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.

Cloud-Based Functional Magnetic Resonance Imaging Neurofeedback to Reduce the Negative Attentional Bias in Depression: A Proof-of-Concept Study.

Individuals with depression show an attentional bias toward negatively valenced stimuli and thoughts. In this proof-of-concept study, we present a novel closed-loop neurofeedback procedure intended to remediate this bias. Internal attentional states were detected in real time by applying machine learning techniques to functional magnetic resonance imaging data on a cloud server; these attentional states were externalized using a visual stimulus that the participant could learn to control. We trained 15 participants with major depressive disorder and 12 healthy control participants over 3 functional magnetic resonance imaging sessions. Exploratory analysis showed that participants with major depressive disorder were initially more likely than healthy control participants to get stuck in negative attentional states, but this diminished with neurofeedback training relative to controls. Depression severity also decreased from pre- to posttraining. These results demonstrate that our method is sensitive to the negative attentional bias in major depressive disorder and showcase the potential of this novel technique as a treatment that can be evaluated in future clinical trials.

The hippocampus as a visual area organized by space and time: A spatiotemporal similarity hypothesis.

The hippocampus is the canonical memory system in the brain and is not typically considered part of the visual system. Yet, it sits atop the ventral visual stream and has been implicated in certain aspects of vision. Here I review the place of the hippocampal memory system in vision science. After a brief primer on the local circuity, external connectivity, and computational functions of the hippocampus, I explore what can be learned from each field about the other. I first present four areas of vision science (scene perception, imagery, eye movements, attention) that challenge our current understanding of the hippocampus in terms of its role in episodic memory. In the reverse direction, I leverage this understanding to inform vision science in other ways, presenting a working hypothesis about a unique form of visual representation. This spatiotemporal similarity hypothesis states that the hippocampus represents objects according to whether they co-occur in space and/or time, and not whether they look alike, as elsewhere in the visual system. This tuning may reflect hippocampal mechanisms of pattern separation, relational binding, and statistical learning, allowing the hippocampus to generate visual expectations to facilitate search and recognition.

Attentional bias in depression: understanding mechanisms to improve training and treatment.

One of the most common symptoms of depression is the tendency to attend to negative stimuli in the world and negative thoughts in mind. This symptom is especially nefarious because it is also a cause - biasing processing to negatively valenced information, thus worsening mood, and exacerbating the condition. Here we attempt to systematize the diverse body of recent research on the negative attentional bias from across cognitive and clinical psychology in order to identify recurring themes and devise potential mechanistic explanations. We leverage theoretical progress in our understanding of healthy attention systems in terms of internal versus external components. With this lens, we review approaches to training attention that might reduce the negative attentional bias, including behavioral interventions and real-time neurofeedback. Although extant findings are somewhat mixed, these approaches provide hope and clues for the next generation of treatments.

Neurofeedback helps to reveal a relationship between context reinstatement and memory retrieval.

Theories of mental context and memory posit that successful mental context reinstatement enables better retrieval of memories from the same context, at the expense of memories from other contexts. To test this hypothesis, we had participants study lists of words, interleaved with task-irrelevant images from one category (e.g., scenes). Following encoding, participants were cued to mentally reinstate the context associated with a particular list, by thinking about the images that had appeared between the words. We measured context reinstatement by applying multivariate pattern classifiers to fMRI, and related this to performance on a free recall test that followed immediately afterwards. To increase sensitivity, we used a closed-loop neurofeedback procedure, whereby higher classifier evidence for the cued category elicited increased visibility of the images from the studied context onscreen. Our goal was to create a positive feedback loop that amplified small fluctuations in mental context reinstatement, making it easier to experimentally detect a relationship between context reinstatement and recall. As predicted, we found that greater amounts of classifier evidence were associated with better recall of words from the reinstated context, and worse recall of words from a different context. In a second experiment, we assessed the role of neurofeedback in identifying this brain-behavior relationship by presenting context images again and manipulating whether their visibility depended on classifier evidence. When neurofeedback was removed, the relationship between classifier evidence and memory retrieval disappeared. Together, these findings demonstrate a clear effect of context reinstatement on memory recall and suggest that neurofeedback can be a useful tool for characterizing brain-behavior relationships.

Closed-loop training of attention with real-time brain imaging.

Lapses of attention can have negative consequences, including accidents and lost productivity. Here we used closed-loop neurofeedback to improve sustained attention abilities and reduce the frequency of lapses. During a sustained attention task, the focus of attention was monitored in real time with multivariate pattern analysis of whole-brain neuroimaging data. When indicators of an attentional lapse were detected in the brain, we gave human participants feedback by making the task more difficult. Behavioral performance improved after one training session, relative to control participants who received feedback from other participants' brains. This improvement was largest when feedback carried information from a frontoparietal attention network. A neural consequence of training was that the basal ganglia and ventral temporal cortex came to represent attentional states more distinctively. These findings suggest that attentional failures do not reflect an upper limit on cognitive potential and that attention can be trained with appropriate feedback about neural signals.