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Related Concept Videos

Variability: Analysis01:11

Variability: Analysis

Measures of variability are statistical metrics that reveal the dispersion pattern within a dataset. They are pivotal in biostatistics, providing insights into the heterogeneity within health and biological data. Variability signifies the degree to which data points diverge from one another, helping researchers understand the potential range of values and associated uncertainty within the data.
The range is a simple measure of variability, indicating the difference between the highest and...
Brain Imaging01:14

Brain Imaging

Brain imaging technologies provide critical insights into both the structure and function of the human brain, enabling medical professionals and researchers to diagnose, study, and treat neurological disorders or psychiatric disorders more effectively.
These technologies include computerized axial tomography (CAT or CT scans), positron-emission tomography (PET scans),  magnetic resonance imaging (MRI),  functional magnetic resonance imaging (fMRI), and Transcranial Magnetic Stimulation (TMS).

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Related Experiment Video

Updated: May 10, 2026

Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy
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Published on: June 27, 2013

Brain signal variability is parametrically modifiable.

Douglas D Garrett1, Anthony R McIntosh2, Cheryl L Grady3

  • 1Max Planck Society-University College London Initiative for Computational Psychiatry and Ageing Research (ICPAR), Center for Lifespan Psychology, Max Planck Institute for Human Development, 14195 Berlin, Germany.

Cerebral Cortex (New York, N.Y. : 1991)
|June 11, 2013
PubMed
Summary
This summary is machine-generated.

Brain signal variability dynamically adjusts to task difficulty, unlike average brain signals. Reductions in this neural variability predict poorer task performance, highlighting its importance for understanding brain function.

Keywords:
brain signal variabilityfMRIface processingnoise

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Area of Science:

  • Neuroscience
  • Cognitive Neuroscience
  • Neuroimaging

Background:

  • Moment-to-moment brain signal variability is a fundamental neural property.
  • While heightened variability may support efficient neural function, its response to environmental demands is unclear.
  • Existing research often focuses on mean brain signals, overlooking dynamic variability.

Purpose of the Study:

  • To investigate if brain signal variability changes with incremental task difficulty.
  • To determine if signal variability, distinct from mean signals, predicts task performance.
  • To explore the relationship between signal variance and signal means in brain activity.

Main Methods:

  • Utilized functional magnetic resonance imaging (fMRI) data from a parametric face processing task.
  • Employed multivariate and mixed-effects modeling to analyze brain signal variability.
  • Examined over 2 million data points across voxels, subjects, and conditions.

Main Results:

  • Within-person brain signal variability significantly responded to incremental task difficulty adjustments.
  • Difficulty-related decreases in signal variability predicted reduced accuracy and longer reaction times.
  • No significant relationship was found between voxel signal variances and means.

Conclusions:

  • Brain signal variability is a dynamic, task-driven metric crucial for understanding neural function.
  • Signal variability offers insights into cognitive processes that mean signals do not capture.
  • This study establishes brain signal variability as a key indicator of cognitive state and performance.