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Related Concept Videos

Sampling Theorem01:15

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In signal processing, the analysis of continuous-time signals, denoted as x(t), often involves sampling techniques to convert these signals into discrete-time signals. This process is essential for digital representation and manipulation. A critical component in sampling is the train of impulses, characterized by the sampling interval and the sampling frequency. The relationship between these parameters and the original signal's properties dictates the success of the sampling process.
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Accurate signal sampling and reconstruction are crucial in various signal-processing applications. A time-domain signal's spectrum can be revealed using its Fourier transform. When this signal is sampled at a specific frequency, it results in multiple scaled replicas of the original spectrum in the frequency domain. The spacing of these replicas is determined by the sampling frequency.
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Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...
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Related Experiment Video

Updated: Apr 27, 2026

Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment
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Increasing fMRI sampling rate improves Granger causality estimates.

Fa-Hsuan Lin1, Jyrki Ahveninen2, Tommi Raij2

  • 1Institute of Biomedical Engineering, National Taiwan University, Taipei, Taiwan; Department of Biomedical Engineering and Computational Science, Aalto University, Espoo, Finland.

Plos One
|June 27, 2014
PubMed
Summary
This summary is machine-generated.

High temporal resolution functional magnetic resonance imaging (fMRI) significantly improves Granger causality analysis for detecting brain connectivity. This advancement aids in understanding functional brain networks underlying cognition and behavior.

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Area of Science:

  • Neuroscience
  • Cognitive Science
  • Biomedical Engineering

Background:

  • Understanding brain networks requires estimating causal interactions between brain areas.
  • Granger causality analysis quantifies directional information flow using time series data.

Purpose of the Study:

  • To investigate the impact of increased temporal sampling rate in fMRI on Granger causality analysis.
  • To determine if high-temporal resolution data improves the detection of causal brain connectivity.

Main Methods:

  • Utilized whole-head inverse imaging (InI) fMRI with a 100-ms sampling rate.
  • Performed Granger causality analysis on both high-resolution and downsampled (2-s) fMRI data.
  • Conducted control analysis using SINC interpolation on downsampled data.

Main Results:

  • Granger causality analysis successfully detected expected causal relations with 100-ms resolution InI data.
  • Causal relations were undetectable when fMRI data was downsampled to 2-s resolution.
  • Control analysis confirmed that increased time-series length alone did not explain the improvements.

Conclusions:

  • High-temporal resolution fMRI data significantly enhances Granger causality connectivity analysis.
  • Increased sampling rates are crucial for accurately estimating functional brain networks.
  • This method offers improved insights into brain function and behavior.