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Ischemic stroke of the middle cerebral artery (MCA), a major brain vessel that supplies the primary motor and premotor cortex, is one of the most common causes for severe upper limb impairment. Currently available motor rehabilitation training largely lacks satisfying efficacy with over 70% of stroke survivors showing residual upper limb dysfunctio...
Contexts in source publication
Context 1
... the recruitment period (December 1st, 2016-February 28, 2018), 44 patients were screened for their eligibility (Figure 2). In total, 37 patients (84%) had to be excluded because they did not meet inclusion criteria. ...
Context 2
... from this preregistered PoC study found only anecdotal evidence for the two main preregistered hypotheses, suggesting that the present sample of MCA stroke patients struggled to activate and self-regulate the SMA during kinesthetic motor imagery-based graded neurofeedback training. Comparing individual effect sizes and Bayes factors (Tables 2, 3) found for SMA activation (H1 A ) with patients' motor impairment scores (Table 1), we found that patients who were the least impaired (P4 and P5) showed the largest SMA activation. ...
Citations
... 69,70 Bayesian sampling plans should be reported in sufficient detail, including the assumed effect size, prior distribution, and stopping criterion in the form of a posterior interval or Bayes factor for the alternative and null hypotheses. 71,72 Frequentist and Bayesian sample size estimations both require specifying a minimum effect size that the used statistical test aims to detect with a specific power or sensitivity, respectively. Several open-source tools are available for power analysis computations (see Table 1). ...
Significance
The expansion of functional near-infrared spectroscopy (fNIRS) methodology and analysis tools gives rise to various design and analytical decisions that researchers have to make. Several recent efforts have developed guidelines for preprocessing, analyzing, and reporting practices. For the planning stage of fNIRS studies, similar guidance is desirable. Study preregistration helps researchers to transparently document study protocols before conducting the study, including materials, methods, and analyses, and thus, others to verify, understand, and reproduce a study. Preregistration can thus serve as a useful tool for transparent, careful, and comprehensive fNIRS study design.
Aim
We aim to create a guide on the design and analysis steps involved in fNIRS studies and to provide a preregistration template specified for fNIRS studies.
Approach
The presented preregistration guide has a strong focus on fNIRS specific requirements, and the associated template provides examples based on continuous-wave (CW) fNIRS studies conducted in humans. These can, however, be extended to other types of fNIRS studies.
Results
On a step-by-step basis, we walk the fNIRS user through key methodological and analysis-related aspects central to a comprehensive fNIRS study design. These include items specific to the design of CW, task-based fNIRS studies, but also sections that are of general importance, including an in-depth elaboration on sample size planning.
Conclusions
Our guide introduces these open science tools to the fNIRS community, providing researchers with an overview of key design aspects and specification recommendations for comprehensive study planning. As such it can be used as a template to preregister fNIRS studies or merely as a tool for transparent fNIRS study design.
... Recently preliminary data from the proof-of-concept (PoC) study of a new paradigm were published where participants practiced not only to activate the ROI, but also to regulate the degree of this activation-the so-called "graded fMRI neurofeedback" (Mehler et al., 2020). This paradigm was previously tested in healthy volunteers (Mehler et al., 2019;Sorger et al., 2018), then transferred later to the stroke population (Mehler et al., 2020). ...
... Recently preliminary data from the proof-of-concept (PoC) study of a new paradigm were published where participants practiced not only to activate the ROI, but also to regulate the degree of this activation-the so-called "graded fMRI neurofeedback" (Mehler et al., 2020). This paradigm was previously tested in healthy volunteers (Mehler et al., 2019;Sorger et al., 2018), then transferred later to the stroke population (Mehler et al., 2020). Vol. ...
... The patients underwent two training sessions of movement imagination of the paretic hand based on the BOLD feedback signal from the SMA at the lesion side and learned to activate the signal from the SMA. The authors keep open the confirmation of the hypothesis of the ability to regulate the degree of this activation and invite to discuss this topic (Mehler et al., 2020). We would like to emphasize the boldness of the declared paradigm that considers the model of neurofeedback as reinforcing the "interactive brain" concept. ...
Interactive brain stimulation is a new generation of neurofeedback characterized by a radical change in the targets of cognitive (volitional, adaptive) influence. These targets are represented by specific cerebral structures and neural networks, the reconstruction of which leads to the brain functions' restoration and behavioral metamorphoses. Functional magnetic resonance imaging (fMRI) in the neurofeedback contour uses a natural intravascular tracer, a blood-oxygenation-level-dependent (BOLD) signal as feedback. The subject included into the "interactive brain contour" learns to modulate and modify his or her own cerebral networks, creating new ones or "awakening" pre-existing ones, in order to improve (or restore) mental, sensory, or motor functions. In this review we focus on interactive brain stimulation based on BOLD signal and its role in the motor rehabilitation of stroke, briefly introducing the basic concepts of the so-called "network vocabulary" and general biophysical basis of the BOLD signal. We also discuss a bimodal fMRI-EEG neurofeedback platform and the prospects of fMRI technology in controlling functional connectivity, a numerical assessment of neuroplasticity.
... Understandably, the efficacy of SMA modulations through NF was investigated in several of the reviewed studies (Subramanian et al., 2011(Subramanian et al., , 2016Papoutsi et al., 2018Papoutsi et al., , 2020Mehler et al., 2019Mehler et al., , 2020. Following NF training, SMA activity was found to be successfully regulated by the study populations afflicted with PD (Subramanian et al., 2011(Subramanian et al., , 2016 and HD (Papoutsi et al., 2018(Papoutsi et al., , 2020 with improvements in motor performance. ...
Dysregulated frontostriatal circuitries are viewed as a common target for the treatment of aberrant behaviors in various psychiatric and neurological disorders. Accordingly, experimental neurofeedback paradigms have been applied to modify the frontostriatal circuitry. The human frontostriatal circuitry is topographically and functionally organized into the “limbic,” the “associative,” and the “motor” subsystems underlying a variety of affective, cognitive, and motor functions. We conducted a systematic review of the literature regarding functional magnetic resonance imaging-based neurofeedback studies that targeted brain activations within the frontostriatal circuitry. Seventy-nine published studies were included in our survey. We assessed the efficacy of these studies in terms of imaging findings of neurofeedback intervention as well as behavioral and clinical outcomes. Furthermore, we evaluated whether the neurofeedback targets of the studies could be assigned to the identifiable frontostriatal subsystems. The majority of studies that targeted frontostriatal circuitry functions focused on the anterior cingulate cortex, the dorsolateral prefrontal cortex, and the supplementary motor area. Only a few studies ( n = 14) targeted the connectivity of the frontostriatal regions. However, post-hoc analyses of connectivity changes were reported in more cases ( n = 32). Neurofeedback has been frequently used to modify brain activations within the frontostriatal circuitry. Given the regulatory mechanisms within the closed loop of the frontostriatal circuitry, the connectivity-based neurofeedback paradigms should be primarily considered for modifications of this system. The anatomical and functional organization of the frontostriatal system needs to be considered in decisions pertaining to the neurofeedback targets.
... However, SMA's real implication in motor recovery and its use is not well understood. It was tested in the motor rehabilitation post-stroke for the upper limb in one proof-of-concept study (Mehler, 2020). NFB with functional magnetic resonance (fMRI) was provided from the SMA targeting two different NFB target levels (low and high). ...
Stroke is a severe health issue, and motor recovery after stroke remains an important challenge in the rehabilitation field. Neurofeedback (NFB), as part of a brain–computer interface, is a technique for modulating brain activity using on-line feedback that has proved to be useful in motor rehabilitation for the chronic stroke population in addition to traditional therapies. Nevertheless, its use and applications in the field still leave unresolved questions. The brain pathophysiological mechanisms after stroke remain partly unknown, and the possibilities for intervention on these mechanisms to promote cerebral plasticity are limited in clinical practice. In NFB motor rehabilitation, the aim is to adapt the therapy to the patient’s clinical context using brain imaging, considering the time after stroke, the localization of brain lesions, and their clinical impact, while taking into account currently used biomarkers and technical limitations. These modern techniques also allow a better understanding of the physiopathology and neuroplasticity of the brain after stroke. We conducted a narrative literature review of studies using NFB for post-stroke motor rehabilitation. The main goal was to decompose all the elements that can be modified in NFB therapies, which can lead to their adaptation according to the patient’s context and according to the current technological limits. Adaptation and individualization of care could derive from this analysis to better meet the patients’ needs. We focused on and highlighted the various clinical and technological components considering the most recent experiments. The second goal was to propose general recommendations and enhance the limits and perspectives to improve our general knowledge in the field and allow clinical applications. We highlighted the multidisciplinary approach of this work by combining engineering abilities and medical experience. Engineering development is essential for the available technological tools and aims to increase neuroscience knowledge in the NFB topic. This technological development was born out of the real clinical need to provide complementary therapeutic solutions to a public health problem, considering the actual clinical context of the post-stroke patient and the practical limits resulting from it.
... Hence, fNIRS-NFT based on (pre) motor cortices such as the SMA may be a promising approach for mobile, scalable fNIRS-NFT. Further, building up on previous reports of parametric modulations in relevant neural circuits during motor imagery [69,70], more recent developments for hemodynamic NFT protocols allow to provide more precise and resource-efficient measures of self-regulation success [115,136]. Currently, one ongoing fNIRS-NFT trial is registered with PD patients that targets the SMA (https://www.drks.de/drks_web/navigate.do?navigationId=trial. HTML&TRIAL_ID=DRKS00022997). ...
Purpose: Motor symptoms of patients suffering from Parkinson’s disease (PD) are currently mainly treated with dopaminergic pharmacology, and where indicated, with deep brain stimulation. In the last decades, a substantial body of literature has described neurophysiological correlates related to both motor symptoms and treatment effects. These mechanistic insights allow, at least theoretically, for precise targeting of neural processes responsible for PD motor symptoms.
Materials and methods: Literature search was conducted to identify electrophysiological and hemodynamic signals that may serve as neural targets for future neurofeedback training protocols.
Results: In particular alpha, beta and gamma oscillations over the motor cortex show high potential as neural targets for electrophysiological neurofeedback training. Hemodynamic functional magnetic resonance imaging (fMRI) with higher spatial resolution provides additional insights about network activity between cortical and subcortical brain regions in response to established treatments. fMRI based neurofeedback training (NFT) further allows targeting involved networks. Hemodynamic functional near infrared spectroscopy (fNIRS) may be a suitable transfer technology for more and cost-efficient hemodynamic NFT.
Conclusions: This scoping review presents summarises neural markers that may be promising for NFT interventions that are informed by validated neural circuit models. Recommendations for best practice in study design and reporting are provided, highlighting the importance of adequate control conditions and statistical power.
... Motor imagery training could improve the precision and accuracy of upper limb movements and elevate the movement of hemiplegic limbs (Grabherr et al., 2015). Recently, the application of brain imaging has established the efficacy of MIT in rehabilitating stroke patients (Lioi et al., 2020;Mehler et al., 2020;Colamarino et al., 2022). Early application of MIT in post-stroke hemiplegic patients can enhance sensory information input, promote dormant synapse activation, accelerate the ischemic penumbra reperfusion, and improve cerebral blood supply, thus, enhancing the rehabilitation effect of stroke (Tavazzi et al., 2022). ...
Stroke, including hemorrhagic and ischemic stroke, refers to the blood supply disorder in the local brain tissue for various reasons (aneurysm, occlusion, etc.). It leads to regional brain circulation imbalance, neurological complications, limb motor dysfunction, aphasia, and depression. As the second-leading cause of death worldwide, stroke poses a significant threat to human life characterized by high mortality, disability, and recurrence. Therefore, the clinician has to care about the symptoms of stroke patients in the acute stage and formulate an effective postoperative rehabilitation plan to facilitate the recovery in patients. We summarize a novel application and update of the rehabilitation therapy in limb motor rehabilitation of stroke patients to provide a potential future stroke rehabilitation strategy.
... FMRI NF has also been suggested as a potentially useful tool in stroke rehabilitation (14,15), however to date no randomized, sham-controlled trials have been published. Previous pilot studies have shown that stroke survivors appear to be able to use fMRI NF to control activity in a variety of motor regions (16)(17)(18) demonstrating feasibility of this approach. After sufficient training, participants receiving NF training may be able to maintain the ability to self-modulate brain activity outside of the training sessions, leading to potentially long-lasting improvements (for review see: (19)). ...
Real-time functional magnetic resonance imaging (fMRI) neurofeedback allows individuals to self-modulate their ongoing brain activity. This may be a useful tool in clinical disorders which are associated with altered brain activity patterns. Motor impairment after stroke has previously been associated with decreased laterality of motor cortex activity. Here we examined whether chronic stroke survivors were able to use real-time fMRI neurofeedback to increase laterality of motor cortex activity and assessed effects on motor performance and on brain structure and function. We carried out a randomized, double-blind, sham-controlled trial in which 24 chronic stroke survivors with mild to moderate upper limb impairment experienced three training days of either Real (n=12) or Sham (n=12) neurofeedback. Stroke survivors were able to use Real neurofeedback to increase laterality of motor cortex activity within, but not across, training days. Improvement in gross hand motor performance assessed by the Jebsen Taylor Test (JTT) was observed in the Real neurofeedback group compared to Sham. However, there were no improvements on the Action Research Arm Test (ARAT) or the Upper Extremity Fugl-Meyer (UE-FM) score. Additionally, decreased white-matter asymmetry of the corticospinal tracts was detected 1-week after neurofeedback training, indicating that the tracts become more similar with Real neurofeedback. Changes in the affected corticospinal tract was positively correlated with neurofeedback performance. Therefore, here we demonstrate that chronic stroke survivors are able to use fMRI neurofeedback to self-modulate motor cortex activity, and that training is associated with improvements in hand motor performance and with white matter structural changes.
... Sequential Bayesian sampling provides higher statistical sensitivity compared to fixed-N sampling plans, in particular for small effects (Schönbrodt and Wagenmakers, 2018). Sampling plans could be either based on effects from a neural outcome measure, such as self-regulation success (e.g., see Mehler et al., 2020) or a behavioral/clinical outcome measure. A detailed description of the sampling plan should ideally be preregistered (see also section 4.9). ...
... Neurofeedback training in dementia is a relatively new field. Thus, in order to accelerate the development of this novel clinical application, as well as to increase transparency and reliability of proposed protocols, we strongly recommend that researchers pre-register their protocols comprehensively to provide transparency about a priori hypotheses and delineate planned from exploratory hypotheses (see for a detailed example, Mehler et al., 2020). We further encourage authors to share data and code that support their results. ...
Dementia describes a set of symptoms that occur in neurodegenerative disorders and that is characterized by gradual loss of cognitive and behavioral functions. Recently, non-invasive neurofeedback training has been explored as a potential complementary treatment for patients suffering from dementia or mild cognitive impairment. Here we systematically reviewed studies that explored neurofeedback training protocols based on electroencephalography or functional magnetic resonance imaging for these groups of patients. From a total of 1,912 screened studies, 10 were included in our final sample ( N = 208 independent participants in experimental and N = 81 in the control groups completing the primary endpoint). We compared the clinical efficacy across studies, and evaluated their experimental designs and reporting quality. In most studies, patients showed improved scores in different cognitive tests. However, data from randomized controlled trials remains scarce, and clinical evidence based on standardized metrics is still inconclusive. In light of recent meta-research developments in the neurofeedback field and beyond, quality and reporting practices of individual studies are reviewed. We conclude with recommendations on best practices for future studies that investigate the effects of neurofeedback training in dementia and cognitive impairment.
... To make neurofeedback findings transparent and reliable, as well as to allow further collaboration between research groups, we strongly recommend that researchers explore and implement open science research practices where possible (Allen and Mehler, 2019;Nosek et al., 2015) by preregistering their study protocol and sharing the data that support their final results. Analytical degrees of freedom remain a controversial topic in neuroimaging ( Botvinik-Nezer et al., 2020;Carp, 2012); real-time experiments already predeclare a substantial part of their analysis pipeline when setting parameters for real-time data analysis and it is hence in particular suited for study preregistration (e.g., Mehler et al. (2020)) or publishable research protocols (e.g., Cox et al. (2016)). Regarding data sharing practices, researchers can benefit from recommendations for reliable analysis pipelines (Nichols et al., 2017), as well tools to standardize data accessibility and reproducibility (Gorgolewski et al., 2017) and facilitate data sharing ( Gorgolewski et al., 2016;Poldrack et al., 2013). ...
Major depressive disorder (MDD) is the leading cause of disability worldwide. Neurofeedback training has been suggested as a potential additional treatment option for MDD patients not reaching remission from standard care (i.e., psychopharmacology and psychotherapy). Here we systematically reviewed neurofeedback studies employing electroencephalography, or functional magnetic resonance-based protocols in depressive patients. Of 585 initially screened studies, 24 were included in our final sample (N = 480 patients in experimental and N = 194 in the control groups completing the primary endpoint). We evaluated the clinical efficacy across studies and attempted to group studies according to the control condition categories currently used in the field that affect clinical outcomes in group comparisons. In most studies, MDD patients showed symptom improvement superior to the control group(s). However, most articles did not comply with the most stringent study quality and reporting practices. We conclude with recommendations on best practices for experimental designs and reporting standards for neurofeedback training.
... It could cause brain nerve cell damage or necrosis, leading to limb discordance, spasm, and even hemiplegia [2]. Since standard physical therapy is costly and limited [3], there has been a lack of effective stroke treatment. The functional rehabilitation of stroke patients mainly depends on neural plasticity [4]. ...
Motor imagery (MI) is an important part of brain-computer interface (BCI) research, which could decode the subject’s intention and help remodel the neural system of stroke patients. Therefore, accurate decoding of electroencephalography- (EEG-) based motion imagination has received a lot of attention, especially in the research of rehabilitation training. We propose a novel multifrequency brain network-based deep learning framework for motor imagery decoding. Firstly, a multifrequency brain network is constructed from the multichannel MI-related EEG signals, and each layer corresponds to a specific brain frequency band. The structure of the multifrequency brain network matches the activity profile of the brain properly, which combines the information of channel and multifrequency. The filter bank common spatial pattern (FBCSP) algorithm filters the MI-based EEG signals in the spatial domain to extract features. Further, a multilayer convolutional network model is designed to distinguish different MI tasks accurately, which allows extracting and exploiting the topology in the multifrequency brain network. We use the public BCI competition IV dataset 2a and the public BCI competition III dataset IIIa to evaluate our framework and get state-of-the-art results in the first dataset, i.e., the average accuracy is 83.83% and the value of kappa is 0.784 for the BCI competition IV dataset 2a, and the accuracy is 89.45% and the value of kappa is 0.859 for the BCI competition III dataset IIIa. All these results demonstrate that our framework can classify different MI tasks from multichannel EEG signals effectively and show great potential in the study of remodelling the neural system of stroke patients.
