Posts Tagged Neurofeedback

[WEB SITE] What is neurohacking and can it actually rewire your brain?

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What is neurohacking and can it actually rewire your brain?

Although at one point, “hack” referred to a creative solution to a tech problem, the term can apply to pretty much anything now. There are kitchen hacks, productivity hacks, personal finance hacks. Brain hacks, or neurohacks, are among the buzziest, though, thanks largely to the Silicon Valley techies who often swear by them as a way to boost their cognitive function, focus, and creativity. Mic asked a neuroscientist to explain neurohacking, which neurohacking methods are especially promising, which are mostly hype, and how to make neurohacking work for you.

First things first: Neurohacking, is a broad umbrella term that encompasses anything that involves “manipulating brain function or structure to improve one’s experience of the world,” says neuroscientist Don Vaughn of Santa Clara University and the University of California, Los Angeles. Like the other myriad forms of hacking, neurohacking uses an engineering approach, treating the brain as a piece of hardware that can be systematically modified and upgraded.

Neurohacking techniques can fall under a number of categories — here are a few of the most relevant ones, as well as the thinking behind them.

Brain stimulation

This involves applying an electric or magnetic field to certain regions of the brain in non-neurotypical people to make their activity more closely resemble that seen in a neurotypical brain. In 2008, the Food and Drug Administration approved transcranial magnetic stimulation (TMS) — a noninvasive form of brain stimulation which delivers magnetic pulses to the brain in a noninvasive manner — for major depression. Since then, the FDA has also approved TMS for pain associated with migraines with auras, as well as obsessive-compulsive disorder. Established brain stimulation techniques (such as TMS or electroconvulsive therapy) performed by an expert provider, such as a psychiatrist or neuroscientist, are generally safe, Vaughn says.

Neurofeedback

This one involves using a device that measures brain activity, such as an electroencephalogram (EEG) or a functional magnetic resonance imaging (fMRI) machine. People with neuropsychological disorders receive feedback on their own brain activity — often in the form of images or sound — and focus on trying to make it more closely resemble the brain activity in a healthy person, Vaughn says. This could happen through changing their thought patterns, Vaughn says. Another possibility is that the feedback itself, or the person’s thoughts about the feedback, may somehow lead to a change in their brain’s wiring.

Reducing cognitive load

This means minimizing how much apps, devices, and other tech compete for your attention. Doing so can sharpen and sustain your focus, or what Vaughn refers to as your attention quotient (AQ). To boost his AQ, Vaughn listens to brown noise, which he likens to “white noise, but deeper.” (Think the low rush of a waterfall versus pure static.) He also chews gum, which he says provides an outlet for his restless “monkey mind” while still allowing him to focus on the task at hand.

Reducing cognitive load can also deepen your connection with others. Vaughn uses Voicea, an app based on an AI assistant that takes and store notes of meetings, whether over the phone or in-person, allowing him to focus solely on the conversation, not on recording it. “If we can quell those disruptions that occur because of the way work is done these days, it will allow us to focus and be more empathic with each other,” he says.

Monitoring sleep

Tracking your sleep patterns and adjusting them accordingly. Every night, you go through around five or so stages of sleep, each one deeper than the last. “People are less groggy and make fewer errors when they wake up in a lighter stage of sleep,” Vaughn says. He uses Sleep Cycle, an app that tracks your sleep patterns based on your movements in bed to rouse you during your lightest sleep stage.

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Microdosing

Microdosing is the routinely consumption of teensy doses of psychedelics like LSD, ecstasy, or magic mushrooms. Many who practice microdosing follow the regimen recommended by James Fadiman, psychologist and author of The Psychedelic Explorer’s Guide: Safe, Therapeutic, and Sacred Journeys: a twentieth to a tenth of a regular dose, once every three days for about a month. While a regular dose may make you trip, a microdose has subtler effects, with some users reporting, for instance, enhanced energy and focus, per The Cut.

Nootropics

These are OTC supplements or drugs taken to enhance cognitive function. They range from everyday caffeine and vitamin B12 (B12 deficiency has been associated with cognitive decline) to prescription drugs like Ritalin and Adderall, used to treat ADHD and narcolepsy, as well as Provigil (modafinil), used to treat extreme drowsiness resulting from narcolepsy and other sleep disorder. (All three of these drugs promote wakefulness.) The science behind nootropic supplements in particular remains rather murky, though.

Does neurohacking work, though?

Vaughn finds microdosing, neurostimulation, and neurofeedback especially promising for neuropsychological disorders. Although studies suggest that larger doses of psychedelics could help with disorders such as PTSD and treatment-resistant major depression, there are few studies on microdosing psychedelics. “The little science that has been done…is mixed—perhaps slightly positive,” Vaughn says. “Microdosing is promising mainly because of anecdotal evidence.” Meanwhile, neurostimulation can be used noninvasively in some cases, and TMS has already received FDA approval for a handful of conditions. Neurofeedback is not only non-invasive, but offers immediate feedback, and studies suggest it could be effective for PTSD and addiction.

But it’s important to note that just because these methods could positively alter brain function in people with neuropsychological disorders, that “doesn’t mean it’s going to take a normal system and make it superhuman,” Vaughn says. “I think there are lots of small hacks to be done that could add up to something big,” rather than huge hacks that can vastly upgrade cognitive function, a la Limitless. Thanks to millions of years of evolution, the human brain is already pretty damn optimized. “I just don’t know how much more we can tweak it to make it better,” Vaughn says.

As far as enhancements for neurotypical brains, he says that “you’ll probably see a much greater improvement” from removing distractions in your environment to reduce cognitive load than say, increasing your B12 intake — which brings us to an important disclaimer about nootropic supplements in particular. As with all supplements, they aren’t FDA-regulated, meaning that companies that sell them don’t need to provide evidence that they’re safe or effective. Vaughn recommends trying nootropics that research has shown to be safe and effective, like B12 or caffeine.

How can I start neurohacking?

As tempting as it is, adopting every neurohack under the sun is “not the answer,” Vaughn says. Remember, everyone is different. While your best friend may gush about how much her mood has improved since she began microdosing shrooms, your brain might not respond to microdosing—or maybe taking psychedelics just doesn’t align with your ethics.

Start by exploring different neurohacks, and of course, be skeptical of any product that makes outrageous claims. Since neurofeedback isn’t a common medical treatment, talk to your doctor about enrolling in academic studies on neurofeedback, or companies that offer it if you’re interested, Vaughn says. You should also talk to your doctor if you want to try brain stimulation. A doctor can prescribe you Adderall, Ritalin, or Provigil but only for their indicated medical uses, not for cognitive enhancement.

Ultimately, neurohacks are tools, Vaughn says. “You have to find the one that works for you.” If anything, taking this DIY approach to improving your brain function will leave you feeling empowered, a benefit that probably rivals anything a supplement or sleep tracking app could offer.

 

via What is neurohacking and can it actually rewire your brain?

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[Abstract + References] Multimodal Head-Mounted Virtual-Reality Brain-Computer Interface for Stroke Rehabilitation – Conference paper

Abstract

Rehabilitation after stroke requires the exploitation of active movement by the patient in order to efficiently re-train the affected side. Individuals with severe stroke cannot benefit from many training solutions since they have paresis and/or spasticity, limiting volitional movement. Nonetheless, research has shown that individuals with severe stroke may have modest benefits from action observation, virtual reality, and neurofeedback from brain-computer interfaces (BCIs). In this study, we combined the principles of action observation in VR together with BCI neurofeedback for stroke rehabilitation to try to elicit optimal rehabilitation gains. Here, we illustrate the development of the REINVENT platform, which takes post-stroke brain signals indicating an attempt to move and drives a virtual avatar arm, providing patient-driven action observation in head-mounted VR. We also present a longitudinal case study with a single individual to demonstrate the feasibility and potentially efficacy of the REINVENT system.

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via Multimodal Head-Mounted Virtual-Reality Brain-Computer Interface for Stroke Rehabilitation | SpringerLink

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[ARTICLE] Effects of a Brain-Computer Interface With Virtual Reality (VR) Neurofeedback: A Pilot Study in Chronic Stroke Patients – Full Text

Rehabilitation for stroke patients with severe motor impairments (e.g., inability to perform wrist or finger extension on the affected side) is burdensome and difficult because most current rehabilitation options require some volitional movement to retrain the affected side. However, although these patients participate in therapy requiring volitional movement, previous research has shown that they may receive modest benefits from action observation, virtual reality (VR), and brain-computer interfaces (BCIs). These approaches have shown some success in strengthening key motor pathways thought to support motor recovery after stroke, in the absence of volitional movement. The purpose of this study was to combine the principles of VR and BCI in a platform called REINVENT and assess its effects on four chronic stroke patients across different levels of motor impairment. REINVENT acquires post-stroke EEG signals that indicate an attempt to move and drives the movement of a virtual avatar arm, allowing patient-driven action observation neurofeedback in VR. In addition, synchronous electromyography (EMG) data were also captured to monitor overt muscle activity. Here we tested four chronic stroke survivors and show that this EEG-based BCI can be safely used over repeated sessions by stroke survivors across a wide range of motor disabilities. Finally, individual results suggest that patients with more severe motor impairments may benefit the most from EEG-based neurofeedback, while patients with more mild impairments may benefit more from EMG-based feedback, harnessing existing sensorimotor pathways. We note that although this work is promising, due to the small sample size, these results are preliminary. Future research is needed to confirm these findings in a larger and more diverse population.

Introduction

Stroke is a leading cause of adult long-term disability worldwide (Mozaffarian et al., 2015), and an increasing number of stroke survivors suffer from severe cognitive and motor impairments each year. This results in a loss of independence in their daily life, such as decreased ability to perform self-care tasks and decreased participation in social activities (Miller et al., 2010). Rehabilitation following stroke focuses on maximizing restoration of lost motor and cognitive functions and on relearning skills to better perform activities of daily living (ADLs). There is increasing evidence that the brain remains plastic at later stages after stroke, suggesting additional recovery remains possible (Page et al., 2004Butler and Page, 2006). To maximize brain plasticity, several rehabilitation strategies have been exploited, including the use of intensive rehabilitation (Wittenberg et al., 2016), repetitive motor training (Thomas et al., 2017), mirror therapy (Pérez-Cruzado et al., 2017), motor-imagery (Kho et al., 2014), and action observation (Celnik et al., 2008), amongst others.

Recently, growing evidence of the positive impact of virtual reality (VR) techniques on recovery following stroke has accumulated (Bermúdez i Badia et al., 2016). When combined with conventional therapy, VR is able to effectively incorporate rehabilitation strategies such as intensity, frequency, and duration of therapy in a novel and low-cost approach in the stroke population (Laver et al., 2017). However, patients with low levels of motor control cannot benefit from current VR tools due to their low volitional motor control, range of motion, pain, and fatigue. Rehabilitation for these individuals is challenging because most current training options require some volitional movement to train the affected side, however, research has shown that individuals with severe stroke may receive modest benefits from action observation and brain-computer interfaces (BCIs) (Silvoni et al., 2011).

Merging BCIs with VR allows for a wide range of experiences in which patients can feel immersed in various aspects of their environment. This allows patients to control their experiences in VR using only brain activity, either directly (e.g., movement in VR through explicit control) or indirectly (e.g., modulating task difficulty level based on workload as implicit control) (Vourvopoulos et al., 2016Friedman, 2017). This direct brain-to-VR communication can induce a sensorimotor contingency between the patient’s internal intentions and the environment’s responsive actions, increasing the patient’s sense of embodiment of their virtual avatar (Slater, 2009Ramos-Murguialday et al., 2013).

After a stroke resulting in severe motor impairments (e.g., inability to perform wrist or finger extension on the affected side), research shows that action observation combined with physical training enhances the effects of motor training (Celnik et al., 2008), eliciting motor-related brain activity in the lesioned hemisphere, leading to modest motor improvements (Ertelt et al., 2007Garrison et al., 2013). Moreover, action observation in a head-mounted VR increases motor activity in both healthy and the post-stroke brains (Ballester et al., 2015Vourvopoulos and Bermúdez i Badia, 2016a).

In addition, neurofeedback through BCIs has been proposed for individuals with severe stroke because BCIs do not require active motor control. Research on BCIs for rehabilitation has shown that motor-related brain signals are reinforced by rewarding feedback so they can be used to strengthen key motor pathways that are thought to support motor recovery after stroke (Wolpaw, 2012). Such feedback has previously shown modest success in motor rehabilitation for severe stroke patients (Soekadar et al., 2015).

The most common brain signal acquisition technology used with BCIs in stroke patients is non-invasive electroencephalography (EEG) (Wolpaw, 2012), which provide a cost-effective BCI platform (Vourvopoulos and Bermúdez i Badia, 2016b). In BCI paradigms for motor rehabilitation, EEG signals related to motor planning and execution are utilized. During a motor attempt, the temporal pattern of the Alpha rhythm (8–12 Hz) desynchronizes. The Alpha rhythm is also termed Rolandic mu or the sensorimotor rhythm (SMR) when it is localized over the sensorimotor cortices of the brain. Mu rhythms (8–12 Hz) are considered indirect indications of the action observation network (Kropotov, 2016) and reflect general sensorimotor activity. Mu rhythms are often detected with changes in the Beta rhythm (12–30 Hz) in the form of event-related desynchronization (ERD), in which a motor action is executed (Pfurtscheller and Lopes da Silva, 1999). These EEG rhythms, or motor-related EEG signatures, are primarily detected during task-based EEG (i.e., when the patient is actively moving or imagining movement) and used for neurofeedback.

Further, neurofeedback-induced changes in brain activity have also been linked to changes in brain activity at rest. That is, after training one’s brain activity using neurofeedback, the intrinsic, resting brain activity (i.e., EEG activity in the absence of a task) may also show changes. This resting brain activity can be used to assess more generalized brain changes, and baseline resting-state signatures may be used to predict recovery (Wu et al., 2015) or response to treatments (Zhou et al., 2018). When combined with neural injury information, resting EEG parameters can also help predict the efficacy of stroke therapy.

In this study, our goal was to combine the principles of virtual reality and BCIs to elicit optimal rehabilitation gains for stroke survivors. We hypothesized that merging BCIs with VR should induce illusions of movement and a strong feeling of embodiment within a virtual body via the action observation network, activating brain areas that overlap with those controlling actual movement, which is important for mobilizing neuroplastic changes (Dobkin, 2007). Using a VR-based BCI, those with severe stroke impairments can trigger voluntary movements of the virtual arm in a closed neurofeedback loop. This helps to increase the illusion of one’s own movements through the coordination between one’s intention and the observed first-person virtual action. Therefore, we developed a training platform called REINVENT, which uses post-stroke brain signals that indicate an attempt to move and then drives the movement of a virtual avatar arm, providing patient-driven action observation in head-mounted VR (Spicer et al., 2017). Our previous work using REINVENT with healthy individuals indeed showed that the combination of VR integrated into a BCI encouraged greater embodiment, and greater embodiment was related to greater neurofeedback performance (Anglin et al., 2019).

For this study, we recruited four chronic stroke survivors to undergo a longitudinal BCI-VR intervention using REINVENT to provide EEG-based neurofeedback with simultaneous EMG acquisition. We assessed intervention results using clinical measures, Transcranial Magnetic Stimulation (TMS) and Magnetic Resonance Imaging (MRI) and compared these measures before and after the intervention. The purpose of this study was twofold. First, we sought to determine whether REINVENT is feasible for stroke patients to use across repeated sessions and second, whether REINVENT might be able to strengthen motor-related brain signals in individuals with differing levels of motor impairment after stroke.[…]

 

Continue —>  Frontiers | Effects of a Brain-Computer Interface With Virtual Reality (VR) Neurofeedback: A Pilot Study in Chronic Stroke Patients | Frontiers in Human Neuroscience

Figure 1. System architecture of a closed neurofeedback loop. From left, (1) the evoked physiological responses are captured at the interfacing layer through the data acquisition clients, (2) sent to the processing layer where the signals are filtered and logged, and then, (3) the extracted features (e.g., EEG bands) are sent to the interaction layer where VR training occurs. Written permission to use this photo was obtained from the individual.

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[Abstract] Brain-machine interface of upper limb recovery in stroke patients rehabilitation: A systematic review

Abstract

BACKGROUND:

Technologies such as brain-computer interfaces are able to guide mental practice, in particular motor imagery performance, to promote recovery in stroke patients, as a combined approach to conventional therapy.

OBJECTIVE:

The aim of this systematic review was to provide a status report regarding advances in brain-computer interface, focusing in particular in upper limb motor recovery.

METHODS:

The databases PubMed, Scopus, and PEDro were systematically searched for articles published between January 2010 and December 2017. The selected studies were randomized controlled trials involving brain-computer interface interventions in stroke patients, with upper limb assessment as primary outcome measures. Reviewers independently extracted data and assessed the methodological quality of the trials, using the PEDro methodologic rating scale.

RESULTS:

From 309 titles, we included nine studies with high quality (PEDro ≥ 6). We found that the most common interface used was non-invasive electroencephalography, and the main neurofeedback, in stroke rehabilitation, was usually visual abstract or a combination with the control of an orthosis/robotic limb. Moreover, the Fugl-Meyer Assessment Scale was a major outcome measure in eight out of nine studies. In addition, the benefits of functional electric stimulation associated to an interface were found in three studies.

CONCLUSIONS:

Neurofeedback training with brain-computer interface systems seem to promote clinical and neurophysiologic changes in stroke patients, in particular those with long-term efficacy.

via: https://www.ncbi.nlm.nih.gov/pubmed/30609208

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[Abstract] Brain‐machine interface of upper limb recovery in stroke patients rehabilitation: A systematic review

Abstract

Background

Technologies such as brain‐computer interfaces are able to guide mental practice, in particular motor imagery performance, to promote recovery in stroke patients, as a combined approach to conventional therapy.

Objective

The aim of this systematic review was to provide a status report regarding advances in brain‐computer interface, focusing in particular in upper limb motor recovery.

Methods

The databases PubMed, Scopus, and PEDro were systematically searched for articles published between January 2010 and December 2017. The selected studies were randomized controlled trials involving brain‐computer interface interventions in stroke patients, with upper limb assessment as primary outcome measures. Reviewers independently extracted data and assessed the methodological quality of the trials, using the PEDro methodologic rating scale.

Results

From 309 titles, we included nine studies with high quality (PEDro ≥ 6). We found that the most common interface used was non‐invasive electroencephalography, and the main neurofeedback, in stroke rehabilitation, was usually visual abstract or a combination with the control of an orthosis/robotic limb. Moreover, the Fugl–Meyer Assessment Scale was a major outcome measure in eight out of nine studies. In addition, the benefits of functional electric stimulation associated to an interface were found in three studies.

Conclusions

Neurofeedback training with brain‐computer interface systems seem to promote clinical and neurophysiologic changes in stroke patients, in particular those with long‐term efficacy.

 

via Brain‐machine interface of upper limb recovery in stroke patients rehabilitation: A systematic review – Carvalho – – Physiotherapy Research International – Wiley Online Library

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[ARTICLE] Emotion Regulation Using Virtual Environments and Real-Time fMRI Neurofeedback – Full Text

Neurofeedback (NFB) enables the voluntary regulation of brain activity, with promising applications to enhance and recover emotion and cognitive processes, and their underlying neurobiology. It remains unclear whether NFB can be used to aid and sustain complex emotions, with ecological validity implications. We provide a technical proof of concept of a novel real-time functional magnetic resonance imaging (rtfMRI) NFB procedure. Using rtfMRI-NFB, we enabled participants to voluntarily enhance their own neural activity while they experienced complex emotions. The rtfMRI-NFB software (FRIEND Engine) was adapted to provide a virtual environment as brain computer interface (BCI) and musical excerpts to induce two emotions (tenderness and anguish), aided by participants’ preferred personalized strategies to maximize the intensity of these emotions. Eight participants from two experimental sites performed rtfMRI-NFB on two consecutive days in a counterbalanced design. On one day, rtfMRI-NFB was delivered to participants using a region of interest (ROI) method, while on the other day using a support vector machine (SVM) classifier. Our multimodal VR/NFB approach was technically feasible and robust as a method for real-time measurement of the neural correlates of complex emotional states and their voluntary modulation. Guided by the color changes of the virtual environment BCI during rtfMRI-NFB, participants successfully increased in real time, the activity of the septo-hypothalamic area and the amygdala during the ROI based rtfMRI-NFB, and successfully evoked distributed patterns of brain activity classified as tenderness and anguish during SVM-based rtfMRI-NFB. Offline fMRI analyses confirmed that during tenderness rtfMRI-NFB conditions, participants recruited the septo-hypothalamic area and other regions ascribed to social affiliative emotions (medial frontal / temporal pole and precuneus). During anguish rtfMRI-NFB conditions, participants recruited the amygdala and other dorsolateral prefrontal and additional regions associated with negative affect. These findings were robust and were demonstrable at the individual subject level, and were reflected in self-reported emotion intensity during rtfMRI-NFB, being observed with both ROI and SVM methods and across the two sites. Our multimodal VR/rtfMRI-NFB protocol provides an engaging tool for brain-based interventions to enhance emotional states in healthy subjects and may find applications in clinical conditions associated with anxiety, stress and impaired empathy among others.

Introduction

Neurofeedback (NFB) is a novel application of brain-computer interfaces that aids real-time voluntarily regulation of brain activity. Mounting evidence shows that NFB has promising effects to enhance behavior, cognitive and emotional processes in normative samples (1–5). NFB has also been preliminary used to restore aberrant neurobiology and symptoms in neurological conditions (e.g., stroke, traumatic brain injury) and in psychopathology (e.g., ADHD, autism, depression, addiction) (1–7). Real-time functional magnetic resonance imaging (rtfMRI) based NFB has the potential to provide insight in understanding the mechanisms of psychological states (8–10). These include affiliative emotions (11) underpinned by deep brain nuclei (12, 13) the activity of which is unlikely to be robustly measured via surface electroencephalography.

rtfMRI NFB tools can be used to study the causal mechanisms of complex emotions and to inform evidence-based personalized interventions to enhance and recover aberrant emotional states (and their neural substrates) in normative and clinical samples. One key practical human challenge of fMRI studies includes participants being distracted and experiencing difficulties to feel valid psychological states in the scanner environment, particularly when trying to sustain complex emotions.

Recent studies have combined immersive virtual environments with multiple sensory modalities to deliver psychological/cognitive interventions, and to enhance their effectiveness via engaging and motivating individuals to practice (14–16).

Only two proof of concept studies have combined rt-NFB with virtual environments as brain computer interfaces (BCI). An electroencephalography-based NFB study computed brain activity from about 500 participants collectively, during an interactive game of relaxation and concentration over one night (16), where individual’s level of brain activity could not be discerned. A separate rtfMRI-NFB paradigm used a virtual fire interface to up-regulate and down-regulate brain activity in eight healthy participants—but this was devoid of any emotional states and far from multimodal and immersive (17).

It remains untested whether rt-NFB platforms integrating multisensory virtual environments can successfully recruit complex emotions and sustain these emotions long and strong enough to probe their underlying neural correlates. Such a platform can advance NFB applications, via (i) increasing the ecological validity of rtfMRI-NFB experiments, and their relevance for the daily experiences of emotions outside of experimental settings, (ii) adapting NFB interfaces to the individual and target population so these are more relatable, engaging and effective in generating and sustaining complex emotions that maximize the success of rtfMRI-NFB interventions (18–20).

This study aims to demonstrate the feasibility of an engaging rtfMRI-NFB interface that can be individually tailored and, specifically, to provide a proof of concept for a rtfMRI-NFB integrating a virtual environment as a BCI and musical stimuli using both local (region of interest, ROI) and distributed (support vector machine, SVM) analyses. The FRIEND Engine Framework system (21) was enhanced and adapted for this aim. We recruited healthy young adults performing rtfMRI-NFB during complex emotion experiences, including tenderness—a positive affiliative emotion – and anguish—a self-reflective negative emotion (11, 13, 22–25).

We also aimed to validate the functional anatomy of these complex emotions during rtfMRI-NFB. After the real-time data was collected, we ran offline fMRI data analyses to verify the effects of the real-time neurofeedback task on brain activity using standard preprocessing and statistical analysis methods.

We hypothesized that participants would voluntary change the color of a virtual environment in the BCI during rtfMRI-NFB using the activity of the following regions: (i) for the tenderness condition, the septo-hypothalamic area (when using ROI-based rtfMRI-NFB method) and other brain areas ascribed to positive affiliative emotions i.e., medial orbitofrontal areas (when using SVM-based rtfMRI-NFB method) (11, 25–27); and (ii) for the anguish condition, the amygdala (during the ROI-based fMRI-NFB method) and also lateral prefrontal cortices implicated in negative affect (e.g., anguish, fear, anxiety, negative mood, stress, psychological pain), and in psychopathologies where negative affect is a feature [e.g., depression and generalized anxiety disorder (28–32)] (during SVM-based rtfMRI-NFB).

Materials and Methods

Participants

We used a single subject, repeated measures design with two identical assessments on two consecutive days, counterbalanced by rtfMRI-NFB method (i.e., ROI and SVM). We recruited eight psychiatrically and neurologically healthy postgraduate research students, free of psychoactive medication and with normal or corrected-to-normal vision. Four participants were recruited from the D’Or Institute for Research and Education (IDOR) in Rio de Janeiro, Brazil (approved by the Ethics and Scientific committees of the Copa D’Or Hospital, Rio de Janeiro, Brazil – No 922.218). To validate the protocol in a different scanner and institution, we also recruited four participants from the Monash Biomedical Imaging (MBI) at Monash University in Melbourne, Australia (MUHREC CF15/1756 – 2015000893). All volunteers provided written informed consent prior to study participation.

Design of the Neurofeedback BCI

Supplementary video 1 and Figure 1 show the BCI used for the rt-fMRI NFB. The BCI comprised a virtual environment as a medium to convey sensory feedback to participants in real time, in association with ongoing tenderness, anguish and neutral emotional states. The virtual environment was created by editing the Unity 3D asset Autumnal Nature Pack (Unity 3D, https://assetstore.unity.com/packages/3d/environments/autumnal-nature-pack-3649) and displayed a first-person navigation at walking speed through hills and cornfields, with a total duration of 10′8″ (Supplementary Video 1). The virtual environment was prepared to alternate between different trial types: neutral (30″), tenderness (46″) and anguish (46″).

The trial types were displayed via changes in the base color hues of the virtual environment and via specific music excerpts. Music excerpts were fixed for each trial type, and not influenced by current neural/psychological states (no music for Neutral, mild, gentle music for Tenderness and eerie, distorted music for Anguish). Music excerpts were selected from 20 audio tracks, all normalized using the root mean square feature of Audacity software (Audacity, http://www.audacityteam.org). The audio tracks were previously rated to have comparable volume, pace, and rhythm. For the rtfMRI-NFB task runs, four excerpts for tenderness and four excerpts for anguish were played.

Neutral trials were characterized by a normal colored virtual landscape displayed in the BCI with no background music. Tenderness trials were characterized by a change in the color of the virtual landscape to orange and were accompanied by tenderness music excerpts. Anguish trials commenced when the color of the environment turned to purple hues and were accompanied by anguish music excerpts.

Neurofeedback Task

Task Practice Outside the MRI

For training purposes, we recorded a video showing a sample of the virtual environment. The video lasted as long as one run of the rtfMRI-NFB task (10′ 8″) and was used by participants to practice tenderness, anguish and neutral states before the MRI. With this practice, participants could learn which music tracks and VR color changes in the BCI corresponded to tenderness, anguish and neutral trials.

Neurofeedback Interface

As shown in Figure 1, instead of a classic thermometer, the color of the virtual environment was used as BCI changed in real time with increased engagement of the neural activity/pattern corresponding to distinct target emotional states—orange for tenderness trials, purple for anguish trials and natural light tones for neutral trials. Participants were instructed to experience tenderness or anguish as intensely as possible in the respective trials and to increase the intensity of their emotion to turn in real time, the color of the virtual environment BCI to as orange as possible during tenderness trials, and as purple as possible during anguish trials, which increased in turn the corresponding neural activity/pattern.

FIGURE 1

Figure 1. Color hue modulation of the virtual environment during rtfMRI-NFB. The color hue changes from baseline neutral trials to a more intense orange and purple as participants increasingly engage target brain regions for tenderness and anguish trials.

[…]

via Frontiers | Emotion Regulation Using Virtual Environments and Real-Time fMRI Neurofeedback | Neurology

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[Abstract] Mental practice for upper limb rehabilitation after stroke: a systematic review and meta-analysis

Abstract

Mental practice (MP) is usually provided in combination with other therapies, and new developments for neurofeedback to support MP have been made recently. The objectives of this study were to evaluate the effectiveness of MP and to investigate the intervention characteristics including neurofeedback that may affect treatment outcome. The Cochrane Central Register of Controlled Trials, PubMed, Embase, KoreaMed, Scopus, Web of Science, PEDro, and CIRRIE were searched from inception to March 2017 for randomized controlled trials to assess the effect of MP for upper limb rehabilitation after stroke. Fugl-Meyer Assessment (FMA) was used as the outcome measure for meta-analysis. Twenty-five trials met the inclusion criteria, and 15 trials were eligible for meta-analysis. Among the trials selected for meta-analysis, MP was added to conventional therapy in eight trials or to modified constraint-induced movement therapy in one trial. The other trials provided neurofeedback to support MP: MP-guided neuromuscular electrical stimulation (NMES) in four trials and MP-guided robot-assisted therapy (RAT) in two trials. MP added to conventional therapy resulted in significantly higher FMA gain than conventional therapy alone. MP-guided NMES showed superior result than conventional NMES as well. However, the FMA gain of MP-guided RAT was not significantly higher than RAT alone. We suggest that MP is an effective complementary therapy either given with neurofeedback or not. Neurofeedback applied to MP showed different results depending on the therapy provided. This study has limitations because of heterogeneity and inadequate quality of trials. Further research is requested.

 

via Mental practice for upper limb rehabilitation after stroke:… : International Journal of Rehabilitation Research

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[Study] Transcranial Electrical Stimulation for mTBI (TES for mTBI)

Recruitment Status  : Recruiting

Study Description

Brief Summary:

Mild traumatic brain injury (mTBI) is a leading cause of sustained physical, cognitive, emotional, and behavioral deficits in OEF/OIF/OND Veterans and the general public. However, the underlying pathophysiology is not completely understood, and there are few effective treatments for post-concussive symptoms (PCS). In addition, there are substantial overlaps between PCS and post-traumatic stress disorder (PTSD) symptoms in mTBI. IASIS is among a class of passive neurofeedback treatments that combine low-intensity pulses for transcranial electrical stimulation (LIP-tES) with electroencephalography (EEG) monitoring. LIP-tES techniques have shown promising results in alleviating PCS individuals with TBI. However, the neural mechanisms underlying the effects of LIP-tES treatment in TBI are unknown, owing to the dearth of neuroimaging investigations of this therapeutic intervention. Conventional neuroimaging techniques such as MRI and CT have limited sensitivity in detecting physiological abnormalities caused by mTBI, or in assessing the efficacy of mTBI treatments. In acute and chronic phases, CT and MRI are typically negative even in mTBI patients with persistent PCS. In contrast, evidence is mounting in support of resting-state magnetoencephalography (rs-MEG) slow-wave source imaging (delta-band, 1-4 Hz) as a marker for neuronal abnormalities in mTBI. The primary goal of the present application is to use rs-MEG to identify the neural underpinnings of behavioral changes associated with IASIS treatment in Veterans with mTBI. Using a double-blind placebo controlled design, the investigators will study changes in abnormal MEG slow-waves before and after IASIS treatment (relative to a ‘sham’ treatment group) in Veterans with mTBI. In addition, the investigators will examine treatment-related changes in PCS, PTSD symptoms, neuropsychological test performances, and their association with changes in MEG slow-waves. The investigators for the first time will address a fundamental question about the mechanism of slow-waves in brain injury, namely whether slow-wave generation in wakefulness is merely a negative consequence of neuronal injury or if it is a signature of ongoing neuronal rearrangement and healing that occurs at the site of the injury. Specific Aim 1 will detect the loci of injury in Veterans with mTBI and assess the mechanisms underlying functional neuroimaging changes related to IASIS treatment using rs-MEG slow-wave source imaging. The investigators hypothesize that MEG slow-wave source imaging will show significantly higher sensitivity than conventional MRI in identifying the loci of injury on a single-subject basis. The investigators also hypothesize that in wakefulness, slow-wave generation is a signature of ongoing neural rearrangement / healing, rather than a negative consequence of neuronal injury. Furthermore, the investigators hypothesize IASIS will ultimately reduce abnormal MEG slow-wave generation in mTBI by the end of the treatment course, owing to the accomplishment of neural rearrangement / healing. Specific Aim 2 will examine treatment-related changes in PCS and PTSD symptoms in Veterans with mTBI. The investigators hypothesize that compared with the sham group, mTBI Veterans in the IASIS treatment group will show significantly greater decreases in PCS and PTSD symptoms between baseline and post-treatment assessments. Specific Aim 3 will study the relationship among IASIS treatment-related changes in rs-MEG slow-wave imaging, PCS, and neuropsychological measures in Veterans with mTBI. The investigators hypothesize that Reduced MEG slow-wave generation will correlate with reduced total PCS score, individual PCS scores (e.g., sleep disturbance, post-traumatic headache, photophobia, and memory problem symptoms), and improved neuropsychological exam scores between post-IASIS and baseline exams. The success of the proposed research will for the first time confirm that facilitation of slow-wave generation in wakefulness leads to significant therapeutic benefits in mTBI, including an ultimate reduction of abnormal slow-waves accompanied by an improvement in PCS and cognitive functioning.

MORE —>  Transcranial Electrical Stimulation for mTBI – No Study Results Posted – ClinicalTrials.gov

 

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[Systematic review] The potential of real-time fMRI neurofeedback for stroke rehabilitation – Full Text

Abstract

Real-time functional magnetic resonance imaging (rt-fMRI) neurofeedback aids the modulation of neural functions by training self-regulation of brain activity through operant conditioning. This technique has been applied to treat several neurodevelopmental and neuropsychiatric disorders, but its effectiveness for stroke rehabilitation has not been examined yet. Here, we systematically review the effectiveness of rt-fMRI neurofeedback training in modulating motor and cognitive processes that are often impaired after stroke. Based on predefined search criteria, we selected and examined 33 rt-fMRI neurofeedback studies, including 651 healthy individuals and 15 stroke patients in total. The results of our systematic review suggest that rt-fMRI neurofeedback training can lead to a learned modulation of brain signals, with associated changes at both the neural and the behavioural level. However, more research is needed to establish how its use can be optimized in the context of stroke rehabilitation.

1. Introduction

The number of stroke survivors is continuously increasing with the ageing of the population: about 15 million people worldwide suffer from stroke every year, of whom 5 million die, whereas another 5 million become chronically disabled (WHO, 2012). Behavioural deficits in cognitive and motor domains are highly prevalent and persistent in stroke survivors (Bickerton et al., 2014; Demeyere, Riddoch, Slavkova, Bickerton, & Humphreys, 2015; Demeyere et al., 2016; Jaillard, Naegele, Trabucco-Miguel, LeBas, & Hommel, 2009; Planton et al., 2012; Verstraeten, Mark, & Sitskoorn, 2016). Neurophysiological and neuroimaging studies suggested that stroke causes network-wide changes across structurally intact regions, directly or indirectly connected to the site of infarction (Carrera & Tononi, 2014; Carter et al., 2010; Gillebert & Mantini, 2013; Grefkes et al., 2008; Ward & Cohen, 2004). Disruptions in even one of the many networks or brain regions implicated in the different aspects of motor function and cognition can have a major impact on quality of life (Achten, Visser-Meily, Post, & Schepers, 2012; Hochstenbach, Mulder, Limbeek, Donders, & Schoonderwaldt, 1998). Accordingly, both local tissue damage and secondary changes in brain function should be considered when developing rehabilitation strategies to improve the recovery rate and generally increase the quality of life in stroke survivors (Chechlacz, Mantini, Gillebert, & Humphreys, 2015; Chechlacz et al., 2013; Corbetta et al., 2015; Gillebert & Mantini, 2013). In this regard, the use of neurofeedback may be a promising approach.

1.1. Neurofeedback

Neurofeedback works as a closed loop system that provides real-time information regarding the participant’s own brain activity and/or connectivity, which can be used to develop self-learning strategies to modulate these brain signals (Weiskopf, Mathiak, et al., 2004). It follows the principle of operant conditioning, a method of learning that occurs through reinforcing specific behaviour with rewards and punishments (Skinner, 1938). If the participant learns to control activity of the brain areas targeted through neurofeedback, this may ultimately lead to a measurable behavioural change that is related to the function of those areas (DeCharms et al., 2005; Haller, Birbaumer, & Veit, 2010; Hartwell et al., 2016).

The origins of neurofeedback are rooted in electroencephalography (EEG), which measures dynamic changes of electrical potentials over the participant’s scalp (Nowlis & Kamiya, 1970). This technique is portable and inexpensive, and provides estimates of brain activity at high temporal resolution. EEG neurofeedback has been widely used over the years to induce long-lasting behavioural changes, both in healthy volunteers and in patients (Gruzelier, 2014; Nelson, 2007). However, because of the low spatial resolution associated with this technique, it is very challenging to selectively target brain areas of interest. As such, the effects of EEG neurofeedback are often not specific (Rogala et al., 2016; Scharnowski & Weiskopf, 2015). Other neuroimaging techniques used for neurofeedback include magnetoencephalography (MEG) (Buch et al., 2012; Okazaki et al., 2015) and functional near-infrared spectroscopy (fNIRS) (Kober et al., 2014; Mihara et al., 2013). However, as also for EEG, their spatial resolution is relatively limited and they do not permit to target precise brain regions.

The field of neurofeedback has rapidly developed and delved into new avenues by the introduction of real-time functional magnetic resonance imaging (rt-fMRI) technology (Cox, Jesmanowicz, & Hyde, 1995). Accordingly, in the past years there has been a steady increase of studies focussing on rt-fMRI neurofeedback applications to induce behavioural changes (Sulzer et al., 2013). Rt-fMRI neurofeedback uses the blood-oxygenation level-dependent (BOLD) signal to present contingent feedback to the participant and to enable modulation of brain activity (Fig. 1). Various acquisition parameters are available, and chosen based on a trade-off between spatial and temporal resolution, and signal-to-noise ratio (Weiskopf, Scharnowski, et al., 2004). The analysis is performed almost immediately or with a delay of a few seconds depending on the available computational resources. With a much higher spatial resolution than EEG, fMRI allows for a refined delineation of both cortical and subcortical target regions. These properties can be valuable for neurofeedback applications (Stoeckel et al., 2014). Recent studies suggest that rt-fMRI is a mature technology to use in the context of neurofeedback training (for a review, see e.g., Ruiz, Buyukturkoglu, Rana, Birbaumer, & Sitaram, 2014; Weiskopf, 2012). As a result, doors are being opened to the application of rt-fMRI neurofeedback in ameliorating disrupted brain functions in stroke survivors.[…]

Fig. 1

Fig. 1. Real-time fMRI neurofeedback is a closed-loop system that can be used to voluntarily modulate brain-activity through the principle of operant conditioning. (A) The participants use self-generated or prior instructed strategies to attempt to change their brain activity. (B) fMRI data are acquired and (C) processed in real-time. Computer programs select the relevant signals and (D) return these to the participants after varied degrees of pre-processing to allow them to adjust their control strategies.

Continue —>  The potential of real-time fMRI neurofeedback for stroke rehabilitation: A systematic review – ScienceDirect

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[Abstract] Preliminary results of testing the recoveriX system on stroke patients 

Abstract

Motor imagery based brain-computer interfaces (BCI) extract the movement intentions of subjects in real-time and can be used to control a cursor or medical devices. In the last years, the control of functional electrical stimulation (FES) devices drew researchers’ attention for the post-stroke rehabilitation field. In here, a patient can use the movement imagery to artificially induce movements of the paretic arms through FES in real-time.

Five patients who had a stroke that affected the motor system participated in the current study, and were trained across 10 to 24 sessions lasting about 40 min each with the recoveriX® system. The patients had to imagine 80 left and 80 right hand movements. The electroencephalogram (EEG) data was analyzed with Common Spatial Patterns (CSP) and linear discriminant analysis (LDA) and a feedback was provided in form of a cursor on a computer screen. If the correct imagination was classified, the FES device was also activated to induce the right or left hand movement.

In at least one session, all patients were able to achieve a maximum accuracy above 96%. Moreover, all patients exhibited improvements in motor function. On one hand, the high accuracies achieved within the study show that the patients are highly motivated to participate into a study to improve their lost motor functions. On the other hand, this study reflects the efficacy of combining motor imagination, visual feedback and real hand movement that activates tactile and proprioceptive systems.

Source: O174 Preliminary results of testing the recoveriX system on stroke patients – Clinical Neurophysiology

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