Sleep Is a Refreshing Process: An fNIRS Study

Öniz, Adile; İnanç, Gonca; Taşlıca, Serhat; Güdücü, Çağdaş; Özgören, Murat

doi:10.3389/fnhum.2019.00160

Cited by 11 publications

(16 citation statements)

References 24 publications

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“…In addition, the proposed configuration can be employed for other applications of NIR spectroscopy signal in the areas of mental workload detection, sleep, brain-computer interface, biofeedback, and diagnosis of some mental diseases especially in infants and neonates. [80][81][82][83][84][85][86][87][88][89][90][91][92][93]…”

Section: Discussionmentioning

confidence: 99%

Simulation and in vivo investigation of light-emitting diode, near infrared Gaussian beam profiles

Mirbagheri

Hakimi

Ebrahimzadeh

et al. 2019

Journal of Near Infrared Spectroscopy

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Near infrared spectroscopy is an optical imaging technique which offers a non-invasive, portable, and low-cost method for continuously measuring the oxygenation of tissues. In particular, it can provide the brain activation through measuring the blood oxygenation and blood volume in the cortex. Understanding and then improving the spatial and depth sensitivity of near infrared spectroscopy measurements to brain tissue are essential for designing experiments as well as interpreting research findings. In this study, we investigate the effect of applying two common light beam profiles including Uniform and Gaussian on the penetration depth of an LED-based near infrared spectroscopy. In this regard, two Gaussian profiles were produced by adjusting plano-convex and bi-convex lenses and the Uniform profile was provided by applying a flat lens. Two experiments were conducted in this study. First, a simulation experiment was carried out based on scanning the intra space of a liquid phantom by using static and pulsating absorbers to compare the penetration depth of the configurations applied on the LED-based near infrared spectroscopy with that of a laser-based near infrared spectroscopy. Second, to show the feasibility of the best proposed configuration applied, an in vivo experiment of stress assessment has been performed and its results have been compared with that results obtained by laser one. The results showed that the LED-based near infrared spectroscopy equipped with bi-convex lens provides a penetration depth and hence quality measurements of near infrared spectroscopy and its extracted heart rate variability signals as well as laser-based near infrared spectroscopy especially in the application of stress assessment.

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Section: Discussionmentioning

confidence: 99%

Simulation and in vivo investigation of light-emitting diode, near infrared Gaussian beam profiles

Mirbagheri

Hakimi

Ebrahimzadeh

et al. 2019

Journal of Near Infrared Spectroscopy

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“…Sleep stages are NREM sleep and rapid eye movement (REM) sleep. NREM has further three stages: N1, N2, and N3 representing light sleep, medium sleep, and deep sleep, respectively (Ahn et al, 2016;Oniz et al, 2019). The sleep cycle starts when wakefulness is followed by NREM stages and REM sleep (Van Wyk et al, 2019;Chi et al, 2020).…”

Section: Sleep Stage-based Threshold Circlesmentioning

confidence: 99%

“…The main feature of drowsiness is slow rolling eye movements (SREM), which are associated with the N1 stage or light sleep. fNIRS studies have investigated brain hemodynamics during various sleep stages and discussed CORE dynamics related to sleep (Oniz et al, 2019). It is conceived that there is a relationship between hemodynamics of sleep stages and W. If CORE dynamics of W is known, then CORE of sleep stages can be deduced from it according to a somewhat fixed relationship as shown in (10).…”

Section: Sleep Stage-based Threshold Circlesmentioning

confidence: 99%

“…The brain experiences HerOx as well as HyCap CORE states, and the VPA trajectory mostly remains in phases 7 and 8 of the vector phase diagram (fourth quadrant). It has been noted that transitioning from wakefulness to sleep increases the oxygen levels in the blood (Khero et al, 2019;Oniz et al, 2019), so HbO rises as well as COE falls gradually. Eventually, the brain has higher HbO and lower HbR levels by the end of sleep (Oniz et al, 2019).…”

Section: Vector Phase Analysis Trajectory Pattern For Drowsiness Detectionmentioning

confidence: 99%

“…Studies have shown rapid and increased brain activity in the DPFC region under brain state transitions from wakefulness to non-rapid eye movement (NREM) sleep stages (Khan and Hong, 2015;Bernardi et al, 2018). This results in increased concentrations of HbO and decreases in COE, which indicates sleep as a refreshing process (Khero et al, 2019;Oniz et al, 2019). During driving, these rapid changeovers between sleep stages (as experienced when a person is consistently nodding off) could be devastating and must be recorded at an earlier stage to avoid life losses (Fonseca et al, 2018;Dai et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

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Vector Phase Analysis Approach for Sleep Stage Classification: A Functional Near-Infrared Spectroscopy-Based Passive Brain–Computer Interface

Arif¹,

Khan²,

Naseer³

et al. 2021

Front. Hum. Neurosci.

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A passive brain–computer interface (BCI) based upon functional near-infrared spectroscopy (fNIRS) brain signals is used for earlier detection of human drowsiness during driving tasks. This BCI modality acquired hemodynamic signals of 13 healthy subjects from the right dorsolateral prefrontal cortex (DPFC) of the brain. Drowsiness activity is recorded using a continuous-wave fNIRS system and eight channels over the right DPFC. During the experiment, sleep-deprived subjects drove a vehicle in a driving simulator while their cerebral oxygen regulation (CORE) state was continuously measured. Vector phase analysis (VPA) was used as a classifier to detect drowsiness state along with sleep stage-based threshold criteria. Extensive training and testing with various feature sets and classifiers are done to justify the adaptation of threshold criteria for any subject without requiring recalibration. Three statistical features (mean oxyhemoglobin, signal peak, and the sum of peaks) along with six VPA features (trajectory slopes of VPA indices) were used. The average accuracies for the five classifiers are 90.9% for discriminant analysis, 92.5% for support vector machines, 92.3% for nearest neighbors, 92.4% for both decision trees, and ensembles over all subjects’ data. Trajectory slopes of CORE vector magnitude and angle: m(|R|) and m(∠R) are the best-performing features, along with ensemble classifier with the highest accuracy of 95.3% and minimum computation time of 40 ms. The statistical significance of the results is validated with a p-value of less than 0.05. The proposed passive BCI scheme demonstrates a promising technique for online drowsiness detection using VPA along with sleep stage classification.

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Adenosinergic modulation of layer 6 microcircuitry in the medial prefrontal cortex is specific to presynaptic cell type

Ding,

Yang,

Feldmeyer

2023

Preprint

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Adenosinergic modulation in the PFC is recognised for its involvement in various behavioural aspects including sleep homoeostasis, decision-making, spatial working memory and anxiety. While the principal cells of layer 6 (L6) exhibit significant morphological diversity, the detailed cell-specific regulatory mechanisms of adenosine in L6 remain unexplored. Here, we quantitatively analysed the morphological and electrophysiological parameters of L6 neurons in the rat medial prefrontal cortex (mPFC) using whole-cell recordings combined with morphological reconstructions. We were able to identify two different morphological categories of excitatory neurons, those with a leading dendrite that was oriented either upright (towards the dial surface) or inverted (towards the white matter). These two excitatory neuron subtypes exhibited different electrophysiological and synaptic properties. Adenosine at a concentration of 30 µM indiscriminately suppressed connections with either an upright or an inverted presynaptic excitatory neuron. However, using lower concentrations of adenosine (10 µM) revealed that synapses originating from L6 upright neurons have a higher sensitivity to adenosine-induced inhibition of synaptic release. Adenosine receptor activation causes a reduction in the probability of presynaptic neurotransmitter release that could be abolished by specifically blocking A1adenosine receptors (A1ARs) using 8-cyclopentyltheophylline (CPT). Our results demonstrate a differential expression level of A1ARs at presynaptic sites of two functionally and morphologically distinct subpopulations of L6 principal neurons, suggesting that they may play distinct roles in the maintenance of sleep homoeostasis by adenosine.

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Sleep Is a Refreshing Process: An fNIRS Study

Cited by 11 publications

References 24 publications

Simulation and in vivo investigation of light-emitting diode, near infrared Gaussian beam profiles

Simulation and in vivo investigation of light-emitting diode, near infrared Gaussian beam profiles

Vector Phase Analysis Approach for Sleep Stage Classification: A Functional Near-Infrared Spectroscopy-Based Passive Brain–Computer Interface

Adenosinergic modulation of layer 6 microcircuitry in the medial prefrontal cortex is specific to presynaptic cell type

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