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Variability: Analysis01:11

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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...
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Reliability and validity are two important considerations that must be made with any type of data collection. Reliability refers to the ability to consistently produce a given result. In the context of psychological research, this would mean that any instruments or tools used to collect data do so in consistent, reproducible ways.
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Reliability of variability and complexity measures for task and task-free BOLD fMRI.

Maren H Wehrheim1,2,3, Joshua Faskowitz4, Anna-Lena Schubert5

  • 1Department of Psychology, Goethe University Frankfurt, Frankfurt, Germany.

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Brain signal variability and complexity measures are reliable for studying individual differences. Temporal variability in global functional connectivity offers a robust new method for quantifying brain dynamics.

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Area of Science:

  • Neuroscience
  • Cognitive Science
  • Brain Imaging

Background:

  • Brain activity exhibits continuous temporal fluctuations, even in controlled states.
  • Understanding these variations is crucial for developmental, individual differences, and clinical research.
  • The psychometric reliability of brain signal variability and complexity measures is not well-established.

Purpose of the Study:

  • To evaluate the reliability of brain signal variability and complexity measures using resting-state and task-based fMRI data.
  • To assess the suitability of these measures for individual differences and longitudinal research.
  • To investigate the impact of region size and scan length on reliability.

Main Methods:

  • Examined split-half and test-retest reliability of BOLD fMRI data (resting-state and task-based).
  • Calculated temporal variability (e.g., standard deviation) and complexity (e.g., entropy) measures.
  • Analyzed time-resolved functional connectivity and assessed reliability across different brain regions and parcellation strategies.

Main Results:

  • Good to excellent split-half reliability for temporal variability measures from activation time series.
  • Moderate test-retest reliability for temporal variability measures under resting conditions.
  • Moderate to good reliability for complexity measures from activation time series; poor reliability for complexity measures from functional connectivity.
  • Global measures and larger subcortical regions showed higher reliability than region-specific or small region measures.
  • Reliability was only minorly affected by scan length.

Conclusions:

  • Variability and complexity of BOLD activation time series are reliable measures for individual differences research.
  • Temporal variability of global functional connectivity is a novel and robust method for quantifying brain dynamics.
  • These findings support the use of BOLD signal variability and complexity in neuroimaging research.