A spike analysis method for characterizing neurons based on phase locking and scaling to the interval between two behavioral events

2020 ◽  
Vol 124 (6) ◽  
pp. 1923-1941
Author(s):  
Masanori Kawabata ◽  
Shogo Soma ◽  
Akiko Saiki-Ishikawa ◽  
Satoshi Nonomura ◽  
Junichi Yoshida ◽  
...  
Keyword(s):  
Brain Function ◽  
Phase Locking ◽  
Neural Mechanisms ◽  
Neuron Activity ◽  
Scaling Analysis ◽  
Analysis Method ◽  
Internal Information ◽  
Spike Analysis ◽  
Functional Neuron ◽  
Phase Scaling

Phase-Scaling analysis is a novel technique to unbiasedly characterize the temporal dependency of functional neuron activity on two behavioral events and objectively determine the latency and form of the activity change. This powerful analysis can uncover several classes of latently functioning neurons that have thus far been overlooked, which may participate differently in intermediate processes of a brain function. The Phase-Scaling analysis will yield profound insights into neural mechanisms for processing internal information.

Identification of Chaos-Periodic Transitions, Band Merging, and Internal Crisis Using Wavelet-DFA Method

2016 ◽  
Vol 26 (04) ◽  
pp. 1650065 ◽  
Author(s):  
Mahsa Vaghefi ◽  
Ali Motie Nasrabadi ◽  
Seyed Mohammad Reza Hashemi Golpayegani ◽  
Mohammad Reza Mohammadi ◽  
Shahriar Gharibzadeh
Keyword(s):  
Detrended Fluctuation Analysis ◽  
Period Doubling ◽  
Nonstationary Time Series ◽  
The Self ◽  
Fluctuation Analysis ◽  
Scaling Analysis ◽  
Analysis Method ◽  
Self Similarity ◽  
Self Similar ◽  
Detrended Fluctuation

Detrended Fluctuation Analysis (DFA) is a scaling analysis method that can identify intrinsic self-similarity in any nonstationary time series. In contrast, Wavelet Transform (WT) method is widely used to investigate the self-similar processes, as the self-similarity properties exist within the subbands. Therefore, a combination of these two approaches, DFA and WPT, is promising for rigorous investigation of such a system. In this paper a new methodology, so-called wavelet DFA, is introduced and interpreted to evaluate this idea. This approach, further than identifying self-similarity properties, enable us to detect and capture the chaos-periodic transitions, band merging, and internal crisis in systems that become chaotic through period-doubling phenomena. Changes of wavelet DFA exponent have been compared with that of Lyapunov and DFA through Logistic, Sine, Gaussian, Cubic, and Quartic Maps. Furthermore, the potential capabilities of this new exponent have been presented.

Research on Attention EEG Based on the Multiscale Jensen-Shannon Divergence

2014 ◽  
Vol 529 ◽  
pp. 675-678
Author(s):  
Zheng Xia Zhang ◽  
Si Qiu Xu ◽  
Er Ning Zhou ◽  
Xiao Lin Huang ◽  
Jun Wang
Keyword(s):  
Time Series ◽  
Brain Function ◽  
Complexity Analysis ◽  
Function Parameter ◽  
Function Evaluation ◽  
Analysis Method ◽  
Eeg Signals ◽  
Eyes Closed ◽  
Statistical Complexity ◽  
Jensen Shannon Divergence

The article adopted the multiscale Jensen-Shannon Divergence analysis method for EEG complexity analysis. Then the study found that this method can distinguish between three different status (Eyes closed, count, in a daze) acquisition of EEG time series. It showed that three different states of EEG time series have significant differences. In each state of the three different states (Eyes closed, count, in a daze), we aimed at comparing and analyzing the statistical complexity of EEG time series itself and the statistical complexity of EEG time series shuffled data. It was found that there are large amounts of nonlinear time series in the EEG signals. This method is also fully proved that the multiscale JSD algorithm can be used to analyze attention EEG signals. The multiscale Jensen-Shannon Divergence statistical complexity can be used as a measure of brain function parameter, which can be applied to the auxiliary clinical brain function evaluation in the future.

scholarly journals Neural Basis for Regulation of Vasopressin Secretion by Anticipated Disturbances in Osmolality

2021 ◽  
Author(s):  
Angela Kim ◽  
Joseph C. Madara ◽  
Chen Wu ◽  
Mark L. Andermann ◽  
Bradford B. Lowell
Keyword(s):  
Water Balance ◽  
Arcuate Nucleus ◽  
Feedback Regulation ◽  
Neural Mechanisms ◽  
Neuron Activity ◽  
Neural Basis ◽  
Water Excretion ◽  
Hypothalamic Arcuate Nucleus ◽  
Vasopressin Secretion ◽  
Renal Water Excretion

AbstractWater balance, tracked by extracellular osmolality, is regulated by feedback and feedforward mechanisms. Feedback regulation is reactive, occurring as deviations in osmolality are detected. Feedforward or presystemic regulation is proactive, occurring when disturbances in osmolality are anticipated. Vasopressin (AVP) is a key hormone regulating water balance and is released during hyperosmolality to limit renal water excretion. AVP neurons are under feedback and feedforward regulation. Not only do they respond to disturbances in blood osmolality, but they are also rapidly suppressed and stimulated, respectively, by drinking and eating, which will ultimately decrease and increase osmolality. Here, we demonstrate that AVP neuron activity is regulated by multiple anatomically-and functionally-distinct neural circuits. Notably, presystemic regulation during drinking and eating are mediated by non-overlapping circuits that involve the lamina terminalis and hypothalamic arcuate nucleus, respectively. These findings reveal neural mechanisms that support differential regulation of AVP release by diverse behavioral and physiological stimuli.

scholarly journals Brain activity links performance in science reasoning with conceptual approach

2019 ◽  
Vol 4 (1) ◽  
Author(s):  
Jessica E. Bartley ◽  
Michael C. Riedel ◽  
Taylor Salo ◽  
Emily R. Boeving ◽  
Katherine L. Bottenhorn ◽  
...  
Keyword(s):  
Problem Solving ◽  
Undergraduate Students ◽  
Brain Function ◽  
Visual Motion ◽  
Brain Activity ◽  
Neural Mechanisms ◽  
Conceptual Approach ◽  
Conceptual Coherence ◽  
Science Reasoning ◽  
And Control

AbstractUnderstanding how students learn is crucial for helping them succeed. We examined brain function in 107 undergraduate students during a task known to be challenging for many students—physics problem solving—to characterize the underlying neural mechanisms and determine how these support comprehension and proficiency. Further, we applied module analysis to response distributions, defining groups of students who answered by using similar physics conceptions, and probed for brain differences linked with different conceptual approaches. We found that integrated executive, attentional, visual motion, and default mode brain systems cooperate to achieve sequential and sustained physics-related cognition. While accuracy alone did not predict brain function, dissociable brain patterns were observed when students solved problems by using different physics conceptions, and increased success was linked to conceptual coherence. Our analyses demonstrate that episodic associations and control processes operate in tandem to support physics reasoning, offering potential insight to support student learning.

scholarly journals Suppression of competing speech through entrainment of cortical oscillations

2013 ◽  
Vol 109 (12) ◽  
pp. 3082-3093 ◽  
Author(s):  
Cort Horton ◽  
Michael D'Zmura ◽  
Ramesh Srinivasan
Keyword(s):  
Cross Correlation ◽  
Neuronal Excitability ◽  
Phase Locking ◽  
Neural Mechanisms ◽  
Rhythmic Structure ◽  
Speech Stream ◽  
Cortical Oscillations ◽  
Human Adults ◽  
Competing Speech ◽  
Suppressive Mechanism

People are highly skilled at attending to one speaker in the presence of competitors, but the neural mechanisms supporting this remain unclear. Recent studies have argued that the auditory system enhances the gain of a speech stream relative to competitors by entraining (or “phase-locking”) to the rhythmic structure in its acoustic envelope, thus ensuring that syllables arrive during periods of high neuronal excitability. We hypothesized that such a mechanism could also suppress a competing speech stream by ensuring that syllables arrive during periods of low neuronal excitability. To test this, we analyzed high-density EEG recorded from human adults while they attended to one of two competing, naturalistic speech streams. By calculating the cross-correlation between the EEG channels and the speech envelopes, we found evidence of entrainment to the attended speech's acoustic envelope as well as weaker yet significant entrainment to the unattended speech's envelope. An independent component analysis (ICA) decomposition of the data revealed sources in the posterior temporal cortices that displayed robust correlations to both the attended and unattended envelopes. Critically, in these components the signs of the correlations when attended were opposite those when unattended, consistent with the hypothesized entrainment-based suppressive mechanism.

scholarly journals Stimulus background influences phase invariant coding by correlated neural activity

eLife ◽  
2017 ◽  
Vol 6 ◽  
Author(s):  
Michael G Metzen ◽  
Maurice J Chacron
Keyword(s):  
Phase Locking ◽  
Low Frequency ◽  
Weakly Electric Fish ◽  
Neuron Activity ◽  
Invariant Representation ◽  
Communication Signals ◽  
Correlated Activity ◽  
Peripheral Afferents ◽  
And Behavior ◽  
Weakly Electric

Previously we reported that correlations between the activities of peripheral afferents mediate a phase invariant representation of natural communication stimuli that is refined across successive processing stages thereby leading to perception and behavior in the weakly electric fish Apteronotus leptorhynchus (Metzen et al., 2016). Here, we explore how phase invariant coding and perception of natural communication stimuli are affected by changes in the sinusoidal background over which they occur. We found that increasing background frequency led to phase locking, which decreased both detectability and phase invariant coding. Correlated afferent activity was a much better predictor of behavior as assessed from both invariance and detectability than single neuron activity. Thus, our results provide not only further evidence that correlated activity likely determines perception of natural communication signals, but also a novel explanation as to why these preferentially occur on top of low frequency as well as low-intensity sinusoidal backgrounds.

scholarly journals Brain activity links performance in science reasoning with conceptual approach

2019 ◽  
Author(s):  
Jessica E. Bartley ◽  
Michael C. Riedel ◽  
Taylor Salo ◽  
Emily R. Boeving ◽  
Katherine L. Bottenhorn ◽  
...  
Keyword(s):  
Undergraduate Students ◽  
Brain Function ◽  
Visual Motion ◽  
Classroom Practices ◽  
Brain Activity ◽  
Neural Mechanisms ◽  
Conceptual Approach ◽  
Science Reasoning ◽  
And Control ◽  
Insight Into

ABSTRACTUnderstanding how students learn is crucial for helping them succeed. We examined brain function in 107 undergraduate students during a task known to be challenging for many students – physics problem solving – to characterize underlying neural mechanisms and determine how these support comprehension and proficiency. Further, we applied module analysis to response distributions, defining groups of students who answered using similar physics conceptions, and probed for brain differences linked with different conceptual approaches. We found integrated executive, attentional, visual motion, and default mode brain systems cooperate to achieve sequential and sustained physics-related cognition. While accuracy alone did not predict brain function, dissociable brain patterns were observed when students solved problems using different physics conceptions, and increased success was linked to conceptual coherence. Our analyses demonstrate that episodic associations and control processes operate in tandem to support physics reasoning, offering insight into effective classroom practices to promote student success.

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