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Brain Imaging

Brain Imaging

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These technologies include computerized axial tomography (CAT or CT scans), positron-emission tomography (PET scans), magnetic resonance imaging (MRI), functional magnetic resonance imaging (fMRI), and Transcranial Magnetic...

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Structural And Functional Connectivity Predict The Effects Of Direct Brain Stimulation On Memory.

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Structural And Functional Connectivity Predict The Effects Of Direct Brain Stimulation On Memory.

Related Experiment Video

Transcranial Direct Current Stimulation and Simultaneous Functional Magnetic Resonance Imaging

Transcranial Direct Current Stimulation and Simultaneous Functional Magnetic Resonance Imaging

Published on: April 27, 2014

Structural and Functional Connectivity Predict the Effects of Direct Brain Stimulation on Memory.

Qirui Zhang¹, Youssef Ezzyat², Ruoyi Cao¹

¹Farber Institute for Neuroscience, Department of Neurology, Thomas Jefferson University; Philadelphia, PA, USA.

Biorxiv : the Preprint Server for Biology

|March 27, 2026

View abstract on PubMed

Summary

This summary is machine-generated.

Brain stimulation can improve memory, but results vary. This study found that targeting specific brain regions with strong structural connections to memory networks enhances memory recall, especially with closed-loop stimulation.

Keywords:

Brain stimulation Memory enhancement Structural connectivity

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Neuronavigated Focalized Transcranial Direct Current Stimulation Administered During Functional Magnetic Resonance Imaging

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Published on: November 15, 2024

Area of Science:

Neuroscience
Cognitive Science
Neuromodulation

Background:

Intracranial stimulation shows potential for memory enhancement, but outcomes are inconsistent across individuals and sites.
Variability in response may stem from differences in stimulation target network embedding.

Purpose of the Study:

To investigate if the structural and functional network embedding of intracranial stimulation targets predicts memory enhancement variability.
To determine the role of network connectivity in optimizing closed-loop stimulation for memory improvement.

Main Methods:

Analyzed intracranial EEG data from 50 epilepsy patients undergoing verbal memory tasks during left temporal cortex stimulation.
Used diffusion tractography and functional connectivity to assess network embedding of stimulation sites.

Employed closed-loop and random stimulation paradigms, correlating memory enhancement with network properties.

Main Results:

Closed-loop stimulation during detected low-encoding states improved recall; random stimulation did not.
Greater memory enhancement correlated with stronger structural coupling to fronto-temporo-parietal networks.
Structure-function congruence predicted closed-loop memory benefit (Spearman ρ = 0.58, P < 0.0001).

Conclusions:

Reliable, stimulation-driven memory enhancement depends on the structural network embedding of the stimulation target.
Optimizing stimulation target's structural integration and timing is crucial for individualized memory improvement.
Findings support a network-based rationale for precision-guided neuromodulation.