Letter to the Editor

Multimodal integration of fNIRS, fMRI and EEG neuroimaging

Functional near-infrared spectroscopy (fNIRS) has gained great interest as a noninvasive modality to study the changes in cerebral hemodynamics related to brain activity. The unique and beneficial characteristics of fNIRS allow ecologically effective investigations of all ages and conditions in more realistic and clinical environments. In this review, we provide a comprehensive description of fNIRS basics, analytical method developments and applications in stroke rehabilitation. We first review various new methods for the time-series processing and functional analysis of fNIRS data. Then, we review the fNIRS-based application in clinical research for stroke rehabilitation and highlight the exciting developments based on fNIRS. Finally, we discuss the possible technical limitations of the implementation of fNIRS and provide suggestions from different aspects for practical application.

Functional near-infrared spectroscopy (fNIRS) is increasingly used in the field of posture and gait to investigate patterns of cortical brain activation while people move freely. fNIRS methods, analysis and reporting of data vary greatly across studies which in turn can limit the replication of research, interpretation of findings and comparison across works.

Considering these issues, we propose a set of practical recommendations for the conduct and reporting of fNIRS studies in posture and gait, acknowledging specific challenges related to clinical groups with posture and gait disorders.

Our paper is organized around three main sections: 1) hardware set up and study protocols, 2) artefact removal and data processing and, 3) outcome measures, validity and reliability; it is supplemented with a detailed checklist.

This paper was written by a core group of members of the International Society for Posture and Gait Research and posture and gait researchers, all experienced in fNIRS research, with the intent of assisting the research community to lead innovative and impactful fNIRS studies in the field of posture and gait, whilst ensuring standardization of research.

Shutdown maintenance, i.e., turning off a facility for a short period for renewal or replacement operations is a highly stressful task. With the limited time and complex operation procedures, human stress is a leading risk. Especially shutdown maintenance workers often need to go through excessive and stressful on-site trainings to digest complex operation information in limited time. The challenge is that workers’ stress status and task performance are hard to predict, as most trainings are only assessed after the shutdown maintenance operation is finished. A proactive assessment or intervention is needed to evaluate workers’ stress status and task performance during the training to enable early warning and interventions. This study proposes a neurophysiological approach to assess workers’ stress status and task performance under different virtual training scenarios. A Virtual Reality (VR) system integrated with the eye-tracking function was developed to simulate the power plant shutdown maintenance operations of replacing a heat exchanger in both normal and stressful scenarios. Meanwhile, a portable neuroimaging device – Functional Near-Infrared Spectroscopy (fNIRS) was also utilized to collect user’s brain activities by measuring hemodynamic responses associated with neuron behavior. A human–subject experiment (n = 16) was conducted to evaluate participants’ neural activity patterns and physiological metrics (gaze movement) related to their stress status and final task performance. Each participant was required to review the operational instructions for a pipe maintenance task for a short period and then perform the task based on their memory in both normal and stressful scenarios. Our experiment results indicated that stressful training had a strong impact on participants’ neural connectivity patterns and final performance, suggesting the use of stressors during training to be an important and useful control factors. We further found significant correlations between gaze movement patterns in review phase and final task performance, and between the neural features and final task performance. In summary, we proposed a variety of supervised machine learning classification models that use the fNIRS data in the review session to estimate individual’s task performance. The classification models were validated with the k-fold (k = 10) cross-validation method. The Random Forest classification model achieved the best average classification accuracy (80.38%) in classifying participants’ task performance compared to other classification models. The contribution of our study is to help establish the knowledge and methodological basis for an early warning and estimating system of the final task performance based on the neurophysiological measures during the training for industrial operations. These findings are expected to provide more evidence about an early performance warning and prediction system based on a hybrid neurophysiological measure method, inspiring the design of a cognition-driven personalized training system for industrial workers.

Understanding the interactions between brain activity and behavior comprehensively in achieving optimal exercise performance in sports is still lacking. The existent research in this area has been limited by the constraints of sports environments and the robustness of the most suitable non-invasive functional neuroimaging methods (electroencephalography, EEG and functional near-infrared spectroscopy, fNIRS) to motion artifacts and noise. However, recent advances in brain mapping technology should improve the capabilities of the future brain imaging devices to assess and monitor the level of adaptive cognitive-motor performance during exercise in sports environments. The purpose of this position manuscript is to discuss the contributions and issues in behavioral neuroscience related to brain activity measured during exercise and in various sports. A first part aims to give an overview of EEG and fNIRS neuroimaging methods assessing electrophysiological activity and hemodynamic responses of the acute and chronic relation of physical exercise on the human brain. Then, methodological issues, such as the reliability of brain data during physical exertion, key limitations and possible prospects of fNIRS and EEG methods are provided. While the use of such methods in sports environments remains scarce and limited to controlled cycling task, new generation of wearable, whole-scalp EEG and fNIRS technologies could open up a range of new applications in sports sciences for providing neuroimaging-based biomarkers (hemodynamic and/or neural electrical signals) to various types of exercise and innovative training.

Previous findings indicate that facilitation of primary motor cortex (PMC) activity using trans-cranial direct current stimulation (tDCS) could improve resistance to physical fatigue. However, studies have failed to consistently replicate these results. Using non-focal-tDCS during a fatiguing task, recent work showed no enhancement of corticospinal excitability of the PMC despite a longer endurance time and suggested that contamination in other brain regions involved in motor command may have occurred. In accordance with recent evidence supporting the role of the prefrontal cortex (PFC) in exercise maintenance, this double-blind sham-controlled crossover study (N = 22) compared the effect of high definition (HD)-tDCS of the PMC or the PFC on endurance time of a sustained contraction task of the elbow flexor. Brain activity was monitored using near infrared spectroscopy (NIRS) to measure the neurovascular response elicited by HD-tDCS. Electromyography (EMG) and force obtained during maximal voluntary and evoked contractions were assessed before and after the contraction task to explore the effect of brain stimulation on peripheral and central fatigue. While the stimulation affected the brain response in the PFC during the contraction task, no effects of the stimulation were observed on endurance time or fatigue indices. These results are discussed in relation to the neurocognitive models of physical effort.

Recently, a lot of effort has been made to ground Rorschach interpretations to their evidence base. To date, however, no studies have yet described, via fMRI, what brain areas get involved when one takes the Rorschach. To fill this gap in the literature, we administered the ten-inkblot stimuli to 26 healthy volunteers during fMRI. Analysis of BOLD signals revealed that, compared to fixating a cross, looking at the Rorschach inkblots while thinking of what they might be associated with higher temporo-occipital and fronto-parietal activations, and with greater activity in some small, sub-cortical regions included in the limbic system. These findings are in line with the traditional conceptualization of the test, as they suggest that taking the Rorschach involves (a) high-level visual processing, (b) top-down as well as bottom-up attentional processes, and (c) perception and processing of emotions and emotional memories.