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-(A) Oscillations are characterized by their frequency (the number of cycles per time unit), amplitude or power (power is amplitude squared), and instantaneous phase. Phases fluctuate between 0 and 2π. (B) Oscillatory frequency is usually represented in the number of cycles per second, in Hz. This means that an oscillation with 5 cycles per second has a frequency of 5 Hz, and an oscillation with 10 cycles per second has a frequency of 10 Hz. The last time series also illustrates the dissociation between amplitude and phase: in the second part of the series the amplitude decreases independently of the phase of the signal.
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... are characterized by three aspects: their frequency, power, and phase (see Figure 3a). ...
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... frequency of an oscillation is the number of cycles per time unit. Frequency is usually expressed as the number of cycles per second, in Hertz (Hz; see Figure 3b). In the human brain, oscillations have been measured from slow, supra-second oscillations of 0.03-0.05 ...
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... first step of phase synchrony computation therefore consists of the decomposition of the raw signals into distinct oscillations with different frequencies. As was discovered by Joseph Fourier, any signal can be expressed as a combination of sine waves, each with their own frequency, amplitude and phase (note that here, phase refers to the value of the sine wave at time=0, which is slightly different from the continuous phase angle time series shown in Figure 3a). We can therefore extract a set of oscillatory time series for each electrode and trial that isolates the frequency band-specific part of the original signal. ...
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... are characterized by three aspects: their frequency, power, and phase (see Figure 3a). ...
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... frequency of an oscillation is the number of cycles per time unit. Frequency is usually expressed as the number of cycles per second, in Hertz (Hz; see Figure 3b). In the human brain, oscillations have been measured from slow, supra-second oscillations of 0.03-0.05 ...
Context 6
... first step of phase synchrony computation therefore consists of the decomposition of the raw signals into distinct oscillations with different frequencies. As was discovered by Joseph Fourier, any signal can be expressed as a combination of sine waves, each with their own frequency, amplitude and phase (note that here, phase refers to the value of the sine wave at time=0, which is slightly different from the continuous phase angle time series shown in Figure 3a). We can therefore extract a set of oscillatory time series for each electrode and trial that isolates the frequency band-specific part of the original signal. ...
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... While inter-brain PLV is interpretable on its own and can be directly compared across conditions without a baseline (Vijver & Cohen, 2019), it was important to consider the potential synchrony between mother and child that could have emerged throughout the session due to joint task-solving. Therefore, global synchrony before feedback onset, that is during the search task, was compared to global synchrony after feedback onset to ensure that the measured effect was truly feedback related and not a byproduct of the naturally arising synchrony in the dyad. ...
Parent and child have been shown to synchronize their behaviors and physiology during social interactions. This synchrony is an important marker of their relationship quality and subsequently the child’s social and emotional development. Therefore, understanding the factors that influence parent–child synchrony is an important undertaking. Using EEG hyperscanning, this study investigated brain-to-brain synchrony in mother-child dyads when they took turns performing a visual search task and received positive or negative feedback. In addition to the effect of feedback valence, we studied how their assigned role, i.e., observing or performing the task, influenced synchrony. Results revealed that mother-child synchrony was higher during positive feedback relative to negative feedback in delta and gamma frequency bands. Furthermore, a main effect was found for role in the alpha band with higher synchrony when a child observed their mother performing the task compared to when the mother observed their child. These findings reveal that a positive social context could lead a mother and child to synchronize more on a neural level, which could subsequently improve the quality of their relationship. This study provides insight into mechanisms that underlie mother-child brain-to-brain synchrony, and establishes a framework by which the impact of emotion and task demand on a dyad’s synchrony can be investigated
Predictive coding is a famous and influential theory in the field of neuroscience. It states that instead of passively receiving the external information and forming perception or decision, the brain holds a hierarchical internal modal that could actively interact with the external stimuli. In this hierarchy, each level could predict the activation of the lower level, with the lowest level representing the outside world; while the predicted level could compute the difference between real stimulation and the prediction and send it upward to update the model for better prediction in the future. It could provide explanations for a wide range of neurophysiological and psychological phenomena, which leads to the belief that predictive coding may serve as a unifying computational framework for brain functions including sensation, perception, memory, and so on. However, the interpretation provided by predictive coding on brain function is not definite enough. More supportive evidence is needed to establish predictive coding as a unifying principle used by the brain. On one hand, researchers build computational models implementing predictive coding dynamics and check whether such a dynamic system could generate some similar results observed in the biological brain. On the other hand, efforts have been made to investigate the neural mechanisms of predictive processing in the brain. In the current thesis, we combined both sides, experimental and computational, attempting to provide more solid evidence for predictive coding as a unifying theory of brain function. Specifically, we conducted three progressive and related studies. The first one focuses on the neural activities in the biological brain, in particular, the oscillatory traveling waves. Could cortical traveling waves underlie predictive processes in the brain? Our results suggest that the ascending traveling waves only appear with the presence of bottom-up driven visual stimuli and disappear when visual inputs are absent; the descending waves, although they receive some modulation from external visual input, are less affected. We explained the results in the predictive coding framework: oscillatory traveling waves might be a neural signature of predictive coding with forward waves carrying prediction errors and backward waves transmitting prediction signals. In the second project, we utilized the deep learning technique and constructed a neural network which could implement predictive coding dynamics. If the human brain functions according to such a dynamic principle, the predictive coding network should show certain human-like properties. We tested this hypothesis with illusory contour image stimuli. The study suggests the neural network driven by predictive coding dynamics possesses illusory perception, supporting the possibility that the same dynamics strategy, i.e. predictive coding, might be shared between the network and biological brain. Based on the results of the first two studies: (i) cortical traveling waves reflecting the neural mechanisms of predictive coding in the biological brain; (ii) the predictive neural network displaying human-like performance, in the third study, we further update the same predictive neural network by adding biologically plausible time delays and constants between network layers in order to generate oscillations.[...]
Aging induces a decline in the ties that bind anatomical networks centered on the prefrontal cortex, which are critical for reinforcement learning and decision making. At the neurophysiological level, the prefrontal cortex may engage electrophysiological oscillatory synchronization to coordinate other brain systems during learning. We recorded scalp EEG from 21 older (mean age 69 years) and 20 young (mean age 22 years) healthy human adults while they learned stimulus-response mappings by trial-and-error using feedback. In young adults, theta-band (4-8 Hz) oscillatory power over medial frontal and anterior frontal cortex predicted learning after errors. Older adults demonstrated a decrease in the theta-band learning-predictive signals over medial frontal but not anterior frontal cortex. This age-related decrease in task-relevant medial frontal theta power may be related to the more general decrease in medial frontal theta power that we observed during rest. These results demonstrate a shift in cortical networks that support reinforcement learning in older adults, and shed new light on the changes in neurophysiological (oscillatory) mechanisms with neurocognitive aging.
