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Related Concept Videos

Determination of Expected Frequency01:08

Determination of Expected Frequency

Suppose one wants to test independence between the two variables of a contingency table. The values in the table constitute the observed frequencies of the dataset. But how does one determine the expected frequency of the dataset? One of the important assumptions is that the two variables are independent, which means the variables do not influence each other. For independent variables, the statistical probability of any event involving both variables is calculated by multiplying the individual...
IR Frequency Region: Fingerprint Region01:03

IR Frequency Region: Fingerprint Region

IR spectra are divided into two main regions: the diagnostic region and the fingerprint region. The diagnostic region of the spectrum lies above 1500 cm−1. The absorptions resulting from single-bond vibrations of the N–H, C–H, and O–H stretch at higher wavenumbers and appear on the left side of the spectrum. The stretching absorptions of the C≡C and C≡N occur between 2100–2300 cm−1. In contrast, those arising from stretching absorptions of the C=O, C=N, and C=C occur between 1600–1850 cm−1.
The...
Difference from Background: Limit of Detection01:05

Difference from Background: Limit of Detection

The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
The LOD indicates the presence or absence...
Expected Frequencies in Goodness-of-Fit Tests01:19

Expected Frequencies in Goodness-of-Fit Tests

A goodness-of-fit test is conducted to determine whether the observed frequency values are statistically similar to the frequencies expected for the dataset. Suppose the expected frequencies for a dataset are equal such as when predicting the frequency of any number appearing when casting a die. In that case, the expected frequency is the ratio of the total number of observations (n) to the number of categories (k).
IR Spectrum Peak Splitting: Symmetric vs Asymmetric Vibrations01:08

IR Spectrum Peak Splitting: Symmetric vs Asymmetric Vibrations

Identical bonds within a polyatomic group can stretch symmetrically (in-phase) or asymmetrically (out-of-phase). Similar to hydrogen bonding, these vibrations also influence the shape of the IR peak. Generally, asymmetric stretching frequencies are higher than symmetric stretching frequencies. For example, primary amines exhibit two distinct IR peaks between 3300–3500 cm−1 corresponding to the symmetric and asymmetric N-H stretching, while secondary amines exhibit a single stretching vibration...
¹H NMR: Interpreting Distorted and Overlapping Signals01:02

¹H NMR: Interpreting Distorted and Overlapping Signals

Spin systems where the difference in chemical shifts of the coupled nuclei is greater than ten times J are called first-order spin systems. These nuclei are weakly coupled, and their chemical shifts and coupling constant can generally be estimated from the well-separated signals in the spectrum.
As Δν decreases and the signals move closer, the doublets appear increasingly distorted. The intensities of the inner lines increase at the cost of those of the outer lines as the signals are slanted or...

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Related Experiment Video

Updated: May 11, 2026

How to Calculate and Validate Inter-brain Synchronization in a fNIRS Hyperscanning Study
05:33

How to Calculate and Validate Inter-brain Synchronization in a fNIRS Hyperscanning Study

Published on: September 8, 2021

Robust gamma coherence between macaque V1 and V2 by dynamic frequency matching.

Mark J Roberts1, Eric Lowet, Nicolas M Brunet

  • 1Donders Institute for Brain, Behavior and Cognition, Radboud University, 6500 HC Nijmegen, the Netherlands. mark.roberts@maastrichtuniversity.nl

Neuron
|May 14, 2013
PubMed
Summary

Neural communication via gamma band oscillations is possible even when brain regions operate at different frequencies. This study demonstrates sustained gamma coherence between V1 and V2 despite frequency fluctuations, supporting flexible neural communication.

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Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time
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Published on: July 1, 2014

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Last Updated: May 11, 2026

How to Calculate and Validate Inter-brain Synchronization in a fNIRS Hyperscanning Study
05:33

How to Calculate and Validate Inter-brain Synchronization in a fNIRS Hyperscanning Study

Published on: September 8, 2021

Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time
07:12

Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time

Published on: July 1, 2014

Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Systems Neuroscience

Background:

  • Gamma band oscillations (30-80 Hz) are theorized to facilitate communication between distant neural populations.
  • Variability in gamma frequency with stimulus changes and over time challenges the fixed-frequency model for neural synchronization.

Purpose of the Study:

  • To investigate whether gamma coherence between visual areas V1 and V2 is maintained despite stimulus-dependent and time-varying gamma frequencies.
  • To determine if neural communication via gamma coherence can operate flexibly without a fixed-frequency channel.

Main Methods:

  • Simultaneous electrophysiological recordings from macaque V1 and V2.
  • Presentation of visual gratings with varying contrast levels.
  • Analysis of gamma frequency and coherence dynamics in relation to stimulus parameters and time.

Main Results:

  • Gamma frequency increased with stimulus contrast in both V1 and V2, yet V1-V2 gamma coherence remained consistent across contrasts.
  • Gamma frequency fluctuations (∼15 Hz) during constant stimulation were highly correlated between V1 and V2.
  • Strongest coherence patterns exhibited layer-specificity, aligning with known feedforward anatomical connections.

Conclusions:

  • Gamma coherence between remote neural populations is robust and can be maintained despite significant changes in gamma frequency.
  • Neural communication through gamma coherence does not require a stimulus-independent, fixed-frequency channel, allowing for flexible information processing.