Specification of Negative Bias in Depression?

Storm on predictive brain: A neurocomputational account of ketamine antidepressant effect

For the past decade, ketamine, an N-methyl-D-aspartate receptor (NMDAr) antagonist, has been considered a promising treatment for major depressive disorder (MDD). Unlike the delayed effect of monoaminergic treatment, ketamine may produce fast-acting antidepressant effects hours after a single administration at subanesthetic dose. Along with these antidepressant effects, it may also induce transient dissociative (disturbing of the sense of self and reality) symptoms during acute administration which resolve within hours. To understand ketamine's rapid-acting antidepressant effect, several biological hypotheses have been explored, but despite these promising avenues, there is a lack of model to understand the timeframe of antidepressant and dissociative effects of ketamine. In this article, we propose a neurocomputational account of ketamine's antidepressant and dissociative effects based on the Predictive Processing (PP) theory, a framework for cognitive and sensory processing. PP theory suggests that the brain produces top-down predictions to process incoming sensory signals, and generates bottom-up prediction errors (PEs) which are then used to update predictions. This iterative dynamic neural process would relies on N-methyl-D-aspartate (NMDAr) and α-amino-3-hydroxy-5-methyl-4-isoxazole-propionic receptors (AMPAr), two major component of the glutamatergic signaling. Furthermore, it has been suggested that MDD is characterized by over-rigid predictions which cannot be updated by the PEs, leading to miscalibration of hierarchical inference and self-reinforcing negative feedback loops. Based on former empirical studies using behavioral paradigms, neurophysiological recordings, and computational modeling, we suggest that ketamine impairs top-down predictions by blocking NMDA receptors, and enhances presynaptic glutamate release and PEs, producing transient dissociative symptoms and fast-acting antidepressant effect in hours following acute administration. Moreover, we present data showing that ketamine may enhance a delayed neural plasticity pathways through AMPAr potentiation, triggering a prolonged antidepressant effect up to seven days for unique administration. Taken together, the two sides of antidepressant effects with distinct timeframe could constitute the keystone of antidepressant properties of ketamine. These PP disturbances may also participate to a ketamine-induced time window of mental flexibility, which can be used to improve the psychotherapeutic process. Finally, these proposals could be used as a theoretical framework for future research into fast-acting antidepressants, and combination with existing antidepressant and psychotherapy.

[Belief updating and mood congruence in depressive disorder]

Depressive disorder is characterized by a polymorphic symptomatology associating emotional, cognitive and behavioral disturbances. One of the most specific symptoms is negative beliefs, called congruent to mood. Despite the importance of these beliefs in the development, the maintenance, and the recurrence of depressive episodes, little is known about the processes underlying the generation of depressive beliefs. In this paper, we detail the link between belief updating mechanisms and the genesis of depressive beliefs. We show how depression alters information processing, generating cognitive immunization when processing positive information, affective updating bias related to the valence of belief and prediction error, and difficultie to disengage from negative information. We suggest that disruption of belief-updating mechanisms forms the basis of belief-mood congruence in depression.

“Ich denke, also sage ich vorher”: Wie “Predictive Processing-Modelle den Einsatz von Verhaltensexperimenten bei Depressionen optimieren können

Verhaltensexperimente stellen eine wichtige Interventionsform bei depressiven Störungen dar, um dysfunktionale Annahmen zu modifizieren. Häufig ist jedoch zu beobachten, dass Patient*innen trotz einer fachgerechten Durchführung von Verhaltensexperimenten weiter an negativen Annahmen festhalten. Vor diesem Hintergrund diskutieren wir in diesem Artikel, wie der Einsatz von Verhaltensexperimenten bei Depressionen optimiert werden kann und beziehen uns dabei auf ein neues Störungsmodell, das interessante Implikationen hierzu liefern könnte. Dieses Störungsmodell aus der neurowissenschaftlichen Forschung zu “Predictive Processing” geht davon aus, dass depressive Störungen durch zwei Kernaspekte gekennzeichnet sind: (1) stark negative Erwartungen, die im Sinne von selbsterfüllenden Pro­phezeiungen zu negativen Erlebnissen führen und zuneh­mend “immun” gegen gegenteilige Erfahrungen machen; (2) das Fehlen von positiven Erwartungen, wodurch die Wahr­scheinlichkeit von positiven Erlebnissen reduziert wird. Darauf aufbauend beschreiben wir zunächst, dass zur Modifikation negativer Erwartungen in einer ausführlichen Vorbesprechung erarbeitet werden sollte, welche möglichen positiven Erfahrungen aus dem Verhaltensexperiment die Patient*innen als glaubwürdig betrachten und zur Veränderung ihrer negativen Erwartungen nutzen würden. Auf diese sollte bei der Durchführung besonders geachtet werden, damit positive Erwartungsverletzungen auch als solche wahrgenommen werden. Zum anderen stellen wir dar, dass es nicht ausreichend ist, nur negative Erwartungen zu redu­zieren, sondern auch explizit positive Erwartungen durch weitere Verhaltensexperimente gefördert werden sollten.