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Nanomagnetic Guidance Shapes the Structure-Function Relationship of Developing Cortical Networks.

Connor L Beck1, Conner T Killeen2, Sara C Johnson1

  • 1Department of Electrical and Computer Engineering, Montana State University, Bozeman, Montana 59717, United States.

Nano Letters
|October 21, 2024
PubMed
Summary

This study demonstrates nanomagnetic forces can guide neuronal network development. Guided networks show enhanced structure, connectivity, and electrical activity, paving the way for advanced neural engineering.

Keywords:
Axon guidanceElectrophysiologyMagnetic nanoparticlesMicroelectrode arraysNeural circuit guidanceNeural networks

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Area of Science:

  • Neuroscience
  • Biotechnology
  • Materials Science

Background:

  • Developing methods to control neuronal network structure and function is crucial for neural engineering.
  • Neuronal development and connectivity are complex processes that are difficult to manipulate precisely.

Purpose of the Study:

  • To implement large-scale nanomagnetic guidance for directing the development of dissociated cortical neuron networks.
  • To investigate the impact of nanomagnetic forces on neurite outgrowth, alignment, and network electrophysiology.

Main Methods:

  • Cortical neurons were cultured on microelectrode arrays and exposed to functionalized magnetic nanoparticles.
  • Magnetic fields were applied to guide neurite outgrowth over 14 days in vitro.
  • Immunofluorescence, brightfield imaging, and spontaneous electrophysiological recordings were used for analysis.

Main Results:

  • Nanomagnetic guidance resulted in a greater number of longer neurites aligned with the applied force.
  • Guided networks exhibited increased firing rates and frequency in force-aligned connectivity.
  • Nonuniform force directions led to increased local activity in targeted regions.

Conclusions:

  • Nanomagnetic forces effectively guide the structure and function of dissociated cortical neuron networks at the millimeter scale.
  • This methodology offers a novel approach for engineering neural networks with controlled architecture and enhanced activity.