Aversive memory formation in humans involves an amygdala-hippocampus phase code.

Memory for aversive events is central to survival but can become maladaptive in psychiatric disorders. Memory enhancement for emotional events is thought to depend on amygdala modulation of hippocampal activity. However, the neural dynamics of amygdala-hippocampal communication during emotional memory encoding remain unknown.

Neurons detect cognitive boundaries to structure episodic memories in humans.

While experience is continuous, memories are organized as discrete events. Cognitive boundaries are thought to segment experience and structure memory, but how this process is implemented remains unclear. We recorded the activity of single neurons in the human medial temporal lobe (MTL) during the formation and retrieval of memories with complex narratives.

Nucleus Accumbens Stimulation Modulates Inhibitory Control by Right Prefrontal Cortex Activation in Obsessive-Compulsive Disorder.

Inhibitory control is considered a compromised cognitive function in obsessive-compulsive (OCD) patients and likely linked to corticostriatal circuitry disturbances. Here, 9 refractory OCD patients treated with deep brain stimulation (DBS) were evaluated to address the dynamic modulations of large-scale cortical network activity involved in inhibitory control after nucleus accumbens (NAc) stimulation and their relationship with cortical thickness. A comparison of DBS "On/Off" states showed that patients committed fewer errors and exhibited increased intraindividual reaction time variability, resulting in improved goal maintenance abilities and proactive inhibitory control.

Action boosts episodic memory encoding in humans via engagement of a noradrenergic system.

We are constantly interacting with our environment whilst we encode memories. However, how actions influence memory formation remains poorly understood. Goal-directed movement engages the locus coeruleus (LC), the main source of noradrenaline in the brain.

Markov Influence Diagrams.

Markov influence diagrams (MIDs) are a new type of probabilistic graphical model that extends influence diagrams in the same way that Markov decision trees extend decision trees. They have been designed to build state-transition models, mainly in medicine, and perform cost-effectiveness analyses. Using a causal graph that may contain several variables per cycle, MIDs can model various patient characteristics without multiplying the number of states; in particular, they can represent the history of the patient without using tunnel states.