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Related Concept Videos

Frequency-dependent Selection01:21

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When the fitness of a trait is influenced by how common it is (i.e., its frequency) relative to different traits within a population, this is referred to as frequency-dependent selection. Frequency-dependent selection may occur between species or within a single species. This type of selection can either be positive—with more common phenotypes having higher fitness—or negative, with rarer phenotypes conferring increased fitness.
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A frequency is the number of times a value of the data occurs. The sum of all the frequency values represents the total number of students included in the sample. It is commonly used to group data of quantitative types. Frequency distributions can be displayed in a table, histogram, line graph, dot plot, or pie chart, just to name a few. A histogram is a graphical representation of tabulated frequencies, shown as adjacent rectangles, erected over discrete intervals (bins), with an area equal to...
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Sometimes, data gathered from an experiment on a large sample or population are organized into concise tables. In such cases, the frequency of the quantitative data set is plotted in the form of a table. Or else, the data values are grouped into the quantity’s intervals, which form classes, and their respective frequencies are known. That is, the data values are distributed over different categories or classes. This is known as frequency distribution.
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Wave summation
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Related Experiment Video

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PIPEMAT-RS: Development and Validation of a Standardized MATLAB Pipeline for Resting-State EEG Preprocessing
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Beware detrending: Optimal preprocessing pipeline for low-frequency fluctuation analysis.

Michael Woletz1, André Hoffmann1, Martin Tik1

  • 1Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria.

Human Brain Mapping
|November 16, 2018
PubMed
Summary
This summary is machine-generated.

Optimizing resting-state fMRI preprocessing is crucial. Full artifact reduction minimizes variance, but polynomial detrending benefits ALFF, not fALFF, for robust brain activity analysis.

Keywords:
ALFFartefact reductiondetrendingfALFFfMRIlow-frequency fluctuationnuisance regressionpreprocessingresting state

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Area of Science:

  • Neuroimaging
  • Neuroscience
  • Brain Function Analysis

Background:

  • Resting-state functional magnetic resonance imaging (rs-fMRI) assesses brain function without tasks.
  • rs-fMRI is sensitive to non-neural signal fluctuations, necessitating robust preprocessing.
  • Artifacts from motion and physiology can contaminate rs-fMRI data.

Purpose of the Study:

  • To investigate the impact of various preprocessing strategies on amplitude of low-frequency fluctuations (ALFF) and fractional ALFF (fALFF).
  • To evaluate test-retest variance and bias correction effects across different preprocessing schemes.
  • To provide recommendations for optimal rs-fMRI preprocessing for ALFF and fALFF analysis.

Main Methods:

  • Applied 16 artifact reduction schemes using nuisance regression to 569 rs-fMRI datasets across 1.5 T, 3 T, and 7 T.
  • Analyzed the effects of polynomial detrending and normalization on ALFF and fALFF.
  • Introduced and evaluated a novel measure, high-frequency ALFF (hfALFF).

Main Results:

  • Full artifact reduction decreased test-retest variance by up to 50%.
  • Polynomial detrending improved group-level t-values for ALFF but negatively impacted fALFF due to normalization.
  • Optimized preprocessing, excluding polynomial detrending for fALFF, increased grey matter group-level t-values by up to 60%.

Conclusions:

  • Recommend full nuisance regression for ALFF analysis, including polynomial detrending.
  • Advise against polynomial detrending for fALFF analysis due to normalization effects.
  • Optimized preprocessing strategies enhance the sensitivity of rs-fMRI measures for detecting brain activity differences.