Functional neuroimaging as a catalyst for integrated neuroscience.

Functional magnetic resonance imaging (fMRI) enables non-invasive access to the awake, behaving human brain. By tracking whole-brain signals across a diverse range of cognitive and behavioural states or mapping differences associated with specific traits or clinical conditions, fMRI has advanced our understanding of brain function and its links to both normal and atypical behaviour. Despite this headway, progress in human cognitive neuroscience that uses fMRI has been relatively isolated from rapid advances in other subdomains of neuroscience, which themselves are also somewhat siloed from one another. In this Perspective, we argue that fMRI is well-placed to integrate the diverse subfields of systems, cognitive, computational and clinical neuroscience. We first summarize the strengths and weaknesses of fMRI as an imaging tool, then highlight examples of studies that have successfully used fMRI in each subdomain of neuroscience. We then provide a roadmap for the future advances that will be needed to realize this integrative vision. In this way, we hope to demonstrate how fMRI can help usher in a new era of interdisciplinary coherence in neuroscience.

Spontaneous eye movements reflect the representational geometries of conceptual spaces.

Functional neuroimaging studies indicate that the human brain can represent concepts and their relational structure in memory using coding schemes typical of spatial navigation. However, whether we can read out the internal representational geometries of conceptual spaces solely from human behavior remains unclear. Here, we report that the relational structure between concepts in memory might be reflected in spontaneous eye movements during verbal fluency tasks: When we asked participants to randomly generate numbers, their eye movements correlated with distances along the left-to-right one-dimensional geometry of the number space (mental number line), while they scaled with distance along the ring-like two-dimensional geometry of the color space (color wheel) when they randomly generated color names. Moreover, when participants randomly produced animal names, eye movements correlated with low-dimensional similarity in word frequencies. These results suggest that the representational geometries used to internally organize conceptual spaces might be read out from gaze behavior.

Variability in the analysis of a single neuroimaging dataset by many teams.

Data analysis workflows in many scientific domains have become increasingly complex and flexible. Here we assess the effect of this flexibility on the results of functional magnetic resonance imaging by asking 70 independent teams to analyse the same dataset, testing the same 9 ex-ante hypotheses1. The flexibility of analytical approaches is exemplified by the fact that no two teams chose identical workflows to analyse the data. This flexibility resulted in sizeable variation in the results of hypothesis tests, even for teams whose statistical maps were highly correlated at intermediate stages of the analysis pipeline. Variation in reported results was related to several aspects of analysis methodology. Notably, a meta-analytical approach that aggregated information across teams yielded a significant consensus in activated regions. Furthermore, prediction markets of researchers in the field revealed an overestimation of the likelihood of significant findings, even by researchers with direct knowledge of the dataset2-5. Our findings show that analytical flexibility can have substantial effects on scientific conclusions, and identify factors that may be related to variability in the analysis of functional magnetic resonance imaging. The results emphasize the importance of validating and sharing complex analysis workflows, and demonstrate the need for performing and reporting multiple analyses of the same data. Potential approaches that could be used to mitigate issues related to analytical variability are discussed.

Fractionating difficulty during sentence comprehension using functional neuroimaging.

Sentence comprehension is highly practiced and largely automatic, but this belies the complexity of the underlying processes. We used functional neuroimaging to investigate garden-path sentences that cause difficulty during comprehension, in order to unpack the different processes used to support sentence interpretation. By investigating garden-path and other types of sentences within the same individuals, we functionally profiled different regions within the temporal and frontal cortices in the left hemisphere. The results revealed that different aspects of comprehension difficulty are handled by left posterior temporal, left anterior temporal, ventral left frontal, and dorsal left frontal cortices. The functional profiles of these regions likely lie along a spectrum of specificity to generality, including language-specific processing of linguistic representations, more general conflict resolution processes operating over linguistic representations, and processes for handling difficulty in general. These findings suggest that difficulty is not unitary and that there is a role for a variety of linguistic and non-linguistic processes in supporting comprehension.

Boosting interpersonal emotion regulation through facial imitation: functional neuroimaging foundations.

Empathic function, which is primarily manifested by facial imitation, is believed to play a pivotal role in interpersonal emotion regulation for mood reinstatement. To explore this association and its neural substrates, we performed a questionnaire survey (study l) to identify the relationship between empathy and interpersonal emotion regulation; and a task-mode fMRI study (study 2) to explore how facial imitation, as a fundamental component of empathic processes, promotes the interpersonal emotion regulation effect. Study 1 showed that affective empathy was positively correlated with interpersonal emotion regulation. Study 2 showed smaller negative emotions in facial imitation interpersonal emotion regulation (subjects imitated experimenter's smile while followed the interpersonal emotion regulation guidance) than in normal interpersonal emotion regulation (subjects followed the interpersonal emotion regulation guidance) and Watch conditions. Mirror neural system (e.g. inferior frontal gyrus and inferior parietal lobe) and empathy network exhibited greater activations in facial imitation interpersonal emotion regulation compared with normal interpersonal emotion regulation condition. Moreover, facial imitation interpersonal emotion regulation compared with normal interpersonal emotion regulation exhibited increased functional coupling from mirror neural system to empathic and affective networks during interpersonal emotion regulation. Furthermore, the connectivity of the right orbital inferior frontal gyrus-rolandic operculum lobe mediated the association between the accuracy of facial imitation and the interpersonal emotion regulation effect. These results show that the interpersonal emotion regulation effect can be enhanced by the target's facial imitation through increased functional coupling from mirror neural system to empathic and affective neural networks.

Disentangling the effects of near-infrared light stimulation and exercise on cognitive function in fNIRS studies.

Functional near-infrared spectroscopy (fNIRS) studies often aim to measure changes in the brain's hemodynamic response in relation to a specific intervention. We recently showed how a fNIRS device could induce photobiomodulatory effects on cognition by using its near-infrared (NIR) light. However, so far, fNIRS research has overlooked the stimulatory potential intrinsic to this technique. The work by Kuwamizu et al. (2023) on pupil dynamics during exercise is no exception. Here, we suggest a fix to their experimental design, which could be taken into account in other fNIRS studies, to guarantee an adequate level of control for possible unconsidered photobiomodulatory effects.

Analyzing fractal dimension in electroconvulsive therapy: Unraveling complexity in structural and functional neuroimaging.

BACKGROUND: Numerous studies show that electroconvulsive therapy (ECT) induces hippocampal neuroplasticity, but findings are inconsistent regarding its clinical relevance. This study aims to investigate ECT-induced plasticity of anterior and posterior hippocampi using mathematical complexity measures in neuroimaging, namely Higuchi's fractal dimension (HFD) for fMRI time series and the fractal dimension of cortical morphology (FD-CM). Furthermore, we explore the potential of these complexity measures to predict ECT treatment response. METHODS: Twenty patients with a current depressive episode (16 with major depressive disorder and 4 with bipolar disorder) underwent MRI-scans before and after an ECT-series. Twenty healthy controls matched for age and sex were also scanned twice for comparison purposes. Resting-state fMRI data were processed, and HFD was computed for anterior and posterior hippocampi. Group-by-time effects for HFD in anterior and posterior hippocampi were calculated and correlations between HFD changes and improvement in depression severity were examined. For FD-CM analyses, we preprocessed structural MRI with CAT12's surface-based methods. We explored group-by-time effects for FD-CM and the predictive value of baseline HFD and FD-CM for treatment outcome. RESULTS: Patients exhibited a significant increase in bilateral hippocampal HFD from baseline to follow-up scans. Right anterior hippocampal HFD increase was associated with reductions in depression severity. We found no group differences and group-by-time effects in FD-CM. After applying a whole-brain regression analysis, we found that baseline FD-CM in the left temporal pole predicted reduction of overall depression severity after ECT. Baseline hippocampal HFD did not predict treatment outcome. CONCLUSION: This study suggests that HFD and FD-CM are promising imaging markers to investigate ECT-induced neuroplasticity associated with treatment response.

Graph neural network and machine learning analysis of functional neuroimaging for understanding schizophrenia.

BACKGROUND: Graph representational learning can detect topological patterns by leveraging both the network structure as well as nodal features. The basis of our exploration involves the application of graph neural network architectures and machine learning to resting-state functional Magnetic Resonance Imaging (rs-fMRI) data for the purpose of detecting schizophrenia. Our study uses single-site data to avoid the shortcomings in generalizability of neuroimaging data obtained from multiple sites. RESULTS: The performance of our graph neural network models is on par with that of our machine learning models, each of which is trained using 69 graph-theoretical measures computed from functional correlations between various regions of interest (ROI) in a brain graph. Our deep graph convolutional neural network (DGCNN) demonstrates a promising average accuracy score of 0.82 and a sensitivity score of 0.84. CONCLUSIONS: This study provides insights into the role of advanced graph theoretical methods and machine learning on fMRI data to detect schizophrenia by harnessing changes in brain functional connectivity. The results of this study demonstrate the capabilities of using both traditional ML techniques as well as graph neural network-based methods to detect schizophrenia using features extracted from fMRI data. The study also proposes two methods to obtain potential biomarkers for the disease, many of which are corroborated by research in this area and can further help in the understanding of schizophrenia as a mental disorder.

Functional Neuroimaging as an Assessment Tool in Critically Ill Patients.

OBJECTIVE: Little is known about residual cognitive function in the earliest stages of serious brain injury. Functional neuroimaging has yielded valuable diagnostic and prognostic information in chronic disorders of consciousness, such as the vegetative state (also termed unresponsive wakefulness syndrome). The objective of the current study was to determine if functional neuroimaging could be efficacious in the assessment of cognitive function in acute disorders of consciousness, such as coma, where decisions about the withdrawal of life-sustaining therapies are often made. METHODS: A hierarchical functional magnetic resonance imaging (fMRI) approach assessed sound perception, speech perception, language comprehension, and covert command following in 17 critically ill patients admitted to the intensive care unit (ICU). RESULTS: Preserved auditory function was observed in 15 patients (88%), whereas 5 (29%) also had preserved higher-order language comprehension. Notably, one patient could willfully modulate his brain activity when instructed to do so, suggesting a level of covert conscious awareness that was entirely inconsistent with his clinical diagnosis at the time of the scan. Across patients, a positive relationship was also observed between fMRI responsivity and the level of functional recovery, such that patients with the greatest functional recovery had neural responses most similar to those observed in healthy control participants. INTERPRETATION: These results suggest that fMRI may provide important diagnostic and prognostic information beyond standard clinical assessment in acutely unresponsive patients, which may aid discussions surrounding the continuation or removal of life-sustaining therapies during the early post-injury period. ANN NEUROL 2023;93:131-141.

Brexanolone Treatment in a Real-World Patient Population: A Case Series and Pilot Feasibility Study of Precision Neuroimaging.

PURPOSE/BACKGROUND: Brexanolone is approved for postpartum depression (PPD) by the United States Food and Drug Administration. Brexanolone has outperformed placebo in clinical trials, but less is known about the efficacy in real-world patients with complex social and medical histories. Furthermore, the impact of brexanolone on large-scale brain systems such as changes in functional connectivity (FC) is unknown. METHODS/PROCEDURES: We tracked changes in depressive symptoms across a diverse group of patients who received brexanolone at a large medical center. Edinburgh Postnatal Depression Scale (EPDS) scores were collected through chart review for 17 patients immediately prior to infusion through approximately 1 year postinfusion. In 2 participants, we performed precision functional neuroimaging (pfMRI), including before and after treatment in 1 patient. pfMRI collects many hours of data in individuals for precision medicine applications and was performed to assess the feasibility of investigating changes in FC with brexanolone. FINDINGS/RESULTS: The mean EPDS score immediately postinfusion was significantly lower than the mean preinfusion score (mean change [95% CI]: 10.76 [7.11-14.40], t (15) = 6.29, P < 0.0001). The mean EPDS score stayed significantly lower at 1 week (mean difference [95% CI]: 9.50 [5.23-13.76], t (11) = 4.90, P = 0.0005) and 3 months (mean difference [95% CI]: 9.99 [4.71-15.27], t (6) = 4.63, P = 0.0036) postinfusion. Widespread changes in FC followed infusion, which correlated with EPDS scores. IMPLICATIONS/CONCLUSIONS: Brexanolone is a successful treatment for PPD in the clinical setting. In conjunction with routine clinical care, brexanolone was linked to a reduction in symptoms lasting at least 3 months. pfMRI is feasible in postpartum patients receiving brexanolone and has the potential to elucidate individual-specific mechanisms of action.

Prefrontal, parietal, and limbic condition-dependent differences in bipolar disorder: a large-scale meta-analysis of functional neuroimaging studies.

BACKGROUND: Over the past few decades, neuroimaging research in Bipolar Disorder (BD) has identified neural differences underlying cognitive and emotional processing. However, substantial clinical and methodological heterogeneity present across neuroimaging experiments potentially hinders the identification of consistent neural biomarkers of BD. This meta-analysis aims to comprehensively reassess brain activation and connectivity in BD in order to identify replicable differences that converge across and within resting-state, cognitive, and emotional neuroimaging experiments. METHODS: Neuroimaging experiments (using fMRI, PET, or arterial spin labeling) reporting whole-brain results in adults with BD and controls published from December 1999-June 18, 2019 were identified via PubMed search. Coordinates showing significant activation and/or connectivity differences between BD participants and controls during resting-state, emotional, or cognitive tasks were extracted. Four parallel, independent meta-analyses were calculated using the revised activation likelihood estimation algorithm: all experiment types, all resting-state experiments, all cognitive experiments, and all emotional experiments. To confirm reliability of identified clusters, two different meta-analytic significance tests were employed. RESULTS: 205 published studies yielding 506 individual neuroimaging experiments (150 resting-state, 134 cognitive, 222 emotional) comprising 5745 BD and 8023 control participants were included. Five regions survived both significance tests. Individuals with BD showed functional differences in the right posterior cingulate cortex during resting-state experiments, the left amygdala during emotional experiments, including those using a mixed (positive/negative) valence manipulation, and the left superior and right inferior parietal lobules during cognitive experiments, while hyperactivating the left medial orbitofrontal cortex during cognitive experiments. Across all experiments, there was convergence in the right caudate extending to the ventral striatum, surviving only one significance test. CONCLUSIONS: Our findings indicate reproducible localization of prefrontal, parietal, and limbic differences distinguishing BD from control participants that are condition-dependent, despite heterogeneity, and point towards a framework for identifying reproducible differences in BD that may guide diagnosis and treatment.

Magnetoencephalography for Epilepsy Presurgical Evaluation.

PURPOSE OF THE REVIEW: Magnetoencephalography (MEG) is a functional neuroimaging technique that records neurophysiology data with millisecond temporal resolution and localizes it with subcentimeter accuracy. Its capability to provide high resolution in both of these domains makes it a powerful tool both in basic neuroscience as well as clinical applications. In neurology, it has proven useful in its ability to record and localize epileptiform activity. Epilepsy workup typically begins with scalp electroencephalography (EEG), but in many situations, EEG-based localization of the epileptogenic zone is inadequate. The complementary sensitivity of MEG can be crucial in such cases, and MEG has been adopted at many centers as an important resource in building a surgical hypothesis. In this paper, we review recent work evaluating the extent of MEG influence of presurgical evaluations, novel analyses of MEG data employed in surgical workup, and new MEG instrumentation that will likely affect the field of clinical MEG. RECENT FINDINGS: MEG consistently contributes to presurgical evaluation and these contributions often change the plan for epilepsy surgery. Extensive work has been done to develop new analytic methods for localizing the source of epileptiform activity with MEG. Systems using optically pumped magnetometry (OPM) have been successfully deployed to record and localize epileptiform activity. MEG remains an important noninvasive tool for epilepsy presurgical evaluation. Continued improvements in analytic methodology will likely increase the diagnostic yield of the test. Novel instrumentation with OPM may contribute to this as well, and may increase accessibility of MEG by decreasing cost.

Functional neuroimaging and behavioral correlates of multisite tDCS as an add-on to language training in a person with post-stroke non-fluent aphasia: a year-long case study.

Mary, who experienced non-fluent aphasia as a result of an ischemic stroke, received 10 years of personalized language training (LT), resulting in transient enhancements in speech and comprehension. To enhance these effects, multisite transcranial Direct Current Stimulation (tDCS) was added to her LT regimen for 15 sessions. Assessment using the Reliable Change Index showed that this combination improved her left inferior frontal connectivity and speech production for two months and significantly improved comprehension after one month. The results indicate that using multisite transcranial direct current stimulation (tDCS) can improve the effectiveness of language therapy (LT) for individuals with non-fluent aphasia.

Using in silico perturbational approach to identify critical areas in schizophrenia.

Schizophrenia is a debilitating neuropsychiatric disorder whose underlying correlates remain unclear despite decades of neuroimaging investigation. One contentious topic concerns the role of global signal (GS) fluctuations and how they affect more focal functional changes. Moreover, it has been difficult to pinpoint causal mechanisms of circuit disruption. Here, we analyzed resting-state fMRI data from 47 schizophrenia patients and 118 age-matched healthy controls and used dynamical analyses to investigate how global fluctuations and other functional metastable states are affected by this disorder. We found that brain dynamics in the schizophrenia group were characterized by an increased probability of globally coherent states and reduced recurrence of a substate dominated by coupled activity in the default mode and limbic networks. We then used the in silico perturbation of a whole-brain model to identify critical areas involved in the disease. Perturbing a set of temporo-parietal sensory and associative areas in a model of the healthy brain reproduced global pathological dynamics. Healthy brain dynamics were instead restored by perturbing a set of medial fronto-temporal and cingulate regions in the model of pathology. These results highlight the relevance of GS alterations in schizophrenia and identify a set of vulnerable areas involved in determining a shift in brain state.

Functional reconfiguration of task-active frontoparietal control network facilitates abstract reasoning.

While the brain's functional network architecture is largely conserved between resting and task states, small but significant changes in functional connectivity support complex cognition. In this study, we used a modified Raven's Progressive Matrices Task to examine symbolic and perceptual reasoning in human participants undergoing fMRI scanning. Previously, studies have focused predominantly on discrete symbolic versions of matrix reasoning, even though the first few trials of the Raven's Advanced Progressive Matrices task consist of continuous perceptual stimuli. Our analysis examined the activation patterns and functional reconfiguration of brain networks associated with resting state and both symbolic and perceptual reasoning. We found that frontoparietal networks, including the cognitive control and dorsal attention networks, were significantly activated during abstract reasoning. We determined that these same task-active regions exhibited flexibly-reconfigured functional connectivity when transitioning from resting state to the abstract reasoning task. Conversely, we showed that a stable network core of regions in default and somatomotor networks was maintained across both resting and task states. We propose that these regionally-specific changes in the functional connectivity of frontoparietal networks puts the brain in a "task-ready" state, facilitating efficient task-based activation.

Prenatal and childhood exposure to organophosphate pesticides and functional brain imaging in young adults.

BACKGROUND: Early life exposure to organophosphate (OP) pesticides has been linked with poorer neurodevelopment from infancy to adolescence. In our Center for the Health Assessment of Mothers and Children of Salinas (CHAMACOS) birth cohort, we previously reported that residential proximity to OP use during pregnancy was associated with altered cortical activation using functional near infrared spectroscopy (fNIRS) in a small subset (n = 95) of participants at age 16 years. METHODS: We administered fNIRS to 291 CHAMACOS young adults at the 18-year visit. Using covariate-adjusted regression models, we estimated associations of prenatal and childhood urinary dialkylphosphates (DAPs), non-specific OP metabolites, with cortical activation in the frontal, temporal, and parietal regions of the brain during tasks of executive function and semantic language. RESULTS: There were some suggestive associations for prenatal DAPs with altered activation patterns in both the inferior frontal and inferior parietal lobes of the left hemisphere during a task of cognitive flexibility (ß per ten-fold increase in DAPs = 3.37; 95% CI: -0.02, 6.77 and ß = 3.43; 95% CI: 0.64, 6.22, respectively) and the inferior and superior frontal pole/dorsolateral prefrontal cortex of the right hemisphere during the letter retrieval working memory task (ß = -3.10; 95% CI: -6.43, 0.22 and ß = -3.67; 95% CI: -7.94, 0.59, respectively). We did not observe alterations in cortical activation with prenatal DAPs during a semantic language task or with childhood DAPs during any task. DISCUSSION: We observed associations of prenatal OP concentrations with mild alterations in cortical activation during tasks of executive function. Associations with childhood exposure were null. This is reasonably consistent with studies of prenatal OPs and neuropsychological measures of attention and executive function found in CHAMACOS and other birth cohorts.

Effects of functional polymorphisms of opioid receptor mu 1 and catechol-O-methyltransferase on the neural processing of pain.

AIM: Pain is reconstructed by brain activities and its subjectivity comes from an interplay of multiple factors. The current study aims to understand the contribution of genetic factors to the neural processing of pain. Focusing on the single-nucleotide polymorphism (SNP) of opioid receptor mu 1 (OPRM1) A118G (rs1799971) and catechol-O-methyltransferase (COMT) val158met (rs4680), we investigated how the two pain genes affect pain processing. METHOD: We integrated a genetic approach with functional neuroimaging. We extracted genomic DNA information from saliva samples to genotype the SNP of OPRM1 and COMT. We used a percept-related model, in which two different levels of perceived pain intensities ("low pain: mildly painful" vs "high pain: severely painful") were employed as experimental stimuli. RESULTS: Low pain involves a broader network relative to high pain. The distinct effects of pain genes were observed depending on the perceived pain intensity. The effects of low pain were found in supramarginal gyrus, angular gyrus, and anterior cingulate cortex (ACC) for OPRM1 and in middle temporal gyrus for COMT. For high pain, OPRM1 affected the insula and cerebellum, while COMT affected the middle occipital gyrus and ACC. CONCLUSION: OPRM1 primarily affects sensory and cognitive components of pain processing, while COMT mainly influences emotional aspects of pain processing. The interaction of the two pain genes was associated with neural patterns coding for high pain and neural activation in the ACC in response to pain. The proteins encoded by the OPRM1 and COMT may contribute to the firing of pain-related neurons in the human ACC, a critical center for subjective pain experience.

Differences in Functional Activity and Connectivity in the Right Frontoparietal Network between Nurses Working Long-Term Shifts and Fixed Day Shifts.

OBJECTIVES: To investigate the differences in functional brain activity and connectivity between nurses working long-term shifts and fixed day shift and explore their correlations with work-related psychological conditions. METHODS: Thirty-five nurses working long-term shifts and 35 nurses working fixed day shifts were recruited. After assessing work-related psychological conditions, such as burnout and perceived stress of these two groups of nurses, amplitude of low-frequency fluctuations (ALFF) and functional connectivity (FC) analyses were performed to investigate the between-group differences in brain functional activity and connectivity. Furthermore, correlation analysis between the ALFF/FC metrics and psychological conditions was conducted. RESULTS: Compared with nurses working fixed day shifts, nurses working long-term shifts showed higher levels of burnout, perceived stress, and depression scores; lower z-transformed ALFF (zALFF) values in the right dorsolateral prefrontal cortex (dlPFC), right superior parietal lobule (SPL), and right anterior cingulate cortex (ACC); and higher zALFF values in the right middle temporal gyrus (voxel-level p < 0.001, cluster-level p < 0.05, gaussian random field (GRF) correction), as well as lower FC values in the right dlPFC-right SPL and right dlPFC-right ACC (p < 0.05, false discovery rate (FDR) corrected). Moreover, the FC values in the right dlPFC-right SPL were negatively correlated with the perceived stress score in nurses working long-term shifts (p < 0.05, FDR corrected). CONCLUSIONS: This study demonstrated that nurses working long-term shifts had lower functional activity and weaker functional connectivity in the right frontoparietal network, which mainly includes the right dlPFC and right SPL, than those working on regular day shift. The current findings provide new insights into the impacts of long-term shift work on nurses' mental health from a functional neuroimaging perspective.

A review of combined functional neuroimaging and motion capture for motor rehabilitation.

BACKGROUND: Technological advancements in functional neuroimaging and motion capture have led to the development of novel methods that facilitate the diagnosis and rehabilitation of motor deficits. These advancements allow for the synchronous acquisition and analysis of complex signal streams of neurophysiological data (e.g., EEG, fNIRS) and behavioral data (e.g., motion capture). The fusion of those data streams has the potential to provide new insights into cortical mechanisms during movement, guide the development of rehabilitation practices, and become a tool for assessment and therapy in neurorehabilitation. RESEARCH OBJECTIVE: This paper aims to review the existing literature on the combined use of motion capture and functional neuroimaging in motor rehabilitation. The objective is to understand the diversity and maturity of technological solutions employed and explore the clinical advantages of this multimodal approach. METHODS: This paper reviews literature related to the combined use of functional neuroimaging and motion capture for motor rehabilitation following the PRISMA guidelines. Besides study and participant characteristics, technological aspects of the used systems, signal processing methods, and the nature of multimodal feature synchronization and fusion were extracted. RESULTS: Out of 908 publications, 19 were included in the final review. Basic or translation studies were mainly represented and based predominantly on healthy participants or stroke patients. EEG and mechanical motion capture technologies were most used for biomechanical data acquisition, and their subsequent processing is based mainly on traditional methods. The system synchronization techniques at large were underreported. The fusion of multimodal features mainly supported the identification of movement-related cortical activity, and statistical methods were occasionally employed to examine cortico-kinematic relationships. CONCLUSION: The fusion of motion capture and functional neuroimaging might offer advantages for motor rehabilitation in the future. Besides facilitating the assessment of cognitive processes in real-world settings, it could also improve rehabilitative devices' usability in clinical environments. Further, by better understanding cortico-peripheral coupling, new neuro-rehabilitation methods can be developed, such as personalized proprioceptive training. However, further research is needed to advance our knowledge of cortical-peripheral coupling, evaluate the validity and reliability of multimodal parameters, and enhance user-friendly technologies for clinical adaptation.

From functional neuroimaging to neurostimulation: fNIRS devices as cognitive enhancers.

Functional near-infrared spectroscopy (fNIRS) relies on near-infrared (NIR) light for changes in tissue oxygenation. For decades, this technique has been used in neuroscience to measure cortical activity. However, recent research suggests that NIR light directed to neural populations can modulate their activity through "photobiomodulation" (PBM). Yet, fNIRS is being used exclusively as a measurement tool. By adopting cognitive tests sensitive to prefrontal functioning, we show that a 'classical' fNIRS device, placed in correspondence of the prefrontal cortices of healthy participants, induces faster RTs and better accuracy in some of the indexes considered. A well-matched control group, wearing the same but inactive device, did not show any improvement. Hence, our findings indicate that the 'standard' use of fNIRS devices generates PBM impacting cognition. The neuromodulatory power intrinsic in that technique has been so far completely overlooked, and future studies will need to take this into account.