Discriminative analysis of schizophrenia and major depressive disorder using fNIRS.

BACKGROUND: Research into the shared and distinct brain dysfunctions in patients with schizophrenia (SCZ) and major depressive disorder (MDD) has been increasing. However, few studies have explored the application of functional near-infrared spectroscopy (fNIRS) in investigating brain dysfunction and enhancing diagnostic methodologies in these two conditions. METHODS: A general linear model was used for analysis of brain activation following task-state fNIRS from 131 patients with SCZ, 132 patients with MDD and 130 healthy controls (HCs). Subsequently, seventy-seven time-frequency analysis methods were used to construct new features of fNIRS, followed by the implementation of five machine learning algorithms to develop a differential diagnosis model for the three groups. This model was evaluated by comparing it to both a diagnostic model relying on traditional fNIRS features and assessments made by two psychiatrists. RESULTS: Brain activation analysis revealed significantly lower activation in Broca's area, the dorsolateral prefrontal cortex, and the middle temporal gyrus for both the SCZ and MDD groups compared to HCs. Additionally, the SCZ group exhibited notably lower activation in the superior temporal gyrus and the subcentral gyrus compared to the MDD group. When distinguishing among the three groups using independent validation datasets, the models utilizing new fNIRS features achieved an accuracy of 85.90 % (AUC = 0.95). In contrast, models based on traditional fNIRS features reached an accuracy of 52.56 % (AUC = 0.66). The accuracies of the two psychiatrists were 42.00 % (AUC = 0.60) and 38.00 % (AUC = 0.50), respectively. CONCLUSION: This investigation brings to light the shared and distinct neurobiological abnormalities present in SCZ and MDD, offering potential enhancements for extant diagnostic systems.

Lexical Morphology as a Source of Risk and Resilience for Learning to Read With Dyslexia: An fNIRS Investigation.

PURPOSE: We examined the neurocognitive bases of lexical morphology in children of varied reading abilities to understand the role of meaning-based skills in learning to read with dyslexia. METHOD: Children completed auditory morphological and phonological awareness tasks during functional near-infrared spectroscopy neuroimaging. We first examined the relation between lexical morphology and phonological processes in typically developing readers (Study 1, N = 66, Mage = 8.39), followed by a more focal inquiry into lexical morphology processes in dyslexia (Study 2, N = 50, Mage = 8.62). RESULTS: Typical readers exhibited stronger engagement of language neurocircuitry during the morphology task relative to the phonology task, suggesting that morphological analyses involve synthesizing multiple components of sublexical processing. This effect was stronger for more analytically complex derivational affixes (like + ly) than more semantically transparent free base morphemes (snow + man). In contrast, children with dyslexia exhibited stronger activation during the free base condition relative to derivational affix condition. Taken together, the findings suggest that although children with dyslexia may struggle with derivational morphology, they may also use free base morphemes' semantic information to boost word recognition. CONCLUSION: This study informs literacy theories by identifying an interaction between reading ability, word structure, and how the developing brain learns to recognize words in speech and print. SUPPLEMENTAL MATERIAL: https://doi.org/10.23641/asha.25944949.

Hyperscanning to explore social interaction among autistic minds.

Hyperscanning - the monitoring of brain activity of two or more people simultaneously - has emerged to be a popular tool for assessing neural features of social interaction. This perspective article focuses on hyperscanning studies that use functional near-infrared spectroscopy (fNIRS), a technique that is very conducive to studies requiring naturalistic paradigms. In particular, we are interested in neural features that are related to social interaction deficits among individuals with autism spectrum disorders (ASD). This population has received relatively little attention in research using neuroimaging hyperscanning techniques, compared to neurotypical individuals. The study is outlined as follows. First, we summarize the findings about brain-behavior connections related to autism from previously published fNIRS hyperscanning studies. Then, we propose a preliminary theoretical framework of inter-brain coherence (IBC) with testable hypotheses concerning this population. Finally, we provide two examples of areas of inquiry in which studies could be particularly relevant for social-emotional/behavioral development for autistic children, focusing on intergenerational relationships in family units and learning in classroom settings in mainstream schools.

Study of a PST-trained voice-enabled artificial intelligence counselor for adults with emotional distress (SPEAC-2): Design and methods.

BACKGROUND: Novel and scalable psychotherapies are urgently needed to address the depression and anxiety epidemic. Leveraging artificial intelligence (AI), a voice-based virtual coach named Lumen was developed to deliver problem solving treatment (PST). The first pilot trial showed promising changes in cognitive control measured by functional neuroimaging and improvements in depression and anxiety symptoms. METHODS: To further validate Lumen in a 3-arm randomized clinical trial, 200 participants with mild-to-moderate depression and/or anxiety will be randomly assigned in a 2:1:1 ratio to receive Lumen-coached PST, human-coached PST as active treatment comparison, or a waitlist control condition where participants can receive Lumen after the trial period. Participants will be assessed at baseline and 18 weeks. The primary aim is to confirm neural target engagement by testing whether compared with waitlist controls, Lumen participants will show significantly greater improvements from baseline to 18 weeks in the a priori neural target for cognitive control, right dorsal lateral prefrontal cortex engaged by the go/nogo task (primary superiority hypothesis). A secondary hypothesis will test whether compared with human-coached PST participants, Lumen participants will show equivalent improvements (i.e., noninferiority) in the same neural target from baseline to 18 weeks. The second aim is to examine (1) treatment effects on depression and anxiety symptoms, psychosocial functioning, and quality of life outcomes, and (2) relationships of neural target engagement to these patient-reported outcomes. CONCLUSIONS: This study offers potential to improve the reach and impact of psychotherapy, mitigating access, cost, and stigma barriers for people with depression and/or anxiety. CLINICALTRIALS: gov #: NCT05603923.

Overlapping neural correlates underpin theory of mind and semantic cognition: Evidence from a meta-analysis of 344 functional neuroimaging studies.

Key unanswered questions for cognitive neuroscience include whether social cognition is underpinned by specialised brain regions and to what extent it simultaneously depends on more domain-general systems. Until we glean a better understanding of the full set of contributions made by various systems, theories of social cognition will remain fundamentally limited. In the present study, we evaluate a recent proposal that semantic cognition plays a crucial role in supporting social cognition. While previous brain-based investigations have focused on dissociating these two systems, our primary aim was to assess the degree to which the neural correlates are overlapping, particularly within two key regions, the anterior temporal lobe (ATL) and the temporoparietal junction (TPJ). We focus on activation associated with theory of mind (ToM) and adopt a meta-analytic activation likelihood approach to synthesise a large set of functional neuroimaging studies and compare their results with studies of semantic cognition. As a key consideration, we sought to account for methodological differences across the two sets of studies, including the fact that ToM studies tend to use nonverbal stimuli while the semantics literature is dominated by language-based tasks. Overall, we observed consistent overlap between the two sets of brain regions, especially in the ATL and TPJ. This supports the claim that tasks involving ToM draw upon more general semantic retrieval processes. We also identified activation specific to ToM in the right TPJ, bilateral anterior mPFC, and right precuneus. This is consistent with the view that, nested amongst more domain-general systems, there is specialised circuitry that is tuned to social processes.

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.

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.

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.

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.

Correlation between subcutaneous adipose tissue of the head and body mass index in children and young adults aged 8-19 years: implications for functional neuroimaging.

High body mass index (BMI) is presumed to signify high amounts of fat (subcutaneous adipose tissue) distributed across the body. High amounts of fat co-occurring with increased BMI has been cited as a potential neuroimaging barrier. Presence of increased fat may result in high electrical impedance and increased light diffusion-resulting in low signal to noise ratios during electroencepholography (EEG), functional near-infrared spectroscopy (fNIRS), and transcranial direct current stimulation (tDCS) measurements. Examining if subcutaneous fat in the head increases with respect to total body fat percentage and BMI in school-aged children and adolescents is an essential next step in developing possible mathematical corrections for neuroimaging modalities. We hypothesized that percentage of subcutaneous adipose tissue in the head region would increase with respect to both total body fat percentage and BMI. Increased subcutaneous head fat percentage was associated with a positive linear relationship with BMI and a quadratic relationship with total body fat. The data indicate that participant age, sex, and adiposity should be considered in the development of model corrections for neuroimaging signal processing in school-aged children and adolescents. Strength of regression coefficients in our models differed from those in adults, indicating that age-specific models should be utilized.

The neuroanatomy of developmental language disorder: a systematic review and meta-analysis.

Developmental language disorder (DLD) is a common neurodevelopmental disorder with adverse impacts that continue into adulthood. However, its neural bases remain unclear. Here we address this gap by systematically identifying and quantitatively synthesizing neuroanatomical studies of DLD using co-localization likelihood estimation, a recently developed neuroanatomical meta-analytic technique. Analyses of structural brain data (22 peer-reviewed papers, 577 participants) revealed highly consistent anomalies only in the basal ganglia (100% of participant groups in which this structure was examined, weighted by group sample sizes; 99.8% permutation-based likelihood the anomaly clustering was not due to chance). These anomalies were localized specifically to the anterior neostriatum (again 100% weighted proportion and 99.8% likelihood). As expected given the task dependence of activation, functional neuroimaging data (11 peer-reviewed papers, 414 participants) yielded less consistency, though anomalies again occurred primarily in the basal ganglia (79.0% and 95.1%). Multiple sensitivity analyses indicated that the patterns were robust. The meta-analyses elucidate the neuroanatomical signature of DLD, and implicate the basal ganglia in particular. The findings support the procedural circuit deficit hypothesis of DLD, have basic research and translational implications for the disorder, and advance our understanding of the neuroanatomy of language.

Optimized microburst VNS elicits fMRI responses beyond thalamic-specific response from standard VNS.

OBJECTIVE: In parallel to standard vagus nerve stimulation (VNS), microburst stimulation delivery has been developed. We evaluated the fMRI-related signal changes associated with standard and optimized microburst stimulation in a proof-of-concept study (NCT03446664). METHODS: Twenty-nine drug-resistant epilepsy patients were prospectively implanted with VNS. Three 3T fMRI scans were collected 2 weeks postimplantation. The maximum tolerated VNS intensity was determined prior to each scan starting at 0.125 mA with 0.125 mA increments. FMRI scans were block-design with alternating 30 sec stimulation [ON] and 30 sec no stimulation [OFF]: Scan 1 utilized standard VNS and Scan 3 optimized microburst parameters to determine target settings. Semi-automated on-site fMRI data processing utilized ON-OFF block modeling to determine VNS-related fMRI activation per stimulation setting. Anatomical thalamic mask was used to derive highest mean thalamic t-value for determination of microburst stimulation parameters. Paired t-tests corrected at P < 0.05 examined differences in fMRI responses to each stimulation type. RESULTS: Standard and microburst stimulation intensities at Scans 1 and 3 were similar (P = 0.16). Thalamic fMRI responses were obtained in 28 participants (19 with focal; 9 with generalized seizures). Group activation maps showed standard VNS elicited thalamic activation while optimized microburst VNS showed widespread activation patterns including thalamus. Comparison of stimulation types revealed significantly greater cerebellar, midbrain, and parietal fMRI signal changes in microburst compared to standard VNS. These differences were not associated with seizure responses. INTERPRETATION: While standard and optimized microburst VNS elicited thalamic activation, microburst also engaged other brain regions. Relationship between these fMRI activation patterns and clinical response warrants further investigation. CLINICAL TRIAL REGISTRATION: The study was registered with clinicaltrials.gov (NCT03446664).

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.

Combining OPM and lesion mapping data for epilepsy surgery planning: a simulation study.

When planning for epilepsy surgery, multiple potential sites for resection may be identified through anatomical imaging. Magnetoencephalography (MEG) using optically pumped sensors (OP-MEG) is a non-invasive functional neuroimaging technique which could be used to help identify the epileptogenic zone from these candidate regions. Here we test the utility of a-priori information from anatomical imaging for differentiating potential lesion sites with OP-MEG. We investigate a number of scenarios: whether to use rigid or flexible sensor arrays, with or without a-priori source information and with or without source modelling errors. We simulated OP-MEG recordings for 1309 potential lesion sites identified from anatomical images in the Multi-centre Epilepsy Lesion Detection (MELD) project. To localise the simulated data, we used three source inversion schemes: unconstrained, prior source locations at centre of the candidate sites, and prior source locations within a volume around the lesion location. We found that prior knowledge of the candidate lesion zones made the inversion robust to errors in sensor gain, orientation and even location. When the reconstruction was too highly restricted and the source assumptions were inaccurate, the utility of this a-priori information was undermined. Overall, we found that constraining the reconstruction to the region including and around the participant's potential lesion sites provided the best compromise of robustness against modelling or measurement error.

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.

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.

Whole-brain structural and functional neuroimaging of individuals who attempted suicide and people who did not: A systematic review and exploratory coordinate-based meta-analysis.

Suicide is the cause of death of approximately 800,000 people a year. Despite the relevance of this behaviour, risk assessment tools rely on clinician experience and subjective ratings. Given that previous suicide attempts are the single strongest predictors of future attempts, we designed a systematic review and coordinate-based meta-analysis to demonstrate whether neuroimaging features can help distinguish individuals who attempted suicide from subjects who did not. Out of 5,659 publications from PubMed, Scopus, and Web of Science, we summarised 102 experiments and meta-analysed 23 of them. A cluster in the right superior temporal gyrus, a region implicated in emotional processing, might be functionally hyperactive in individuals who attempted suicide. No statistically significant differences in brain morphometry were evidenced. Furthermore, we used JuSpace to show that this cluster is enriched in 5-HT1A heteroreceptors in the general population. This exploratory meta-analysis provides a putative neural substrate linked to previous suicide attempts. Heterogeneity in the analytical techniques and weak or absent power analysis of the studies included in this review currently limit the applicability of the findings, the replication of which should be prioritised.

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.