Simultaneous EEG-fNIRS reveals how age and feedback affect motor imagery signatures

Brain-computer interfaces and human factors: the role of language and cultural differences-Still a missing gap?

Brain-computer interfaces (BCIs) aim at the non-invasive investigation of brain activity for supporting communication and interaction of the users with their environment by means of brain-machine assisted technologies. Despite technological progress and promising research aimed at understanding the influence of human factors on BCI effectiveness, some topics still remain unexplored. The aim of this article is to discuss why it is important to consider the language of the user, its embodied grounding in perception, action and emotions, and its interaction with cultural differences in information processing in future BCI research. Based on evidence from recent studies, it is proposed that detection of language abilities and language training are two main topics of enquiry of future BCI studies to extend communication among vulnerable and healthy BCI users from bench to bedside and real world applications. In addition, cultural differences shape perception, actions, cognition, language and emotions subjectively, behaviorally as well as neuronally. Therefore, BCI applications should consider cultural differences in information processing to develop culture- and language-sensitive BCI applications for different user groups and BCIs, and investigate the linguistic and cultural contexts in which the BCI will be used.

Analyzing and computing humans by means of the brain using Brain-Computer Interfaces - understanding the user - previous evidence, self-relevance and the user's self-concept as potential superordinate human factors of relevance

Brain-computer interfaces (BCIs) are well-known instances of how technology can convert a user's brain activity taken from non-invasive electroencephalography (EEG) into computer commands for the purpose of computer-assisted communication and interaction. However, not all users are attaining the accuracy required to use a BCI consistently, despite advancements in technology. Accordingly, previous research suggests that human factors could be responsible for the variance in BCI performance among users. Therefore, the user's internal mental states and traits including motivation, affect or cognition, personality traits, or the user's satisfaction, beliefs or trust in the technology have been investigated. Going a step further, this manuscript aims to discuss which human factors could be potential superordinate factors that influence BCI performance, implicitly, explicitly as well as inter- and intraindividually. Based on the results of previous studies that used comparable protocols to examine the motivational, affective, cognitive state or personality traits of healthy and vulnerable EEG-BCI users within and across well-investigated BCIs (P300-BCIs or SMR-BCIs, respectively), it is proposed that the self-relevance of tasks and stimuli and the user's self-concept provide a huge potential for BCI applications. As potential key human factors self-relevance and the user's self-concept (self-referential knowledge and beliefs about one's self) guide information processing and modulate the user's motivation, attention, or feelings of ownership, agency, and autonomy. Changes in the self-relevance of tasks and stimuli as well as self-referential processing related to one's self (self-concept) trigger changes in neurophysiological activity in specific brain networks relevant to BCI. Accordingly, concrete examples will be provided to discuss how past and future research could incorporate self-relevance and the user's self-concept in the BCI setting - including paradigms, user instructions, and training sessions.

Aging effects on electrical and hemodynamic responses in the sensorimotor network during unilateral proximal upper limb functional tasks

Healthy aging leads to poorer performance in upper limb (UL) daily living movements. Understanding the neural correlates linked with UL functional movements may help to better understand how healthy aging affects motor control. Two non-invasive neuroimaging methods allow for monitoring the movement-related brain activity: functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG), respectively based on the hemodynamic response and electrical activity of brain regions. Coupled, they provide a better spatiotemporal mapping. The aim of this study was to evaluate the effect of healthy aging on the bilateral sensorimotor (SM1) activation patterns of functional proximal UL movements. Twenty-one young and 21 old healthy participants realized two unilateral proximal UL movements during: i) a paced reaching target task and ii) a circular steering task to capture the speed-accuracy trade-off. Combined fNIRS-EEG system was synchronised with movement capture system to record SM1 activation while moving. The circular steering task performance was significantly lower for the older group. The rate of increase in hemodynamic response was longer in the older group with no difference on the amplitude of fNIRS signal for the two tasks. The EEG results showed aging related reduction of the alpha-beta rhythms synchronisation but no desynchronisation modification. In conclusion, this study uncovers the age-related changes in brain electrical and hemodynamic response patterns in the bilateral sensorimotor network during two functional proximal UL movements using two complementary neuroimaging methods. This opens up the possibility to utilise combined fNIRS-EEG for monitoring the movement-related neuroplasticity in clinical practice.

Electroencephalography-Based Brain-Machine Interfaces in Older Adults: A Literature Review

The aging process is a multifaceted phenomenon that affects cognitive-affective and physical functioning as well as interactions with the environment. Although subjective cognitive decline may be part of normal aging, negative changes objectified as cognitive impairment are present in neurocognitive disorders and functional abilities are most impaired in patients with dementia. Electroencephalography-based brain-machine interfaces (BMI) are being used to assist older people in their daily activities and to improve their quality of life with neuro-rehabilitative applications. This paper provides an overview of BMI used to assist older adults. Both technical issues (detection of signals, extraction of features, classification) and application-related aspects with respect to the users' needs are considered.

Concurrent fNIRS and EEG for Brain Function Investigation: A Systematic, Methodology-Focused Review

Electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) stand as state-of-the-art techniques for non-invasive functional neuroimaging. On a unimodal basis, EEG has poor spatial resolution while presenting high temporal resolution. In contrast, fNIRS offers better spatial resolution, though it is constrained by its poor temporal resolution. One important merit shared by the EEG and fNIRS is that both modalities have favorable portability and could be integrated into a compatible experimental setup, providing a compelling ground for the development of a multimodal fNIRS-EEG integration analysis approach. Despite a growing number of studies using concurrent fNIRS-EEG designs reported in recent years, the methodological reference of past studies remains unclear. To fill this knowledge gap, this review critically summarizes the status of analysis methods currently used in concurrent fNIRS-EEG studies, providing an up-to-date overview and guideline for future projects to conduct concurrent fNIRS-EEG studies. A literature search was conducted using PubMed and Web of Science through 31 August 2021. After screening and qualification assessment, 92 studies involving concurrent fNIRS-EEG data recordings and analyses were included in the final methodological review. Specifically, three methodological categories of concurrent fNIRS-EEG data analyses, including EEG-informed fNIRS analyses, fNIRS-informed EEG analyses, and parallel fNIRS-EEG analyses, were identified and explained with detailed description. Finally, we highlighted current challenges and potential directions in concurrent fNIRS-EEG data analyses in future research.

fNIRS-based adaptive visuomotor task improves sensorimotor cortical activation

OBJECTIVE

Investigating how to promote the functional activation of the central sensorimotor system is an important goal in the neurorehabilitation research domain. We aim to validate the effectiveness of facilitating cortical excitability using a closed-loop visuomotor task, in which the task difficulty is adaptively adjusted based on an individual's sensorimotor cortical activation.

APPROACH

We developed a novel visuomotor task, in which subjects moved a handle of a haptic device along a specific path while exerting a constant force against a virtual surface under visual feedback. The difficulty levels of the task were adapted with the aim of increasing the activation of sensorimotor areas, measured non-invasively by functional near-infrared spectroscopy. The changes in brain activation of the bilateral prefrontal cortex, sensorimotor cortex, and the occipital cortex obtained during the adaptive visuomotor task (adaptive group), were compared to the brain activation pattern elicited by the same duration of task with random difficulties in a control group.

MAIN RESULTS

During one intervention session, the adaptive group showed significantly increased activation in the bilateral sensorimotor cortex, also enhanced effective connectivity between the prefrontal and sensorimotor areas compared to the control group.

SIGNIFICANCE

Our findings demonstrated that the fNIRS-based adaptive visuomotor task with high ecological validity can facilitate the neural activity in sensorimotor areas and thus has the potential to improve hand motor functions.

Toward an Adapted Neurofeedback for Post-stroke Motor Rehabilitation: State of the Art and Perspectives

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.

Viewing neurovascular coupling through the lens of combined EEG-fNIRS: A systematic review of current methods

Neurovascular coupling is a key physiological mechanism that occurs in the healthy human brain, and understanding this process has implications for understanding the aging and neuropsychiatric populations. Combined electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) has emerged as a promising, noninvasive tool for probing neurovascular interactions in humans. However, the utility of this approach critically depends on the methodological quality used for multimodal integration. Despite a growing number of combined EEG-fNIRS applications reported in recent years, the methodological rigor of past studies remains unclear, limiting the accurate interpretation of reported findings and hindering the translational application of this multimodal approach. To fill this knowledge gap, we critically evaluated various methodological aspects of previous combined EEG-fNIRS studies performed in healthy individuals. A literature search was conducted using PubMed and PsycINFO on June 28, 2021. Studies involving concurrent EEG and fNIRS measurements in awake and healthy individuals were selected. After screening and eligibility assessment, 96 studies were included in the methodological evaluation. Specifically, we critically reviewed various aspects of participant sampling, experimental design, signal acquisition, data preprocessing, outcome selection, data analysis, and results presentation reported in these studies. Altogether, we identified several notable strengths and limitations of the existing EEG-fNIRS literature. In light of these limitations and the features of combined EEG-fNIRS, recommendations are made to improve and standardize research practices to facilitate the use of combined EEG-fNIRS when studying healthy neurovascular coupling processes and alterations in neurovascular coupling among various populations.

Neural Suppression Elicited During Motor Imagery Following the Observation of Biological Motion From Point-Light Walker Stimuli

Introduction: Advantageous effects of biological motion (BM) detection, a low-perceptual mechanism that allows the rapid recognition and understanding of spatiotemporal characteristics of movement via salient kinematics information, can be amplified when combined with motor imagery (MI), i.e., the mental simulation of motor acts. According to Jeannerod's neurostimulation theory, asynchronous firing and reduction of mu and beta rhythm oscillations, referred to as suppression over the sensorimotor area, are sensitive to both MI and action observation (AO) of BM. Yet, not many studies investigated the use of BM stimuli using combined AO-MI tasks. In this study, we assessed the neural response in the form of event-related synchronization and desynchronization (ERD/S) patterns following the observation of point-light-walkers and concordant MI, as compared to MI alone. Methods: Twenty right-handed healthy participants accomplished the experimental task by observing BM stimuli and subsequently performing the same movement using kinesthetic MI (walking, cycling, and jumping conditions). We recorded an electroencephalogram (EEG) with 32 channels and performed time-frequency analysis on alpha (8-13 Hz) and beta (18-24 Hz) frequency bands during the MI task. A two-way repeated-measures ANOVA was performed to test statistical significance among conditions and electrodes of interest. Results: The results revealed significant ERD/S patterns in the alpha frequency band between conditions and electrode positions. Post hoc comparisons showed significant differences between condition 1 (walking) and condition 3 (jumping) over the left primary motor cortex. For the beta band, a significantly less difference in ERD patterns (p < 0.01) was detected only between condition 3 (jumping) and condition 4 (reference). Discussion: Our results confirmed that the observation of BM combined with MI elicits a neural suppression, although just in the case of jumping. This is in line with previous findings of AO and MI (AOMI) eliciting a neural suppression for simulated whole-body movements. In the last years, increasing evidence started to support the integration of AOMI training as an adjuvant neurorehabilitation tool in Parkinson's disease (PD). Conclusion: We concluded that using BM stimuli in AOMI training could be promising, as it promotes attention to kinematic features and imitative motor learning.

The Impact of Context on EEG Motor Imagery Neurofeedback and Related Motor Domains

Neurofeedback (NF) is a versatile non-invasive neuromodulation technique. In combination with motor imagery (MI), NF has considerable potential for enhancing motor performance or supplementing motor rehabilitation. However, not all users achieve reliable NF control. While research has focused on various brain signal properties and the optimisation of signal processing to solve this issue, the impact of context, i.e. the conditions in which NF motor tasks occur, is comparatively unknown. We review current research on the impact of context on MI NF and related motor domains. We identify long-term factors that act at the level of the individual or of the intervention, and short-term factors, with levels before/after and during a session. The reviewed literature indicates that context plays a significant role. We propose considering context factors as well as within-level and across-level interactions when studying MI NF.

Application of bi-modal signal in the classification and recognition of drug addiction degree based on machine learning

Most studies on drug addiction degree are made based on statistical scales, addicts' account, and subjective judgement of rehabilitation doctors. No objective, quantified evaluation has been made. This paper uses devises the synchronous bimodal signal collection and experimentation paradigm with electroencephalogram (EEG) and forehead high-density near-infrared spectroscopy (NIRS) device. The drug addicts are classified into mild, moderate and severe groups with reference to the suggestions of researchers and medical experts. Data of 45 drug addicts (mild: 15; moderate: 15; and severe: 15) is collected, and then used to design an addiction degree testing algorithm based on decision fusion. The algorithm is used to classify mild, moderate and severe addiction. This paper pioneers to use two types of Convolutional Neural Network (CNN) to abstract the EEG and NIR data of drug addicts, and introduces batch normalization to CNN, thus accelerating training process, reducing parameter sensitivity, and enhancing system robustness. The characteristics output by two CNNs are transformed into dimensions. Two new characteristics are assigned with a weight of 50% each. The data is used for decision fusion. In the networks, 27 subjects are used as training sets, 9 as validation sets, and 9 as testing sets. The 3-class accuracy remains to be 63.15%, preliminarily justifying this method as an effective approach to measure drug addiction degree. And the method is ready to use, objective, and offers results in real time.

A Systematic Review of the Application of Functional Near-Infrared Spectroscopy to the Study of Cerebral Hemodynamics in Healthy Aging

Positron emission tomography (PET) and functional magnetic resonance imaging (fMRI) studies have shown that healthy aging is associated with functional brain deterioration that preferentially affects the prefrontal cortex. This article reviews the application of an alternative method, functional near-infrared spectroscopy (fNIRS), to the study of age-related changes in cerebral hemodynamics and factors that influence cerebral hemodynamics in the elderly population. We conducted literature searches in PudMed and PsycINFO, and selected only English original research articles that used fNIRS to study healthy individuals with a mean age of ≥ 55 years. All articles were published in peer-reviewed journals between 1977 and May 2019. We synthesized 114 fNIRS studies examining hemodynamic changes that occurred in the resting state and during the tasks of sensation and perception, motor control, semantic processing, word retrieval, attentional shifting, inhibitory control, memory, and emotion and motivation in healthy older adults. This review, which was not registered in a registry, reveals an age-related reduction in resting-state cerebral oxygenation and connectivity in the prefrontal cortex. It also shows that aging is associated with a reduction in functional hemispheric asymmetry and increased compensatory activity in the frontal lobe across multiple task domains. In addition, this article describes the beneficial effects of healthy lifestyles and the detrimental effects of cardiovascular disease risk factors on brain functioning among nondemented older adults. Limitations of this review include exclusion of gray and non-English literature and lack of meta-analysis. Altogether, the fNIRS literature provides some support for various neurocognitive aging theories derived from task-based PET and fMRI studies. Because fNIRS is relatively motion-tolerant and environmentally unconstrained, it is a promising tool for fostering the development of aging biomarkers and antiaging interventions.

Brain-Based Binary Communication Using Spatiotemporal Features of fNIRS Responses

“Locked-in” patients lose their ability to communicate naturally due to motor system dysfunction. Brain-computer interfacing offers a solution for their inability to communicate by enabling motor-independent communication. Straightforward and convenient in-session communication is essential in clinical environments. The present study introduces a functional near-infrared spectroscopy (fNIRS)-based binary communication paradigm that requires limited preparation time and merely nine optodes. Eighteen healthy participants performed two mental imagery tasks, mental drawing and spatial navigation, to answer yes/no questions during one of two auditorily cued time windows. Each of the six questions was answered five times, resulting in five trials per answer. This communication paradigm thus combines both spatial (two different mental imagery tasks, here mental drawing for “yes” and spatial navigation for “no”) and temporal (distinct time windows for encoding a “yes” and “no” answer) fNIRS signal features for information encoding. Participants’ answers were decoded in simulated real-time using general linear model analysis. Joint analysis of all five encoding trials resulted in an average accuracy of 66.67 and 58.33% using the oxygenated (HbO) and deoxygenated (HbR) hemoglobin signal respectively. For half of the participants, an accuracy of 83.33% or higher was reached using either the HbO signal or the HbR signal. For four participants, effective communication with 100% accuracy was achieved using either the HbO or HbR signal. An explorative analysis investigated the differentiability of the two mental tasks based solely on spatial fNIRS signal features. Using multivariate pattern analysis (MVPA) group single-trial accuracies of 58.33% (using 20 training trials per task) and 60.56% (using 40 training trials per task) could be obtained. Combining the five trials per run using a majority voting approach heightened these MVPA accuracies to 62.04 and 75%. Additionally, an fNIRS suitability questionnaire capturing participants’ physical features was administered to explore its predictive value for evaluating general data quality. Obtained questionnaire scores correlated significantly (r = -0.499) with the signal-to-noise of the raw light intensities. While more work is needed to further increase decoding accuracy, this study shows the potential of answer encoding using spatiotemporal fNIRS signal features or spatial fNIRS signal features only.

Enhancing fNIRS Analysis Using EEG Rhythmic Signatures: An EEG-Informed fNIRS Analysis Study

Neurovascular coupling represents the relationship between changes in neuronal activity and cerebral hemodynamics. Concurrent Electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) recording and integration analysis has emerged as a promising multi-modal neuroimaging approach to study the neurovascular coupling as it provides complementary properties with regard to high temporal and moderate spatial resolution of brain activity. In this study we developed an EEG-informed-fNIRS analysis framework to investigate the neuro-correlate between neuronal activity and cerebral hemodynamics by identifying specific EEG rhythmic modulations which contribute to the improvement of the fNIRS-based general linear model (GLM) analysis. Specifically, frequency-specific regressors derived from EEG were used to construct design matrices to guide the GLM analysis of the fNIRS signals collected during a hand grasp task. Our results showed that the EEG-informed fNIRS GLM analysis, especially the alpha and beta band, revealed significantly higher sensitivity and specificity in localizing the task-evoked regions compared to the canonical boxcar model, demonstrating the strong correlations between hemodynamic response and EEG rhythmic modulations. Results also indicated that analysis based on the deoxygenated hemoglobin (HbR) signal slightly outperformed the oxygenated hemoglobin (HbO)-based analysis. The findings in our study not only validate the feasibility of enhancing fNIRS GLM analysis using simultaneously recorded EEG signals, but also provide a new perspective to study the neurovascular coupling of brain activity.

Investigating Priming Effects of Physical Practice on Motor Imagery-Induced Event-Related Desynchronization

For motor imagery (MI) to be effective, an internal representation of the to-be-imagined movement may be required. A representation can be achieved through prior motor execution (ME), but the neural correlates of MI that are primed by ME practice are currently unknown. In this study, young healthy adults performed MI practice of a unimanual visuo-motor task (Group MI, n = 19) or ME practice combined with subsequent MI practice (Group ME&MI, n = 18) while electroencephalography (EEG) was recorded. Data analysis focused on the MI-induced event-related desynchronization (ERD). Specifically, changes in the ERD and movement times (MT) between a short familiarization block of ME (Block pre-ME), conducted before the MI or the ME combined with MI practice phase, and a short block of ME conducted after the practice phase (Block post-ME) were analyzed. Neither priming effects of ME practice on MI-induced ERD were found nor performance-enhancing effects of MI practice in general. We found enhancements of the ERD and MT in Block post-ME compared to Block pre-ME, but only for Group ME&MI. A comparison of ME performance measures before and after the MI phase indicated however that these changes could not be attributed to the combination of ME and MI practice. The mixed results of this study may be a consequence of the considerable intra- and inter-individual differences in the ERD, introduced by specifics of the experimental setup, in particular the individual and variable task duration, and suggest that task and experimental setup can affect the interplay of ME and MI.

The use of EEG biofeedback to improve memory, concentration, attention and reduce emotional tension

Biofeedback is a method of giving patients computerised feedback signals about changes in the physiological state of their body. This allows them to learn how to consciously modify functions not controlled consciously. This method allows active and conscious involvement of the patient in controlling their own physiological processes. The therapy aims to regulate the frequency of human brain waves. The human brain produces different ranges of waves that are characteristic of different types of human activity, a mechanism used in this method. The use of this method in routine rehabilitation with a specifically designed computer programme provides physicians, physiotherapists, neuropsychologists and speech therapists with a new tool for treatment, opportunities for improvement in treatment, and helps them better plan and develop treatment strategies using Evidence-Based Medicine. The aim of the work is to discuss how EEG Biofeedback software can be applied in neurorehabilitation and to discuss the use of EEG Biofeedback software in order to improve memory, concentration, attention, reduce emotional tension, increase resistance to stress, improve self-control, self-esteem and relaxation. Key words EEG biofeedback, neurorehabilitation, computer software in rehabilitation Article received: 14.01.2019; Accepted: 17.09.2019

Linking brain activation to topological organization in the frontal lobe as a synergistic indicator to characterize the difference between various cognitive processes of executive functions

Executive functions (EFs) associated with the frontal lobe are vital for goal-orientated behavior. To date, limited efforts have been made to examine the relationships among the behavior, brain activation, and topological organization of functional networks in the frontal lobe underlying various EF tasks, including inhibition, working memory, and cognitive flexibility. In this study, functional near-infrared spectroscopy neuroimaging technique was used to systematically inspect the differences in the brain activation and the topological organization of brain networks between various EF tasks in the frontal lobe. In addition, the relationships between brain activation/network properties and task performances and the relationships between brain activation and network properties were, respectively, examined for different EF tasks. Consequently, we have discovered that the nodal and global properties of the resting-state and task-evoked networks, respectively, exhibited significant correlations with the activation of various brain regions during various EF tasks. In particular, the measure that links the neural activation to the topological organization of the brain networks in the frontal lobe can serve as a synergistic indicator to examine the difference between various EF tasks, which paves a way toward a comprehensive understanding of the neural mechanism underlying EFs.

Does Fractional Anisotropy Predict Motor Imagery Neurofeedback Performance in Healthy Older Adults?

Motor imagery neurofeedback training has been proposed as a potential add-on therapy for motor impairment after stroke, but not everyone benefits from it. Previous work has used white matter integrity to predict motor imagery neurofeedback aptitude in healthy young adults. We set out to test this approach with motor imagery neurofeedback that is closer to that used for stroke rehabilitation and in a sample whose age is closer to that of typical stroke patients. Using shrinkage linear discriminant analysis with fractional anisotropy values in 48 white matter regions as predictors, we predicted whether each participant in a sample of 21 healthy older adults (48–77 years old) was a good or a bad performer with 84.8% accuracy. However, the regions used for prediction in our sample differed from those identified previously, and previously suggested regions did not yield significant prediction in our sample. Including demographic and cognitive variables which may correlate with motor imagery neurofeedback performance and white matter structure as candidate predictors revealed an association with age but also led to loss of statistical significance and somewhat poorer prediction accuracy (69.6%). Our results suggest cast doubt on the feasibility of predicting the benefit of motor imagery neurofeedback from fractional anisotropy. At the very least, such predictions should be based on data collected using the same paradigm and with subjects whose characteristics match those of the target case as closely as possible.

Mapping the Cortical Network Arising From Up-Regulated Amygdaloidal Activation Using -Louvain Algorithm

The amygdala plays an important role in emotion processing. Several studies have proved that its activation can be regulated by real-time functional magnetic resonance imaging (rtfMRI)-based neurofeedback training. However, although studies have found brain regions that are functionally closely connected to the amygdala in the cortex, it is not clear whether these brain regions and the amygdala are structurally closely connected, and if they show the same training effect as the amygdala in the process of emotional regulation. In this paper, we instructed subjects to up-regulate the activation of the left amygdala (LA) through rtfMRI-based neurofeedback training. In order to fuse multimodal imaging data, we introduced a network analysis method called the -Louvain clustering algorithm. This method was used to integrate multimodal data from the training experiment and construct an LA-cortical network. Correlation analysis and main-effect analysis were conducted to determine the signal covariance associated with the activation of the target area; ultimately, we identified the left temporal pole superior as the amygdaloidal-cortical network region. As a deep nucleus in the brain, the treatment and stimulation of the amygdala remains challenging. Our results provide new insights for the regulation of activation in a deep nucleus using more neurofeedback techniques.

Shining a Light on Awareness: A Review of Functional Near-Infrared Spectroscopy for Prolonged Disorders of Consciousness

Qualitative clinical assessments of the recovery of awareness after severe brain injury require an assessor to differentiate purposeful behavior from spontaneous behavior. As many such behaviors are minimal and inconsistent, behavioral assessments are susceptible to diagnostic errors. Advanced neuroimaging tools can bypass behavioral responsiveness and reveal evidence of covert awareness and cognition within the brains of some patients, thus providing a means for more accurate diagnoses, more accurate prognoses, and, in some instances, facilitated communication. The majority of reports to date have employed the neuroimaging methods of functional magnetic resonance imaging, positron emission tomography, and electroencephalography (EEG). However, each neuroimaging method has its own advantages and disadvantages (e.g., signal resolution, accessibility, etc.). Here, we describe a burgeoning technique of non-invasive optical neuroimaging—functional near-infrared spectroscopy (fNIRS)—and review its potential to address the clinical challenges of prolonged disorders of consciousness. We also outline the potential for simultaneous EEG to complement the fNIRS signal and suggest the future directions of research that are required in order to realize its clinical potential.

Brain Electrodynamic and Hemodynamic Signatures Against Fatigue During Driving

Fatigue is likely to be gradually cumulated in a prolonged and attention-demanding task that may adversely affect task performance. To address the brain dynamics during a driving task, this study recruited 16 subjects to participate in an event-related lane-departure driving experiment. Each subject was instructed to maintain attention and task performance throughout an hour-long driving experiment. The subjects' brain electrodynamics and hemodynamics were simultaneously recorded via 32-channel electroencephalography (EEG) and 8-source/16-detector functional near-infrared spectroscopy (fNIRS). The behavior performance demonstrated that all subjects were able to promptly respond to lane-deviation events, even if the sign of fatigue arose in the brain, which suggests that the subjects were fighting fatigue during the driving experiment. The EEG event-related analysis showed strengthening alpha suppression in the occipital cortex, a common brain region of fatigue. Furthermore, we noted increasing oxygenated hemoglobin (HbO) of the brain to fight driving fatigue in the frontal cortex, primary motor cortex, parieto-occipital cortex and supplementary motor area. In conclusion, the increasing neural activity and cortical activations were aimed at maintaining driving performance when fatigue emerged. The electrodynamic and hemodynamic signatures of fatigue fighting contribute to our understanding of the brain dynamics of driving fatigue and address driving safety issues through the maintenance of attention and behavioral performance.

Non-physical approaches to counteract age-related functional deterioration: Applications for rehabilitation and neural mechanisms

Normal and pathological ageing are associated with several motor impairments that reduce quality of life and represent a general challenge for public healthcare systems. Consequently, over the past decades, many scientists and physiotherapists dedicated their research to the development and improvement of safe and costless methods to counteract the progressive decline of motor functions with age. The urgency of finding new and easy to implement methods is even more paramount in case of acute pathologies (e.g. stroke or hip surgery). The frailty of older population makes it difficult or even impossible to use traditional physical therapy at an early stage after the occurrence of a pathology. To that purpose, non-physical approaches such as cognitive training (e.g. memory, attention training) and mental techniques (e.g. motor imagery) have grown in popularity for the elderly. Such methods, involving individual and/or group exercises, have shown particular effects on increasing or maintaining cognitive functions, as well as physical performances. Improving the motor function (especially in older age) requires an improvement of motor execution, i.e. the pathway from the brain motor areas to the muscle but also higher cognitive control. The present work reviews different non-physical interventions that can be used as a complementary approach by asymptomatic or frail older adults, and the effects thereof on functional performance. The use of cognitive training or motor imagery protocols is recommended when physical practice is limited or not possible. Finally, insights into the underlying neurophysiological mechanisms are proposed.

High-Intensity Chronic Stroke Motor Imagery Neurofeedback Training at Home: Three Case Reports

Motor imagery (MI) with neurofeedback has been suggested as promising for motor recovery after stroke. Evidence suggests that regular training facilitates compensatory plasticity, but frequent training is difficult to integrate into everyday life. Using a wireless electroencephalogram (EEG) system, we implemented a frequent and efficient neurofeedback training at the patients' home. Aiming to overcome maladaptive changes in cortical lateralization patterns we presented a visual feedback, representing the degree of contralateral sensorimotor cortical activity and the degree of sensorimotor cortex lateralization. Three stroke patients practiced every other day, over a period of 4 weeks. Training-related changes were evaluated on behavioral, functional, and structural levels. All 3 patients indicated that they enjoyed the training and were highly motivated throughout the entire training regime. EEG activity induced by MI of the affected hand became more lateralized over the course of training in all three patients. The patient with a significant functional change also showed increased white matter integrity as revealed by diffusion tensor imaging, and a substantial clinical improvement of upper limb motor functions. Our study provides evidence that regular, home-based practice of MI neurofeedback has the potential to facilitate cortical reorganization and may also increase associated improvements of upper limb motor function in chronic stroke patients.

Modulating hemispheric lateralization by brain stimulation yields gain in mental and physical activity

Imagery plays an important role in our life. Motor imagery is the mental simulation of a motor act without overt motor output. Previous studies have documented the effect of motor imagery practice. However, its translational potential for patients as well as for athletes, musicians and other groups, depends largely on the transfer from mental practice to overt physical performance. We used bilateral transcranial direct current stimulation (tDCS) over sensorimotor areas to modulate neural lateralization patterns induced by unilateral mental motor imagery and the performance of a physical motor task. Twenty-six healthy older adults participated (mean age = 67.1 years) in a double-blind cross-over sham-controlled study. We found stimulation-related changes at the neural and behavioural level, which were polarity-dependent. Specifically, for the hand contralateral to the anode, electroencephalographic activity induced by motor imagery was more lateralized and motor performance improved. In contrast, for the hand contralateral to the cathode, hemispheric lateralization was reduced. The stimulation-related increase and decrease in neural lateralization were negatively related. Further, the degree of stimulation-related change in neural lateralization correlated with the stimulation-related change on behavioural level. These convergent neurophysiological and behavioural effects underline the potential of tDCS to improve mental and physical motor performance.

Motor Imagery Impairment in Postacute Stroke Patients

Not much is known about how well stroke patients are able to perform motor imagery (MI) and which MI abilities are preserved after stroke. We therefore applied three different MI tasks (one mental chronometry task, one mental rotation task, and one EEG-based neurofeedback task) to a sample of postacute stroke patients (n = 20) and age-matched healthy controls (n = 20) for addressing the following questions: First, which of the MI tasks indicate impairment in stroke patients and are impairments restricted to the paretic side? Second, is there a relationship between MI impairment and sensory loss or paresis severity? And third, do the results of the different MI tasks converge? Significant differences between the stroke and control groups were found in all three MI tasks. However, only the mental chronometry task and EEG analysis revealed paresis side-specific effects. Moreover, sensitivity loss contributed to a performance drop in the mental rotation task. The findings indicate that although MI abilities may be impaired after stroke, most patients retain their ability for MI EEG-based neurofeedback. Interestingly, performance in the different MI measures did not strongly correlate, neither in stroke patients nor in healthy controls. We conclude that one MI measure is not sufficient to fully assess an individual's MI abilities.