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The confidence coefficient is also known as the confidence level or degree of confidence. It is the percent expression for the probability, 1-α, that the confidence interval contains the true population parameter assuming that the confidence interval is obtained after sufficient unbiased sampling; for example, if the CL = 90%, then in 90 out of 100 samples the interval estimate will enclose the true population parameter. Here α is the area under the curve, distributed equally under...
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An unbiased point estimate is often insufficient to predict a population estimate, such as population mean or population proportion. In this scenario, a confidence interval is used. A confidence interval is an estimate similar to a  sample proportion. However, unlike the point estimate which is a single value, the confidence interval  contains a range of values. These values have lower and upper limits, known as confidence limits, and can be designated as L1 and L2, respectively.
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Language

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Language is a unique communication system that uses words and systematic rules to organize and transmit information. Unlike other forms of communication, which may involve postures, movements, odors, or vocalizations, language relies on symbols and grammar. This makes human communication distinct from that of other species, who also communicate but do not use language in the same way humans do.
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Uncertainty: Confidence Intervals00:54

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The confidence interval is the range of values around the mean that contains the true mean. It is expressed as a probability percentage. The interpretation of a 95% confidence interval, for instance, is that the statistician is 95% confident that the true mean falls within the interval. The upper and lower limits of this range are known as confidence limits. The confidence limits for the true mean are estimated from the sample's mean, the standard deviation, and the statistical factor...
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A confidence interval is a better estimate of the population than a point estimate, as it uses a range of values from a sample instead of a single value.
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A point estimate of the population mean is obtained from a single sample. Such a point estimate does not represent a population well because it needs to account for variability in the population. Single point estimate can also be biased despite the sample being selected randomly. Thus, a point estimate is often unreliable. A confidence interval is needed to reduce this unreliability.
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Quantifying the Confidence in fMRI-Based Language Lateralisation Through Laterality Index Deconstruction.

Martin Wegrzyn1, Markus Mertens2, Christian G Bien2

  • 1Department of Psychology, Bielefeld University, Bielefeld, Germany.

Frontiers in Neurology
|July 6, 2019
PubMed
Summary

This study introduces a new method for analyzing fMRI data to determine language lateralisation in epilepsy patients, improving diagnostic accuracy and identifying inconclusive cases. The approach enhances presurgical planning by providing more reliable language mapping results.

Keywords:
cerebral dominanceepilepsy surgeryfunctional MRIlanguagelateralisation

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Area of Science:

  • Neuroscience
  • Medical Imaging
  • Cognitive Science

Background:

  • Language lateralisation is crucial for epilepsy presurgical diagnosis.
  • Task-based functional MRI (fMRI) is used to assess language lateralisation.
  • Current methods for calculating the laterality index (LI) are sensitive to thresholds and regions of interest, impacting diagnostic utility.

Purpose of the Study:

  • To develop and validate a data-driven approach for calculating the laterality index (LI) from fMRI data in epilepsy patients.
  • To improve the accuracy and reliability of language lateralisation assessment in presurgical planning.
  • To establish a robust classification scheme for translating continuous LI values into categorical decisions.

Main Methods:

  • Analysis of fMRI data from 712 epilepsy patients performing a verbal fluency task.
  • Utilisation of data-driven methods to define activity thresholds and regions of interest for LI computation.
  • Development of a classification scheme to translate LI values into language lateralisation categories (left, right, bilateral, inconclusive).
  • Deconstruction of LI into laterality (L-R) and strength (L+R) measures to model activation strength and data set conclusiveness.

Main Results:

  • The developed approach achieved 91% accuracy on conclusive data and 82% accuracy when including inconclusive data in a held-out dataset.
  • The method generalised to predict language Wada test results with significant above-chance accuracy.
  • Compared to existing methods, this approach improved the identification and exclusion of inconclusive fMRI cases, ensuring high accuracy for remaining data.

Conclusions:

  • The proposed data-driven method enhances the assessment of fMRI data for language lateralisation in epilepsy patients.
  • This approach supports clinicians in determining the certainty of lateralisation or the need for additional diagnostic information.
  • Improved accuracy and handling of inconclusive cases contribute to more reliable presurgical planning for epilepsy patients.