Post-hypnotic suggestion improves confidence and speed of memory access with long-lasting effects.

In our study, we use the post-hypnotic suggestion of easy remembering to improve memory with long-lasting effects. We tested 24 highly suggestible participants in an online study. Participants learned word lists and recalled them later in a recognition memory task. At the beginning of the study, participants were hypnotized and the post-hypnotic suggestion to remember easily was associated with a cue that participants used during the recognition memory task. In a control condition, the same participants used a neutral cue. One week later, participants repeated both conditions with new word lists. Participants were significantly faster and more confident in their recognition ratings in the easy-remembering condition compared to the control condition, and this effect persisted over one week. Crucially, the increased speed and confidence in the easy-remembering condition did not affect memory accuracy. That makes our hypnosis intervention promising for patients experiencing subjective memory impairments. APA PSYCINFO CODES: 2343 (Learning and Memory), 2380 (Consciousness States), 3351 (Clinical Hypnosis).

RT-Cloud: A cloud-based software framework to simplify and standardize real-time fMRI.

Real-time fMRI (RT-fMRI) neurofeedback has been shown to be effective in treating neuropsychiatric disorders and holds tremendous promise for future breakthroughs, both with regard to basic science and clinical applications. However, the prevalence of its use has been hampered by computing hardware requirements, the complexity of setting up and running an experiment, and a lack of standards that would foster collaboration. To address these issues, we have developed RT-Cloud (https://github.com/brainiak/rt-cloud), a flexible, cloud-based, open-source Python software package for the execution of RT-fMRI experiments. RT-Cloud uses standardized data formats and adaptable processing streams to support and expand open science in RT-fMRI research and applications. Cloud computing is a key enabling technology for advancing RT-fMRI because it eliminates the need for on-premise technical expertise and high-performance computing; this allows installation, configuration, and maintenance to be automated and done remotely. Furthermore, the scalability of cloud computing makes it easier to deploy computationally-demanding multivariate analyses in real time. In this paper, we describe how RT-Cloud has been integrated with open standards, including the Brain Imaging Data Structure (BIDS) standard and the OpenNeuro database, how it has been applied thus far, and our plans for further development and deployment of RT-Cloud in the coming years.

Replay of Stimulus-specific Temporal Patterns during Associative Memory Formation.

Forming a memory often entails the association of recent experience with present events. This recent experience is usually an information-rich and dynamic representation of the world around us. We here show that associating a static cue with a previously shown dynamic stimulus yields a detectable, dynamic representation of this stimulus. We further implicate this representation in the decrease of low-frequency power (∼4-30 Hz) in the ongoing EEG, which is a well-known correlate of successful memory formation. The reappearance of content-specific patterns in desynchronizing brain oscillations was observed in two sensory domains, that is, in a visual condition and in an auditory condition. Together with previous results, these data suggest a mechanism that generalizes across domains and processes, in which the decrease in oscillatory power allows for the dynamic representation of information in ongoing brain oscillations.

The Temporal Signature of Memories: Identification of a General Mechanism for Dynamic Memory Replay in Humans.

Reinstatement of dynamic memories requires the replay of neural patterns that unfold over time in a similar manner as during perception. However, little is known about the mechanisms that guide such a temporally structured replay in humans, because previous studies used either unsuitable methods or paradigms to address this question. Here, we overcome these limitations by developing a new analysis method to detect the replay of temporal patterns in a paradigm that requires participants to mentally replay short sound or video clips. We show that memory reinstatement is accompanied by a decrease of low-frequency (8 Hz) power, which carries a temporal phase signature of the replayed stimulus. These replay effects were evident in the visual as well as in the auditory domain and were localized to sensory-specific regions. These results suggest low-frequency phase to be a domain-general mechanism that orchestrates dynamic memory replay in humans.