Functional near-infrared spectroscopy (fNIRS) was used to investigate spontaneous hemodynamic activity in the temporal cortex for typically developing (TD) children and children with autism spectrum disorder (ASD). Forty-seven children participated in the experiments including twenty-five with ASD. Compared with TD children, children with ASD showed weaker bilateral resting-state functional connectivity (RSFC), but much stronger fluctuation magnitude in terms of oxy-hemoglobin (HbO 2 ) and deoxy-hemoglobin (Hb). Differentiating between ASD and TD based on a support vector machine (SVM) model including bilateral RSFC and the fluctuation power of HbO 2 and Hb as variables could achieve high accurate classification with sensitivity of 81.6% and specificity of 94.6%. This study demonstrates optical brain imaging has the potential for screening children with risk of ASD.

Extinction coefficient (ε) is a critical parameter for quantification of oxy-, deoxy-, and total-hemoglobin concentrations (Δ[HbO], Δ[Hb], Δ[tHb]) from optical measurements of Near-infrared spectroscopy (NIRS). There are several different ε data sets which were frequently used in NIRS quantification. A previous study reported that even a small variation in ε could cause a significant difference in hemodynamic measurements. Apparently the selection of an optimal ε data set is important for NIRS. We conducted oxygen-state-varied and blood-concentration-varied model experiments with 57 human blood samples to mimic tissue hemodynamic variations. Seven reported ε data sets were evaluated by comparisons between quantifications and assumed values. We found that the Moaveni et al (1970)' ε data set was the optimal one, the NIRS quantification varied significantly among different ε data sets and parameter Δ[tHb] was most sensitive to ε data sets selection.

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. Significance : Multi-laboratory initiatives are essential in performance assessment and standardization—crucial for bringing biophotonics to mature clinical use—to establish protocols and develop reference tissue phantoms that all will allow universal instrument comparison. Aim : The largest multi-laboratory comparison of performance assessment in near-infrared diffuse optics is presented, involving 28 instruments and 12 institutions on a total of eight experiments based on three consolidated protocols (BIP, MEDPHOT, and NEUROPT) as implemented on three kits of tissue phantoms. A total of 20 synthetic indicators were extracted from the dataset, some of them defined here anew. Approach : The exercise stems from the Innovative Training Network BitMap funded by the European Commission and expanded to include other European laboratories. A large variety of diffuse optics instruments were considered, based on different approaches (time domain/frequency domain/continuous wave), at various stages of maturity and designed for different applications (e.g., oximetry, spectroscopy, and imaging). Results : This study highlights a substantial difference in hardware performances (e.g., nine decades in responsivity, four decades in dark count rate, and one decade in temporal resolution). Agreement in the estimates of homogeneous optical properties was within 12% of the median value for half of the systems, with a temporal stability of over 1 h, and day-to-day reproducibility of . Other tests encompassed linearity, crosstalk, uncertainty, and detection of optical inhomogeneities. Conclusions : This extensive multi-laboratory exercise provides a detailed assessment of near-infrared Diffuse optical instruments and can be used for reference grading. The dataset—available soon in an open data repository—can be evaluated in multiple ways, for instance, to compare different analysis tools or study the impact of hardware implementations.

Functional near-infrared spectroscopy (fNIRS) was used to investigate resting state connectivity of language areas including bilateral inferior frontal gyrus (IFG) and superior temporal gyrus (STG). Thirty-two subjects participated in the experiment, including twenty adults and twelve children. Spontaneous hemodynamic fluctuations were recorded, and then intra- and inter-hemispheric temporal correlations of these signals were computed. The correlations of all hemoglobin components were observed significantly higher for adults than children. Moreover, the differences for the STG were more significant than for the IFG. In the adult group, differences in the correlations between males and females were not significant. Our results suggest by measuring resting state intra- and inter-hemispheric correlations, fNIRS is able to provide qualitative and quantitative evaluation on the functioning of the cortical network.

Time-domain diffuse correlation spectroscopy (TD-DCS) has been recently proposed to improve detection of deep blood flow dynamics in a biological tissue, such as human brain. To obtain a high sensitive measurement, several experimental parameters such as the source-detector (SD) distance, gate opening time, and width need to be considered and optimized. We use a simulation approach to optimize these parameters based on Monte Carlo computations using a realistic human head model. Two cortical regions are investigated including the frontal and temporal lobes. SD distance ranging from 0 to 45 mm, gate opening time from 400 to 1000 ps, and gate width from 50 to 3000 ps are considered. The goal is to find out the optimal combinations of these parameters by which the higher contrast measurement on the cortical dynamics can be achieved. The simulations show that with an acceptable input power of light, the combinations of SD distance ranging from 0 to 15 mm, gate opening time at 700 to 800 ps, and gate width of 800 ps are optimal for achieving higher contrast measurement on the cortical dynamics. The simulation approach and results are helpful for the optimization of TD-DCS experimental design focused on brain functional detection.

Simple SummaryAnticipatory behaviour to an oncoming food reward can be triggered via classical conditioning, implies the activation of neural networks, and may serve to study the emotional state of animals. This work aimed to investigate how the anticipatory response affects cerebral cortex activity in sheep. Eight ewes were conditioned to associate a neutral auditory stimulus (water bubble) to a food reward (maize grains). Then, sheep were trained to wait 15 s before accessing the food (anticipatory phase). Behavioural reaction and changes in cortical oxy-haemoglobin ([ΔO2Hb]) and deoxy-haemoglobin ([ΔHHb]) concentration were recorded by functional near infrared spectroscopy (fNIRS). During the anticipatory phase, sheep increased their active behaviour together with a cortical activation (increase of [ΔO2Hb] and a decrease of [ΔHHb]) compared to baseline. Sheep showed a greater response of the right hemisphere compared to the left hemisphere, possibly indicating frustration. Behavioural and cortical changes observed during anticipation of a food reward reflect a learnt association and an increased arousal, but no clear emotional valence of the sheep subjective experience.AbstractAnticipatory behaviour to an oncoming food reward can be triggered via classical conditioning, implies the activation of neural networks, and may serve to study the emotional state of animals. The aim of this study was to investigate how the anticipatory response to a food reward affects the cerebral cortex activity in sheep. Eight ewes from the same flock were trained to associate a neutral auditory stimulus (water bubble) to the presence of a food reward (maize grains). Once conditioned, sheep were trained to wait 15 s behind a gate before accessing a bucket with food (anticipation phase). For 6 days, sheep were submitted to two sessions of six consecutive trials each. Behavioural reaction was filmed and changes in cortical oxy- and deoxy-hemoglobin concentration ([ΔO2Hb] and [ΔHHb] respectively) following neuronal activation were recorded by functional near infrared spectroscopy (fNIRS). Compared to baseline, during the anticipation phase sheep increased their active behaviour, kept the head oriented to the gate (Wilcoxon’s signed rank test; p ≤ 0.001), and showed more asymmetric ear posture (Wilcoxon’s signed rank test; p ≤ 0.01), most likely reflecting a learnt association and an increased arousal. Results of trial-averaged [ΔO2Hb] and [ΔHHb] within individual sheep showed in almost every sheep a cortical activation during the anticipation phase (Student T-test; p ≤ 0.05). The sheep showed a greater response of the right hemisphere compared to the left hemisphere, possibly indicating a negative affective state, such as frustration. Behavioural and cortical changes observed during anticipation of a food reward reflect a learnt association and an increased arousal, but no clear emotional valence of the sheep subjective experience. Future work should take into consideration possible factors affecting the accurateness of measures, such a...

Electroencephalogram (EEG) signals have been widely used in emotion recognition. However, the current EEG-based emotion recognition has low accuracy of emotion classification, and its real-time application is limited. In order to address these issues, in this paper, we proposed an improved feature selection algorithm to recognize subjects’ emotion states based on EEG signal, and combined this feature selection method to design an online emotion recognition brain-computer interface (BCI) system. Specifically, first, different dimensional features from the time-domain, frequency domain, and time-frequency domain were extracted. Then, a modified particle swarm optimization (PSO) method with multi-stage linearly-decreasing inertia weight (MLDW) was purposed for feature selection. The MLDW algorithm can be used to easily refine the process of decreasing the inertia weight. Finally, the emotion types were classified by the support vector machine classifier. We extracted different features from the EEG data in the DEAP data set collected by 32 subjects to perform two offline experiments. Our results showed that the average accuracy of four-class emotion recognition reached 76.67%. Compared with the latest benchmark, our proposed MLDW-PSO feature selection improves the accuracy of EEG-based emotion recognition. To further validate the efficiency of the MLDW-PSO feature selection method, we developed an online two-class emotion recognition system evoked by Chinese videos, which achieved good performance for 10 healthy subjects with an average accuracy of 89.5%. The effectiveness of our method was thus demonstrated.