scholarly journals Widespread nociceptive maps in the human neonatal somatosensory cortex

2021 ◽  
Author(s):  
Laura Jones ◽  
Madeleine Verriotis ◽  
Robert Cooper ◽  
Maria Laudiano-Dray ◽  
Mohammed Rupawala ◽  
...  
Keyword(s):  
Near Infrared ◽  
Signal To Noise Ratio ◽  
Newborn Infants ◽  
Sensory Stimulation ◽  
Functional Near Infrared Spectroscopy ◽  
Stimulus Modality ◽  
Pain Behaviour ◽  
Motor Responses ◽  
Skin Region ◽  
Stimulation Of

Topographic cortical maps are essential for spatial localisation of sensory stimulation and generation of appropriate task-related motor responses. Somatosensation and nociception are finely mapped and aligned in the adult somatosensory (S1) cortex, but in infancy, when pain behaviour is disorganised and poorly directed, nociceptive maps may be less refined. We compared the topographic pattern of S1 activation following noxious (clinically required heel lance) and innocuous (touch) mechanical stimulation of the same skin region in newborn infants (n=32) using multi-optode functional near-infrared spectroscopy (fNIRS). Signal to noise ratio and overall activation area did not differ with stimulus modality. Within S1 cortex, touch and lance of the heel elicit localised, partially overlapping increases in oxygenated haemoglobin (HbO), but while touch activation was restricted to the heel area, lance activation extended into cortical hand regions. The data reveals a widespread cortical nociceptive map in infant S1, consistent with their poorly directed pain behaviour.

scholarly journals Facial Vibrotactile Stimulation Activates the Parasympathetic Nervous System: Study of Salivary Secretion, Heart Rate, Pupillary Reflex, and Functional Near-Infrared Spectroscopy Activity

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Hisao Hiraba ◽  
Motoharu Inoue ◽  
Kanako Gora ◽  
Takako Sato ◽  
Satoshi Nishimura ◽  
...  
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Infrared Spectroscopy ◽  
Near Infrared ◽  
Human Subjects ◽  
Vibrotactile Stimulation ◽  
Functional Near Infrared Spectroscopy ◽  
Healthy Human ◽  
Healthy Human Subjects ◽  
Stimulation Of

We previously found that the greatest salivation response in healthy human subjects is produced by facial vibrotactile stimulation of 89 Hz frequency with 1.9 μm amplitude (89 Hz-S), as reported by Hiraba et al. (2012, 20011, and 2008). We assessed relationships between the blood flow to brain via functional near-infrared spectroscopy (fNIRS) in the frontal cortex and autonomic parameters. We used the heart rate (HRV: heart rate variability analysis in RR intervals), pupil reflex, and salivation as parameters, but the interrelation between each parameter and fNIRS measures remains unknown. We were to investigate the relationship in response to established paradigms using simultaneously each parameter-fNIRS recording in healthy human subjects. Analysis of fNIRS was examined by a comparison of various values between before and after various stimuli (89 Hz-S, 114 Hz-S, listen to classic music, and “Ahh” vocalization). We confirmed that vibrotactile stimulation (89 Hz) of the parotid glands led to the greatest salivation, greatest increase in heart rate variability, and the most constricted pupils. Furthermore, there were almost no detectable differences between fNIRS during 89 Hz-S and fNIRS during listening to classical music of fans. Thus, vibrotactile stimulation of 89 Hz seems to evoke parasympathetic activity.

scholarly journals Exploring FPGA Based Lock-in Techniques for Brain Monitoring Applications

2017 ◽  
Author(s):  
Giuseppe Costantino Giaconia ◽  
Giuseppe Greco ◽  
Leonardo Mistretta ◽  
Raimondo Rizzo
Keyword(s):  
Signal Detection ◽  
Near Infrared ◽  
Signal To Noise Ratio ◽  
Mixed Solution ◽  
Functional Near Infrared Spectroscopy ◽  
Noise Rejection ◽  
Field Programmable ◽  
Lock In ◽  
Design Cost ◽  
Health Applications

Functional Near Infrared Spectroscopy (fNIRS) systems for e-health applications usually suffer of poor signal detection mainly due to a low end-to-end signal to noise ratio of the electronics chain. Lock-In Amplifiers (LIA) historically represent a powerful technique helping to improve performances in such circumstances. In this work it has been designed and implemented a digital LIA system, based on a Zynq® Field Programmable Gate Array (FPGA), trying to explore if this technique might improve fNIRS system performances. More broadly, FPGA based solution flexibility has been investigated, with particular emphasis applied to digital filter parameters, needed in the digital LIA, and it has been evaluated its impact on the final signal detection and noise rejection capability. The realized architecture was a mixed solution between VHDL hardware modules and software ones, running within a softcore microprocessor. Experimental results have shown the goodness of the proposed solutions and comparative details among different implementation will be detailed. Finally a key aspect taken into account throughout the design was its modularity, allowing an ease increase of the input channels while avoiding the growth of the design cost of the electronics system.

scholarly journals MOTION ARTIFACT REDUCTION IN FUNCTIONAL NEAR INFRARED SPECTROSCOPY SIGNALS BY AUTOREGRESSIVE MOVING AVERAGE MODELING BASED KALMAN FILTERING

2013 ◽  
Vol 06 (04) ◽  
pp. 1350035
Author(s):  
MEHDI AMIAN ◽  
S. KAMALEDIN SETAREHDAN
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Signal To Noise Ratio ◽  
Moving Average ◽  
Motion Artifact ◽  
Space Representation ◽  
Autoregressive Moving Average ◽  
Functional Near Infrared Spectroscopy ◽  
State Space Representation

Functional near infrared spectroscopy (fNIRS) is a technique that is used for noninvasive measurement of the oxyhemoglobin (HbO2) and deoxyhemoglobin (HHb) concentrations in the brain tissue. Since the ratio of the concentration of these two agents is correlated with the neuronal activity, fNIRS can be used for the monitoring and quantifying the cortical activity. The portability of fNIRS makes it a good candidate for studies involving subject's movement. The fNIRS measurements, however, are sensitive to artifacts generated by subject's head motion. This makes fNIRS signals less effective in such applications. In this paper, the autoregressive moving average (ARMA) modeling of the fNIRS signal is proposed for state-space representation of the signal which is then fed to the Kalman filter for estimating the motionless signal from motion corrupted signal. Results are compared to the autoregressive model (AR) based approach, which has been done previously, and show that the ARMA models outperform AR models. We attribute it to the richer structure, containing more terms indeed, of ARMA than AR. We show that the signal to noise ratio (SNR) is about 2 dB higher for ARMA based method.

scholarly journals A Motion Artifact Correction Procedure for fNIRS Signals Based on Wavelet Transform and Infrared Thermography Video Tracking

Sensors ◽  
2021 ◽  
Vol 21 (15) ◽  
pp. 5117
Author(s):  
David Perpetuini ◽  
Daniela Cardone ◽  
Chiara Filippini ◽  
Antonio Maria Chiarelli ◽  
Arcangelo Merla
Keyword(s):  
Wavelet Transform ◽  
Infrared Thermography ◽  
Near Infrared ◽  
Signal To Noise Ratio ◽  
Motion Artifact ◽  
Video Tracking ◽  
Wavelet Coherence ◽  
Functional Near Infrared Spectroscopy ◽  
Artifact Correction ◽  
Physiological Signal

Functional near infrared spectroscopy (fNIRS) is a neuroimaging technique that allows to monitor the functional hemoglobin oscillations related to cortical activity. One of the main issues related to fNIRS applications is the motion artefact removal, since a corrupted physiological signal is not correctly indicative of the underlying biological process. A novel procedure for motion artifact correction for fNIRS signals based on wavelet transform and video tracking developed for infrared thermography (IRT) is presented. In detail, fNIRS and IRT were concurrently recorded and the optodes’ movement was estimated employing a video tracking procedure developed for IRT recordings. The wavelet transform of the fNIRS signal and of the optodes’ movement, together with their wavelet coherence, were computed. Then, the inverse wavelet transform was evaluated for the fNIRS signal excluding the frequency content corresponding to the optdes’ movement and to the coherence in the epochs where they were higher with respect to an established threshold. The method was tested using simulated functional hemodynamic responses added to real resting-state fNIRS recordings corrupted by movement artifacts. The results demonstrated the effectiveness of the procedure in eliminating noise, producing results with higher signal to noise ratio with respect to another validated method.

scholarly journals Hemodynamic response to visual stimulation in newborn infants using functional near-infrared spectroscopy

2008 ◽  
Vol 29 (4) ◽  
pp. 453-460 ◽  
Author(s):  
Tanja Karen ◽  
Geert Morren ◽  
Daniel Haensse ◽  
Andrea S. Bauschatz ◽  
Hans Ulrich Bucher ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Visual Stimulation ◽  
Newborn Infants ◽  
Hemodynamic Response ◽  
Functional Near Infrared Spectroscopy

scholarly journals Parallel Factor Analysis for multidimensional decomposition of fNIRS data - A validation study

2019 ◽  
Author(s):  
Hüsser Alejandra ◽  
Caron-Desrochers Laura ◽  
Tremblay Julie ◽  
Vannasing Phetsamone ◽  
Martínez-Montes Eduardo ◽  
...  
Keyword(s):  
Near Infrared ◽  
Signal To Noise Ratio ◽  
Motion Artifact ◽  
Functional Near Infrared Spectroscopy ◽  
Cerebral Activation ◽  
Artifact Correction ◽  
Verbal Fluency Task ◽  
Time Space ◽  
Concentration Changes ◽  
Parallel Factor

AbstractSignificanceFunctional near-infrared spectroscopy (fNIRS) is a neuroimaging technique that uses near-infrared lights to estimate cerebral hemodynamic response, based on concentration changes in oxygenated and deoxygenated hemoglobin. A multi-dimensional decomposition technique, parallel factor (PARAFAC) analysis, has been validated for the identification of artifacts and cerebral activation patterns in electroencephalography and neuroimaging.AimWe aimed at introducing and validating the use of the PARAFAC model for fNIRS data analysis, which is inherently multidimensional (time, space, wavelength).ApproachEighteen healthy adults underwent fNIRS acquisition during a verbal fluency task. The signal-to-noise ratio and Pearson’s correlation were used to evaluate the efficacy of PARAFAC for motion artifact correction. Temporal, spatial and hemodynamic characteristics of the PARAFAC component allowed to identify task-related cerebral activations.ResultsMotion artifact correction with PARAFAC led to significant improvements in data quality and other advantages as compared to traditional 2D approaches (ICA, tPCA). Although PARAFAC revealed a slightly more distributed functional network, temporal and spatial characteristics of the task-related brain activation identified with PARAFAC mostly overlapped with those obtained with commonly used approaches.ConclusionThis study describes the first implementation of PARAFAC in fNIRS and supports it as a promising data-driven alternative for multi-dimensional data analyses in fNIRS.

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