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

Mennen, Anne C.; Turk‐Browne, Nicholas B.; Wallace, Grant; Seok, Darsol; Jaganjac, Adna; Stock, Janet; deBettencourt, Megan T.; Cohen, Jonathan D.; Norman, Kenneth A.; Sheline, Yvette I.

doi:10.1016/j.bpsc.2020.10.006

Cited by 15 publications

(18 citation statements)

References 40 publications

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“…Such technologies open up multiple opportunities to conduct complex analyses in real time. This was notably shown in a recent proof-of-concept study (Mennen et al, 2021).…”

Section: F Cloud Computing and Parallel Processingmentioning

confidence: 59%

“…The real-time processing of standard fMRI neurofeedback experiments can be easily achieved on most desktop computers. However, the speed of modern internet connections makes it possible to transfer fMRI data to a remote computer (i.e., in the cloud) in order to conduct processing over there in real time (Cohen et al, 2017;Mennen et al, 2021). This allows one to conduct more intensive analyses in real time while using relatively simple desktop computers onsite.…”

Section: F Cloud Computing and Parallel Processingmentioning

confidence: 99%

“…Based on two recent systematic reviews of the field (Tursic et al, 2020;Pindi et al, 2021), we sub-selected studies targeting a mental health disorder where: (1) patients were blinded to experimental goals and (2) a control group or control condition was included. Our approach is not meant to diminish the usefulness of proof-of-principle studies that may not fulfil these criteria (Paret, Kluetsch, et al, 2016;Orlov et al, 2018;Chiba et al, 2019;Zaehringer et al, 2019;Takamura et al, 2020;Mennen et al, 2021). Such studies are crucial in order to show target engagement before more extensive funding can be secured for larger clinical studies.…”

Section: Promising Approaches In the Treatment Of Mental Disordersmentioning

confidence: 99%

“…These results suggest the possibility of training sustained attention in healthy individuals and might present interesting clinical applications for attention deficit / hyperactivity disorder but also for major depressive disorder. For instance, a recent proof-of-concept study showed the potential of this approach to reduce the negative attentional bias in depressed patients (This study was not included in Table 1 and in the section on Major depressive disorders as we could not establish if the study was single-blind) (Mennen et al, 2021).…”

Section: Attention Deficit / Hyperactivity Disordermentioning

confidence: 99%

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Real-Time fMRI in the Treatment of Mental Health Disorders

Taschereau-Dumouchel¹,

Cushing²,

Lau³

2022

Preprint

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Multiple mental disorders have been associated with dysregulations of precise brain processes. However, few therapeutic approaches are currently available in order to correct such specific patterns of brain activity. Since the late 60s and early 70s, many have hoped that this feat could be achieved by closed-loop brain imaging approaches, such as neurofeedback, that aim at modulating brain activity directly. However, neurofeedback never acquired mainstream acceptance in mental health, in part due to methodological considerations. Here, we argue that, when contemporary methodological guidelines are followed, neurofeedback is one of the few intervention methods in psychology that can be assessed in double-blind placebo-controlled trials. Furthermore, using new advances in machine learning and statistics, it is now possible to target very precise patterns of brain activity for therapeutic purposes. We review the recent literature in functional magnetic resonance imaging (fMRI) neurofeedback and discuss current and future applications to mental health.

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“…Such technologies open up multiple opportunities to conduct complex analyses in real time. This was notably shown in a recent proof-of-concept study (Mennen et al, 2021).…”

Section: F Cloud Computing and Parallel Processingmentioning

confidence: 59%

Section: F Cloud Computing and Parallel Processingmentioning

confidence: 99%

Section: Promising Approaches In the Treatment Of Mental Disordersmentioning

confidence: 99%

Section: Attention Deficit / Hyperactivity Disordermentioning

confidence: 99%

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Real-Time fMRI in the Treatment of Mental Health Disorders

Taschereau-Dumouchel¹,

Cushing²,

Lau³

2022

Preprint

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“…Thus far, two studies have been completed using previous versions of the RT-Cloud framework. The first study, referred to here as RT-Attention, was a clinical study conducted at Penn Medicine; the focus of this study was to train participants with major depressive disorder (MDD) to disengage their attention from negatively-valenced stimuli (Mennen et al, 2021). The second study, referred to here as GreenEyes, was conducted on healthy participants at Princeton University; this study aimed to bias participants toward a particular interpretation of an ambiguous story (Mennen et al, 2022).…”

Section: Real-world Validation Of the Frameworkmentioning

confidence: 99%

RT-Cloud: A Cloud-based Software Framework to Simplify and Standardize Real-Time fMRI

Wallace¹,

Polcyn²,

Brooks

et al. 2022

Preprint

Self Cite

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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.

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Deep learning and multimodal feature fusion for the aided diagnosis of Alzheimer's disease

Jia

Lao

2022

Neural Comput & Applic

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

Cited by 15 publications

References 40 publications

Real-Time fMRI in the Treatment of Mental Health Disorders

Real-Time fMRI in the Treatment of Mental Health Disorders

RT-Cloud: A Cloud-based Software Framework to Simplify and Standardize Real-Time fMRI

Deep learning and multimodal feature fusion for the aided diagnosis of Alzheimer's disease

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