Brain signatures indexing variation in internal processing during perceptual decision-making.

Area of Science:

• Neuroscience
• Cognitive Science
• Computational Neuroscience

Background:

• Brain activity exhibits significant trial-to-trial variability during cognitive tasks.
• Characterizing this variability and its link to internal processes often relies on external experimental manipulations.
• Internal processing changes can occur independently of experimental conditions, posing a challenge for traditional methods.

Purpose of the Study:

• To develop and apply a data-driven method for identifying consistent spatial-temporal electroencephalography (EEG) activity patterns across individual trials.
• To investigate the relationship between identified brain activity patterns and behavioral performance in a motion discrimination task.
• To explore how endogenous brain state variability influences decision-making processes.

Main Methods:

• Utilized a data-driven clustering method based on modularity maximization to analyze EEG data.
• Applied the method to electroencephalography (EEG) data from 25 subjects performing a motion discrimination task with varying coherence levels.
• Employed computational modeling to interpret the characteristics of identified brain activity subtypes.

Main Results:

• Clustering identified two distinct subtypes of trials with differing EEG activity patterns.
• Subtype 1, associated with faster response times, was unexpectedly more frequent in trials with lower motion coherence.
• Computational modeling indicated that Subtype 1 trials were characterized by a lower decision threshold.

Conclusions:

• Across-trial variability in brain activity reflects endogenous decision processes not always apparent through experimental manipulation.
• The developed clustering method effectively identifies distinct brain states influencing cognitive behavior.
• This approach offers a valuable tool for studying brain state variability relevant to cognition and behavior.