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Related Concept Videos

Brain Imaging01:14

Brain Imaging

265
Brain imaging technologies provide critical insights into both the structure and function of the human brain, enabling medical professionals and researchers to diagnose, study, and treat neurological disorders or psychiatric disorders more effectively.
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...
265

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Related Experiment Video

Updated: Jul 27, 2025

Transcranial Direct Current Stimulation tDCS for Memory Enhancement
10:37

Transcranial Direct Current Stimulation tDCS for Memory Enhancement

Published on: September 18, 2021

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Machine-learning defined precision tDCS for improving cognitive function.

Alejandro Albizu1, Aprinda Indahlastari2, Ziqian Huang3

  • 1Center for Cognitive Aging and Memory, McKnight Brain Institute, University of Florida, Gainesville, USA; Department of Neuroscience, College of Medicine, University of Florida, Gainesville, USA.

Brain Stimulation
|June 6, 2023
PubMed
Summary
This summary is machine-generated.

This study developed a personalized method to optimize transcranial direct current stimulation (tDCS) dosage for older adults, significantly improving cognitive training outcomes and paving the way for precision brain stimulation.

Keywords:
AgingFinite element modelMRIMachine-learningPrecision medicinetES

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Area of Science:

  • Neuroscience
  • Computational Biology
  • Gerontology

Background:

  • Transcranial direct current stimulation (tDCS) combined with cognitive training (CT) shows promise for enhancing cognitive function in older adults.
  • Individual responses to tDCS+CT vary, potentially due to differences in brain anatomy.

Purpose of the Study:

  • To create an objective method for personalizing tDCS current dosage to maximize cognitive gains.
  • To improve the effectiveness of non-invasive brain stimulation for cognitive enhancement.

Main Methods:

  • A support vector machine (SVM) model predicted tDCS response using computational models of current density.
  • Feature weights from the SVM informed a Gaussian Mixture Model (GMM) to optimize electrode montage and current intensity.
  • The SVM-GMM approach aimed to convert tDCS non-responders into responders.

Main Results:

  • Optimized current distributions showed 93% voxel-wise coherence in target brain regions between responders and non-responders.
  • Personalized tDCS doses for non-responders were significantly closer to those of responders.
  • The optimized models achieved high prediction accuracy (99.993% response likelihood) and successfully reclassified non-responders as responders.

Conclusions:

  • This study establishes a foundation for custom tDCS dose optimization.
  • The findings support a precision medicine approach for tDCS to enhance cognitive function in older adults.
  • This strategy holds potential for improving outcomes in cognitive decline remediation.