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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

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Data-driven region-of-interest selection without inflating Type I error rate.

Joseph L Brooks^1,2, Alexia Zoumpoulaki^2,3, Howard Bowman^2,3,4

¹School of Psychology, University of Kent, Canterbury, UK.

Psychophysiology

|December 22, 2016

Summary

This summary is machine-generated.

Data-driven methods for selecting regions of interest (ROIs) in neuroscience data can increase study power. The aggregate grand average from trials (AGAT) method is often safe, but avoid it when noise differs between conditions.

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Area of Science:

Neuroscience
Cognitive Neuroscience
Data Analysis

Background:

Researchers analyzing large neuroscience datasets like event-related potentials (ERPs) select regions of interest (ROIs).
ROI selection methods significantly impact study conclusions, potentially leading to missed effects or false positives.
Traditional methods rely on a priori hypotheses or independent data, which can lack sensitivity to experiment-specific variations.

Purpose of the Study:

To evaluate the power and validity of data-driven ROI selection methods in neuroscience.
To compare data-driven approaches against traditional methods for ROI localization.
To identify conditions under which data-driven ROI selection, specifically using the aggregate grand average from trials (AGAT), is reliable.

Main Methods:

Simulations of simple event-related potential (ERP) experiments were used to assess ROI selection strategies.
The study compared the statistical power of data-driven ROI selection against a priori or independent ROI selection.
The aggregate grand average from trials (AGAT) method was specifically analyzed for its performance and limitations.

Main Results:

Data-driven ROI selection demonstrated greater statistical power compared to a priori or independent methods in simulations.
The AGAT method proved safe for ROI selection in many scenarios, despite using the analyzed data.
However, the AGAT method inflated Type I error rates when significant noise differences existed between experimental conditions.

Conclusions:

Data-driven ROI selection can enhance the power of neuroscience studies.
The AGAT method is a viable data-driven approach for ROI selection under specific assumptions.
Researchers and reviewers should carefully consider the assumptions and potential pitfalls of data-driven ROI methods, especially concerning noise differences between conditions.