fNIRS System Research Articles

Research on the Advantages and Challenges of Replacing LEDs with Lasers in Functional Near-Infrared Spectroscopy Systems Based on Advanced Signal Processing Algorithms

Abstract: Functional near infrared spectroscopy (fNIRS) has received increasing attention as a non-invasive, portable brain hemodynamic monitoring tool due to its potential for use in natural Settings. Traditional fNIRS systems commonly use light-emitting diodes (LEDs) as light sources, which have become the mainstream choice because of their low cost, easy integration, low heat output, and availability of a variety of near-infrared wavelengths. However, LEDs are limited by their low optical output power and broad wavelength range, which restrict their effectiveness in deep tissue penetration and signal quality. In contrast, Laser Diodes (LD) have the advantages of good monochromaticity and high optical output power, which can provide deeper tissue penetration and higher signal stability. Therefore, this study explores the feasibility of using lasers instead of leds in fNIRS systems and their impact on signal quality and imaging depth. By comparing the performance of LED and laser-based fNIRS systems, it is evident that lasers provide significant improvements in signal quality and imaging depth, although they face challenges in terms of cost, thermal management, and safety. The research in this paper provides an important reference for the future design of fNIRS system, especially in the application scenarios requiring high precision imaging.

Evaluating prefrontal changes in depression using functional near-infrared spectroscopy utilizing Stroop test: A comparison with healthy controls

Background: Functional near-infrared spectroscopy (fNIRS) is being increasingly utilized to visualize the brain areas involved in cognitive activity to understand the human brain better. Its portability and easy setup give it an advantage over other functional brain imaging tools. The current study utilizes fNIRS while performing a Stroop test, which is commonly used to assess the impairment of information selection in depression. Aim: To compare cortical activation during the Stroop test in depressed individuals to healthy controls. Methods: This cross-sectional study compared oxyhemoglobin (OxyHb) concentration changes in 39 individuals with depression to 40 healthy individuals. The Stroop test was construed in an event-related design with an intertrial interval of 2 seconds with jitter. A continuous wave fNIRS system was used for recording the cortical activity at 17 locations. Analysis of fNIRS data was done using statistical parametric mapping (SPM) for estimating general linear model (GLM) coefficients. Further analysis of the mean change of OxyHb concentrations during the 2 seconds after the presentation of congruent and incongruent stimuli was done between the groups using Mann–Whitney U test corrected for multiple comparisons with Bonferroni correction. Results: While the number of errors and correct responses were similar between the groups, the reaction time for correct responses was more in the depression group in comparison to healthy individuals (t = -2.39, P = 0.01). For both healthy and depressive individuals in incongruent versus congruent task contrast, deactivation was seen in the region between the left middle frontal sulcus and frontopolar area of the brain (t = 0.41 and t = 0.21, respectively, P < 0.05). The depressed group had a higher mean change in OxyHb concentration following incongruent stimuli in comparison to the HCs (mean rank: HC = 32.63, depression = 47.56). Conclusion: Our findings indicate that individuals with depression exhibited prolonged reaction times and distinct activation patterns of the frontal cortex compared to healthy individuals. The observed pattern of brain activation for congruent and incongruent tasks among both healthy and depressed individuals aligns with the findings of the prior studies, emphasizing the utility of fNIRS as a valuable instrument for assessing brain activity.

Gender differences in the use of an upper-extremity exoskeleton during physically and cognitively demanding tasks- a study protocol for a randomized experimental trial.

Upper limb exoskeletons are recommended to alleviate muscle fatigue, particularly in working conditions inducing musculoskeletal discomfort like overhead work. However, wearing an exoskeleton might introduce cognitive-motor interference, affecting performance. Understanding its neural impact and potential gender differences in design effects is crucial. Therefore, the aim of this study is to examine exoskeleton effects addressing cross-gender comparisons, and exploring the impact on cognitive and physical workload in real-world scenarios. The research questions address the impact of exoskeleton use on muscle synergies, upper body posture, cognitive resources, comfort/discomfort, acceptance and usability. The cross-sectional study integrates a multifactorial mixed-measure design. Participants are grouped by gender (female vs. male) and working condition (with vs. without exoskeleton). Motor performance and underlying neuronal correlates (fNIRS) will be analyzed. Based on an a priori sample size calculation, 80 participants (40 female/40 male) will be recruited. Working performance will be assessed by 1. Physical Performance Task (PILE task) and 2. Precision Task (following the Fitts paradigm), while body postures will be monitored with an Xsens motion capture system. Brain activation will be captured with an fNIRS system comprising 32 active optodes. Postural comfort/discomfort, acceptance, and usability will be reported via standardized questionnaires. The study will gain insights into potential gender differences in exoskeleton use and will contribute to designing and optimizing the implementation of exoskeletons by considering muscle synergies, movement variability and cognitive resource allocation. Additionally, the study also highlights user discomfort, a crucial factor that could impede widespread adoption, particularly among females, in real-world scenarios.

NinjaNIRS: an open hardware solution for wearable whole-head high-density functional near-infrared spectroscopy.

Functional near-infrared spectroscopy (fNIRS) technology has been steadily advancing since the first measurements of human brain activity over 30 years ago. Initially, efforts were focused on increasing the channel count of fNIRS systems and then to moving from sparse to high density arrays of sources and detectors, enhancing spatial resolution through overlapping measurements. Over the last ten years, there have been rapid developments in wearable fNIRS systems that place the light sources and detectors on the head as opposed to the original approach of using fiber optics to deliver the light between the hardware and the head. The miniaturization of the electronics and increased computational power continues to permit impressive advances in wearable fNIRS systems. Here we detail our design for a wearable fNIRS system that covers the whole head of an adult human with a high-density array of 56 sources and up to 192 detectors. We provide characterization of the system showing that its performance is among the best in published systems. Additionally, we provide demonstrative images of brain activation during a ball squeezing task. We have released the hardware design to the public, with the hope that the community will build upon our foundational work and drive further advancements.

Design of an fNIRS-EEG hybrid terminal for wearable BCI systems.

The importance of brain-computer interfaces (BCI) is increasing, and various methods have been developed. Among the developed BCI methods, functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG) are favored due to their non-invasive feature and compact device sizes. EEG monitors the electrical potentials generated by the activation of neurons, and fNIRS monitors the blood flow also generated by neurons, resulting in signals with different properties between the two methods. As the two BCI methods greatly differ in the characteristics of the acquired neural activity signals, for cases of estimating the intention or thought of a subject by BCI, it has been proven that further accurate information may be extracted by utilizing both methods simultaneously. Both systems are powered by electricity, and as EEG systems are greatly sensitive to electrical noises, application of two separate fNIRS and EEG systems together may result in electrical interference as the systems are required to be in contact with the skin and stray currents from the fNIRS system may flow along the surface of the skin into the EEG system. This research proposes a wearable fNIRS-EEG hybrid BCI system, where a single terminal is capable of operating both as a continuous wave fNIRS emitter and as a detector, and also as an EEG electrode. The system has been designed such that the fNIRS and EEG components are electrically separated to avoid electrical interference between each other. It is expected that by utilizing the developed fNIRS-EEG hybrid terminals, the development of BCI analysis may be further accelerated in various fields.

Comparison of Brain Function Between Medication-Naïve ADHD with and without Comorbidity in Chinese Children Using Resting-State fNIRS.

This study used functional near-infrared spectroscopy (fNIRS) to investigate brain activation patterns in children with attention deficit hyperactivity disorder (ADHD) with and without additional comorbidities to identify disease-related biomarkers by the neuroimaging that will facilitate to make a diagnosis decision. In this study, 165 medication-naive children aged 7 to 15 years were recruited and categorized into four groups: ADHD, ADHD with learning disabilities (ADHD&LD), ADHD with oppositional defiant disorder (ADHD&ODD), and healthy controls. A multichannel fNIRS system was used to monitor hemodynamic changes at rest state in the prefrontal and temporal lobes of the brain. The amplitude of a low-frequency fluctuation (ALFF) matrix was calculated by summation and averaging of the square root of the signal power spectrum. One-way analysis of variance was used to identify statistical differences between channels. All ADHD children presented significantly higher ALFF values in different brain regions when compared with the healthy controls. Patients with ADHD&LD exhibited higher ALFF values in the medial prefrontal cortex (P Ch38 = .01, P Ch48 = .01), temporal cortex (P Ch22 = .04, P Ch41 = .002, P Ch51 = .001), and the left ventrolateral prefrontal cortex (P Ch39 = .0009, P Ch50 = .001), whereas ADHD&ODD children were not significantly different to those diagnosed with ADHD. ADHD with learning disabilities (LD) possessed a different pathogenesis from ADHD, manifested as lower functional brain activity in the medial prefrontal cortex, temporal cortex, and the left ventrolateral prefrontal cortex, while ADHD&ODD did not present significant changes compared with ADHD. ODD-related symptoms may be part of ADHD symptoms rather than being an independent disorder.

Multimodal analysis of cortical activation in young male adults with internet gaming disorder: A resting state EEG-fNIRS study

BackgroundInternet gaming disorder (IGD) has become increasingly prevalent worldwide and is recognized as a significant public health concern because of its negative consequences on individuals mental and physical health, social relationships, academic performance and overall well-being. While research on IGD has gained significant momentum in the past decade, the neural substrates underlying this disorder remains unclear. This study aims to investigate resting-state cortical activation in male subjects with IGD using a concurrent functional near infrared spectroscopy (fNIRS) and electroencephalography (EEG) hybrid system. MethodsTwenty-two male individuals with IGD (18–23 years old) and twenty-two male healthy, age-matched healthy controls were included in the study. Mean oxygenation of the prefrontal cortex (PFC) and whole head neural activity were measured using fNIRS and EEG respectively, during eyes-open and eyes-closed conditions at the resting state. EEG signals were decomposed into distinct frequency sub-bands with wavelet transform, followed by the analysis of the power spectral density within each band. Mean oxygenation of PFC is measured using a multichannel fNIRS system. ResultsResults revealed that the individuals with IGD had significantly higher beta power in the frontal region compared to the control group. Individuals with IGD showed significantly increased PFC oxygenation compared to healthy controls. Additionally, both frontal beta power and PFC oxygenation were significantly correlated with IGD severity. However, there were no significant correlations observed between oxygenation and beta powers. ConclusionThis study is the first to examine resting-state cortical activation using multimodal EEG-fNIRS system in young adults with IGD. Moreover, it provides an important contribution to the understanding of the underlying neural mechanisms of IGD and offer new insights for the diagnosis and intervention of the disorder using multimodal EEG-fNIRS system. Further studies should aim to replicate the findings of this study using a larger and more culturally diverse sample to support the neurophysiological basis of IGD.

Flexible circuit-based spatially aware modular optical brain imaging system for high-density measurements in natural settings.

Functional near-infrared spectroscopy (fNIRS) presents an opportunity to study human brains in everyday activities and environments. However, achieving robust measurements under such dynamic conditions remains a significant challenge. The modular optical brain imaging (MOBI) system is designed to enhance optode-to-scalp coupling and provide a real-time probe three-dimensional (3D) shape estimation to improve the use of fNIRS in everyday conditions. The MOBI system utilizes a bendable and lightweight modular circuit-board design to enhance probe conformity to head surfaces and comfort for long-term wearability. Combined with automatic module connection recognition, the built-in orientation sensors on each module can be used to estimate optode 3D positions in real time to enable advanced tomographic data analysis and motion tracking. Optical characterization of the MOBI detector reports a noise equivalence power of 8.9 and at 735 and 850nm, respectively, with a dynamic range of 88dB. The 3D optode shape acquisition yields an average error of 4.2mm across 25 optodes in a phantom test compared with positions acquired from a digitizer. Results for initial in vivo validations, including a cuff occlusion and a finger-tapping test, are also provided. To the best of our knowledge, the MOBI system is the first modular fNIRS system featuring fully flexible circuit boards. The self-organizing module sensor network and automatic 3D optode position acquisition, combined with lightweight modules ( ) and ergonomic designs, would greatly aid emerging explorations of brain function in naturalistic settings.

Variations in Cortical Oxygenation by Near-Infrared Spectroscopy According to Head Position after Acute Stroke: The Preliminary Findings of an Observational Study.

Background: After ischemic stroke, there is no general consensus on the optimal position for the head of patients in the acute phase. This observational study aimed to measure the variations in cortical oxygenation using noninvasive functional near-infrared spectroscopy (fNIRS) at different degrees of head positioning on a bed. Methods: Consecutive ischemic stroke patients aged 18 years or older with anterior circulation ischemic stroke within 48 h of symptom onset who could safely assume different positions on a bed were included. A 48-channel fNIRS system was placed in the bilateral sensorimotor cortex. Then, the bed of each patient was moved into four consecutive positions: (1) seated (90° angle between the head and bed surface); (2) lying at 30°; (3) seated again (90°); and (4) lying flat (0°). Each position was maintained for 90 s; the test was conducted 48 h after stroke onset and after 5 ± 1 days. The variations in oxygenated hemoglobin in the global brain surface and for each hemisphere were recorded and compared. Results: Twenty-one patients were included (males, n = 11; age, 79 ± 9 years; ASPECTS, 8 ± 2). When evaluating the affected side, the median oxygenation was significantly greater in the lying-flat (0°) and 30° positions than in the 90° position (p < 0.001 for both comparisons). No significant differences between the supine position and the 30° position were found, although oxygenation was slightly lower in the 30° position than in the supine position (p = 0.063). No differences were observed when comparing recanalized and nonrecanalized patients separately or according to stroke severity. The evaluation conducted 5 days after the stroke confirmed the previous data, with a significant difference in oxygenation at 0° and 30° compared to 90°. Conclusions: This preliminary study suggested that there are no substantial differences in brain oxygenation between the lying-flat head position and the 30° laying position.

The relationship between computational thinking and mental rotation ability: a functional near-infrared spectroscopy study

ABSTRACT This study investigates the correlation between computational thinking (CT) skills and mental rotation ability, utilizing functional near-infrared spectroscopy (fNIRS) to assess neural efficiency as a potential underlying factor. We divided 38 participants into groups with high and low CT skills to perform mental rotation tasks. Prefrontal brain activity was monitored via a 48-channel fNIRS system. The experimental results suggested that participants possessing superior CT skills exhibited enhanced performance on the mental rotation test, accompanied by a higher neural efficiency index in the specified cerebral regions, as denoted by reduced oxygen consumption. This implies that individuals endowed with advanced CT capabilities can process information with greater efficacy, thereby attaining superior performance utilising lesser cognitive exertion. Our study offers some of the first empirical evidence regarding the neural basis underlying the relationship between CT skills and mental rotation ability in undergraduate students.

Flexible-circuit-based 3-D aware modular optical brain imaging system for high-density measurements in natural settings.

Functional near-infrared spectroscopy (fNIRS) presents an opportunity to study human brains in everyday activities and environments. However, achieving robust measurements under such dynamic condition remains a significant challenge. The modular optical brain imaging (MOBI) system is designed to enhance optode-to-scalp coupling and provide real-time probe 3-D shape estimation to improve the use of fNIRS in everyday conditions. The MOBI system utilizes a bendable and lightweight modular circuit-board design to enhance probe conformity to head surfaces and comfort for long-term wearability. Combined with automatic module connection recognition, the built-in orientation sensors on each module can be used to estimate optode 3-D positions in real-time to enable advanced tomographic data analysis and motion tracking. Optical characterization of the MOBI detector reports a noise equivalence power (NEP) of 8.9 and 7.3 at 735 nm and 850 nm, respectively, with a dynamic range of 88 dB. The 3-D optode shape acquisition yields an average error of 4.2 mm across 25 optodes in a phantom test compared to positions acquired from a digitizer. Results for initial in vivo validations, including a cuff occlusion and a finger-tapping test, are also provided. To the best of our knowledge, the MOBI system is the first modular fNIRS system featuring fully flexible circuit boards. The self-organizing module sensor network and automatic 3-D optode position acquisition, combined with lightweight modules (18 g/module) and ergonomic designs, would greatly aid emerging explorations of brain function in naturalistic settings.

Exploratory insights into prefrontal cortex activity in continuous glucose monitoring: findings from a portable wearable functional near-infrared spectroscopy system.

The escalating global prevalence of diabetes highlights an urgent need for advancements in continuous glucose monitoring (CGM) technologies that are non-invasive, accurate, and user-friendly. Here, we introduce a groundbreaking portable wearable functional near-infrared spectroscopy (fNIRS) system designed to monitor glucose levels by assessing prefrontal cortex (PFC) activity. Our study delineates the development and application of this novel fNIRS system, emphasizing its potential to revolutionize diabetes management by providing a non-invasive, real-time monitoring solution. Fifteen healthy university students participated in a controlled study, where we monitored their PFC activity and blood glucose levels under fasting and glucose-loaded conditions. Our findings reveal a significant correlation between PFC activity, as measured by our fNIRS system, and blood glucose levels, suggesting the feasibility of fNIRS technology for CGM. The portable nature of our system overcomes the mobility limitations of traditional setups, enabling continuous, real-time monitoring in everyday settings. We identified 10 critical features related to blood glucose levels from extensive fNIRS data and successfully correlated PFC function with blood glucose levels by constructing predictive models. Results show a positive association between fNIRS data and blood glucose levels, with the PFC exhibiting a clear response to blood glucose. Furthermore, the improved regressive rule principal component analysis (PCA) method outperforms traditional PCA in model prediction. We propose a model validation approach based on leave-one-out cross-validation, demonstrating the unique advantages of K-nearest neighbor (KNN) models. Comparative analysis with existing CGM methods reveals that our paper's KNN model exhibits lower RMSE and MARD at 0.11 and 8.96%, respectively, and the fNIRS data were highly significant positive correlation with actual blood glucose levels (r = 0.995, p < 0.000). This study provides valuable insights into the relationship between metabolic states and brain activity, laying the foundation for innovative CGM solutions. Our portable wearable fNIRS system represents a significant advancement in effective diabetes management, offering a promising alternative to current technologies and paving the way for future advancements in health monitoring and personalized medicine.

Brain activity behind the negative and positive emotions: an experimental setting with functional near-infrared spectroscopy (fNIRS)

Introduction The fNIRS is an optical brain monitoring technique which uses near-infrared spectroscopy for the purpose of functional neuroimaging. Using fNIRS, brain activity is measured by using near-infrared light to estimate cortical hemodynamic activity which occurs in response to neural activity. In the aspect of psychiatry fNIRS is a tool that can potentially facilitate the clinical diagnostic process and identify stages of psychiatric illnesses by providing objective and quantifiable evidences of brain changes. However, this will require specific cerebral haemodynamic patterns to be validated in larger clinical populations with specific psychiatric disorders.ObjectivesOur team decided to set a fNIRS system to find out the difference in prefrontocortical (PFC) activity pattern between healthy and anhedonic population. This abstract has been created for introduce our first findings about the difference in PFC activity under emotionally positively or negatively coloured stimuli in healthy population.MethodsWe have measured 5 healthy adults, non-anhedonic participants under emotionally different visual and acustic stimuli with the use of NIRX/NIRScout system with the view of our prefronto-temporo-parietal montege.Fig 1 We devided our experimental tools into 4 individual 20 second long parts:Passive neutral visual or acustic stimuli for baseline (watching a black dot or scilence)Passive visual stimuli (watching a single picture)Active visual stimuli (choosing from photo collage) to detect contrast of cortical background activationPassive acustic stimuli (listening sounds)In total, we defined emotionally, two neutral, four positive and four negative stimuli in our experimental setting. Softwares: We used HOMER3 for analyzing our datas and estimate hemodinamic reponse factor (HRF) using general linear matrix (GLM) regression. We used AtlasViewer for reconstructing HRF image onto the Colin27 digitalised brain model. And we also used SPSS for statictical analysis between stimuli types and HRF means.ResultsFig 2 Significant HRF differences were measured in the dominant hemisphere dorsolateral prefrontal cortex (DLPFC) between the influence of each emotionally negative and positive stimuli (p&lt;0.001). The level of DLPFC activity was positively influenced by emotionally positive stimuli (p&lt;0.001).Image:Image 2:Image 3:ConclusionsFig 3 Our fNIRS experimental system is a suitable tool to measure and model the pattern of prefrontal cortical activity.Based on the measured hemodynamic values, we detected a significant activity difference in the dominant hemisphere DLPFC during emotionally positive and negative stimuli, the extent of which is positively influenced by emotionally positive stimuli.The left DLPFC appears to be a promising target for our next studies of anhedonia.Disclosure of InterestNone Declared

Reliability Evaluation for Continuous-Wave Functional Near-Infrared Spectroscopy Systems: Comprehensive Testing from Bench Characterization to Human Test.

In recent years, biomedical optics technology has developed rapidly. The current widespread use of biomedical optics was made possible by the invention of optical instruments. The advantages of being non-invasive, portable, effective, low cost, and less susceptible to system noise have led to the rapid development of functional near-infrared spectroscopy (fNIRS) technology for hemodynamics detection, especially in the field of functional brain imaging. At the same time, laboratories and companies have developed various fNIRS-based systems. The safety, stability, and efficacy of fNIRS systems are key performance indicators. However, there is still a lack of comprehensive and systematic evaluation methods for fNIRS instruments. This study uses the fNIRS system developed in our laboratory as the test object. The test method established in this study includes system validation and performance testing to comprehensively assess fNIRS systems' reliability. These methods feature low cost and high practicality. Based on this study, existing or newly developed systems can be comprehensively and easily evaluated in the laboratory or workspace.

Artificial neural network models: implementation of functional near-infrared spectroscopy-based spontaneous lie detection in an interactive scenario.

Deception is an inevitable occurrence in daily life. Various methods have been used to understand the mechanisms underlying brain deception. Moreover, numerous efforts have been undertaken to detect deception and truth-telling. Functional near-infrared spectroscopy (fNIRS) has great potential for neurological applications compared with other state-of-the-art methods. Therefore, an fNIRS-based spontaneous lie detection model was used in the present study. We interviewed 10 healthy subjects to identify deception using the fNIRS system. A card game frequently referred to as a bluff or cheat was introduced. This game was selected because its rules are ideal for testing our hypotheses. The optical probe of the fNIRS was placed on the subject's forehead, and we acquired optical density signals, which were then converted into oxy-hemoglobin and deoxy-hemoglobin signals using the Modified Beer-Lambert law. The oxy-hemoglobin signal was preprocessed to eliminate noise. In this study, we proposed three artificial neural networks inspired by deep learning models, including AlexNet, ResNet, and GoogleNet, to classify deception and truth-telling. The proposed models achieved accuracies of 88.5%, 88.0%, and 90.0%, respectively. These proposed models were compared with other classification models, including k-nearest neighbor, linear support vector machines (SVM), quadratic SVM, cubic SVM, simple decision trees, and complex decision trees. These comparisons showed that the proposed models performed better than the other state-of-the-art methods.

A Wireless Integrated EEG–fNIRS System for Brain Function Monitoring

A Wireless Integrated EEG–fNIRS System for Brain Function Monitoring

Hemodynamic signal changes during volitional swallowing in dysphagia patients with different unilateral hemispheric stroke and brainstem stroke: A near-infrared spectroscopy study

Background and objectivesStrokes will result in decreased in cortical excitability and changed in the balance between the affected and unaffected hemispheres. Previous studies have focused on cortical changes in healthy subjects during swallowing, while they remain unknown in patients with stroke at different locations. Thus, the purpose of this study was to research cortical activation patterns of swallowing in patients with dysphagia and healthy subjects by the functional near-infrared spectroscopy (fNIRS). We also focus on the comparability of brain activation areas associated with swallowing between patients with different stroke locations and healthy subjects. Methodstotal of 104 participants were invited to our study, involving 86 patients with dysphagic unilateral hemispheric stroke and 18 age and sex matched healthy controls. The stroke patients were categorized into patients with left unilateral stroke lesions (n = 30), patients with right unilateral stroke lesions (n = 32) and patients with brainstem injury (n = 24) according to different stroke sites. All patients underwent a series of clinical swallowing function assessments, such as the Fiberoptic endoscopic dysphagia severity scale (FEDSS), penetration-aspiration scale (PAS) of Rosenbek, the gugging swallowing screen (GUSS) and the functional oral Intake scale (FOIS) after informed consent has been signed. All participants received the fNIRS system assessment. ResultsThe results showed that extensive areas of the cerebral cortex activated during the swallowing tasks in healthy participants (P < FDR 0.05). For patients with left unilateral stroke lesions, the HbO concentration were strongest over the right hemisphere (P < FDR 0.05). In addition, a less severe activation was also observed in the left hemisphere. Comparable to patients with left unilateral stroke lesions, the strongest activation during swallowing task were found in the left hemisphere in patients with right unilateral stroke lesions (P < FDR 0.05). Similarly, the right hemisphere also has activated less. In contrast, patients with brain stem injury showed more bilaterally activation patterns. ConclusionOur finding states that cortical activation areas differ between patients with different stroke locations and healthy subjects during swallowing. There was a more bilateral activation in healthy participants and patients with lesions in the brainstem while more cortical activation in unaffected hemisphere in patients with unilateral hemispheric stroke. It also provides a basis for the future treatment of dysphagia after stroke.

Changes in cerebral cortex activity during a simple motor task after MRgFUS treatment in patients affected by essential tremor and Parkinson's disease: a pilot study using functional NIRS

Objective. Magnetic resonance imaging-guided focused ultrasound surgery (MRgFUS) is a non-invasive thermal ablation method that involves high-intensity focused ultrasound surgery (FUS) and Magnetic Resonance Imaging for anatomical imaging and real-time thermal mapping. This technique is widely employed for the treatment of patients affected by essential tremor (ET) and Parkinson’s disease (PD). In the current study, functional near-infrared spectroscopy (fNIRS) was used to highlight hemodynamics changes in cerebral cortex activity, during a simple hand motor task, i.e. unimanual left and right finger-tapping, in ET and PD patients. Approach. All patients were evaluated before, one week and one month after MRgFUS treatment. Main results. fNIRS revealed cerebral hemodynamic changes one week and one month after MRgFUS treatment, especially in the ET group, that showed a significant clinical improvement in tremor clinical scores. Significance. To our knowledge, our study is the first that showed the use of fNIRS system to measure the cortical activity changes following unilateral ventral intermediate nucleus thalamotomy after MRgFUS treatment. Our findings showed that therapeutic MRgFUS promoted the remodeling of neuronal networks and changes in cortical activity in association with symptomatic improvements.

Interpretable deep learning model for major depressive disorder assessment based on functional near-infrared spectroscopy

BackgroundMajor depressive disorder (MDD) affects a substantial number of individuals worldwide. New approaches are required to improve the diagnosis of MDD, which relies heavily on subjective reports of depression-related symptoms. AimEstablish an objective measurement and evaluation of MDD. MethodsFunctional near-infrared spectroscopy (fNIRS) was used to investigate the brain activity of MDD patients and healthy controls (HCs). Leveraging a sizeable fNIRS dataset of 263 HCs and 251 patients with MDD, including mild to moderate MDD (mMDD; n = 139) and severe MDD (sMDD; n = 77), we developed an interpretable deep learning model for screening MDD and staging its severity. ResultsThe proposed deep learning model achieved an accuracy of 80.9% in diagnostic classification and 78.6% in severity staging for MDD. We discerned five channels with the most significant contribution to MDD identification through Shapley additive explanations (SHAP), located in the right medial prefrontal cortex, right dorsolateral prefrontal cortex, right superior temporal gyrus, and left posterior superior frontal cortex. The findings corresponded closely to the features of haemoglobin responses between HCs and individuals with MDD, as we obtained a good discriminative ability for MDD using cortical channels that are related to the disorder, namely the frontal and temporal cortical channels with areas under the curve of 0.78 and 0.81, respectively. ConclusionOur study demonstrated the potential of integrating the fNIRS system with artificial intelligence algorithms to classify and stage MDD in clinical settings using a large dataset. This approach can potentially enhance MDD assessment and provide insights for clinical diagnosis and intervention.

Artificial Intelligence-Based Diagnostic Support System for Functional Dyspepsia Based on Brain Activity and Food Preference.

Background and aim Disorders of gut-brain interaction (DGBI) aredisorders where no organic clinical abnormalities are detected such as functional dyspepsia (FD) and irritable bowel syndrome (IBS). The brain activity of individuals with FD and IBS differs from that of healthy controls. Artificial intelligence can distinguish healthy controls from individuals with DGBI using several biomarkers. This study aimed to establish an artificial intelligence-based diagnostic support system using food preferences and brain activity in patients with DGBI. Methods ROME IV criteria were used to diagnose patients with FD and IBS. Their food preference was scored using a visual analog scale, and brain activity in the prefrontal cortex was investigated using functional near-infrared spectroscopy (fNIRS). The diagnostic model was developed based on the brain activity and visual analog scale scores for food using an artificial neural network model. Results Forty-one participants, including 25 patients with DGBI were enrolled in the study. The accuracy of the artificial intelligence-based diagnostic model using an artificial neural network in differentiating between healthy controls and patients with DGBI and between healthy controls and those with FD were 72.3% and 77.1%, respectively. Conclusions The artificial intelligence-based diagnostic model using brain activity and preference to food images showed sufficiently high accuracy in distinguishing patients with DGBI from healthy controls, and those with FD from healthy controls. Therefore, the fNIRS system provides objective evidence for diagnosing DGBI.