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Lessons learned from an fMRI-guided rTMS study on performance in a numerical Stroop task.

Lysianne Beynel1, Hannah Gura1,2, Zeynab Rezaee1

  • 1Noninvasive Neuromodulation Unit, Experimental Therapeutics Branch, Intramural Research Program, National Institute of Mental Health, Bethesda, Maryland, United States of America.

Plos One
|May 6, 2024
PubMed
Summary
This summary is machine-generated.

Advanced computational modeling revealed that transcranial magnetic stimulation (TMS) over the intraparietal sulcus (IPS) impacts decision-making processes during the numerical Stroop task. This highlights the utility of sophisticated analysis for understanding brain stimulation effects.

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Area of Science:

  • Cognitive Neuroscience
  • Neuroimaging
  • Computational Psychiatry

Background:

  • The Stroop task is a standard method for studying how conflicting information affects decision-making.
  • Previous research, including neuroimaging and repetitive transcranial magnetic stimulation (rTMS) studies, has implicated the intraparietal sulcus (IPS) in the Stroop task.
  • Individualized functional magnetic resonance imaging (fMRI) targeting has previously shown superior results for rTMS in the numerical Stroop task.

Purpose of the Study:

  • To investigate if fMRI-guided rTMS effects on numerical Stroop task performance persist with modern techniques.
  • To compare traditional reaction time analysis with advanced drift diffusion modeling in assessing rTMS efficacy.
  • To explore the impact of rTMS on perceptual decision-making using advanced computational methods.

Main Methods:

  • Fifteen participants completed the numerical Stroop task.
  • Active or sham 10 Hz rTMS was applied over the right IPS, identified via individualized fMRI.
  • Performance was analyzed using traditional reaction time measures and drift diffusion modeling.

Main Results:

  • Standard reaction time analysis showed no significant difference between active and sham rTMS, potentially due to practice effects or cumulative stimulation.
  • Drift diffusion modeling revealed a significant effect of rTMS on the drift rate, indicating altered processing of congruent and incongruent stimuli.
  • Advanced computational modeling successfully elucidated rTMS effects on brain function where simpler analyses failed.

Conclusions:

  • While traditional analyses may not detect subtle effects, advanced computational modeling can reveal rTMS impacts on cognitive processes.
  • The study demonstrates that rTMS over the IPS influences decision-making parameters in the numerical Stroop task.
  • This underscores the importance of sophisticated analytical approaches in neurostimulation research for a deeper understanding of brain function.