Three-dimensional brain reconstruction of in vivo electrode tracks for neuroscience and neural prosthetic applications

Craig D Markovitz,David P Edge,Hubert H Lim,Tien T Tang

doi:10.3389/fncir.2012.00039

Abstract

The brain is a densely interconnected network that relies on populations of neurons within and across multiple nuclei to code for features leading to perception and action. However, the neurophysiology field is still dominated by the characterization of individual neurons, rather than simultaneous recordings across multiple regions, without consistent spatial reconstruction of their locations for comparisons across studies. There are sophisticated histological and imaging techniques for performing brain reconstructions. However, what is needed is a method that is relatively easy and inexpensive to implement in a typical neurophysiology lab and provides consistent identification of electrode locations to make it widely used for pooling data across studies and research groups. This paper presents our initial development of such an approach for reconstructing electrode tracks and site locations within the guinea pig inferior colliculus (IC) to identify its functional organization for frequency coding relevant for a new auditory midbrain implant (AMI). Encouragingly, the spatial error associated with different individuals reconstructing electrode tracks for the same midbrain was less than 65 μm, corresponding to an error of ~1.5% relative to the entire IC structure (~4–5 mm diameter sphere). Furthermore, the reconstructed frequency laminae of the IC were consistently aligned across three sampled midbrains, demonstrating the ability to use our method to combine location data across animals. Hopefully, through further improvements in our reconstruction method, it can be used as a standard protocol across neurophysiology labs to characterize neural data not only within the IC but also within other brain regions to help bridge the gap between cellular activity and network function. Clinically, correlating function with location within and across multiple brain regions can guide optimal placement of electrodes for the growing field of neural prosthetics.

Highlights

The first validation of our method was to qualitatively compare our computer model (Figures 3E and F) of the midbrain to images of the midbrain taken a day before slicing
Overlaying our reconstruction on the fixed midbrain shows a close correspondence in shape and size of the midbrain
While electrode locations cannot be perfectly determined due to processing errors, subjectivity within the method and variability between animals, general functional trends can be correlated with anatomical locations using this process

Summary

Introduction

The neuroscience field has experienced rapid technological advances over the past decade, including the development of multi-site array technologies that can record or stimulate across more than 100 sites (Lehew and Nicolelis, 2008; Falcone and Bhatti, 2011; Stevenson and Kording, 2011), the discovery of optogenetics in which neurons can be genetically altered to become excited or suppressed to different lights (Bamann et al, 2010; Fenno et al, 2011; Miesenbock, 2011), and advances in magnetic resonance imaging (MRI) techniques that have enabled non-invasive functional and anatomical mapping of the brain (Yacoub et al, 2008; Lenglet et al, 2009; Leergaard et al, 2010; Van Essen et al, 2012). One major bottleneck in successful implementation of these different neural prosthetics has been the identification of optimal locations for stimulation to treat the health condition (Johnson et al, 2008; McCreery, 2008; Lim et al, 2009; Hemm and Wardell, 2010)

Methods

Results

Conclusion