A method for localizing microelectrode trajectories in the macaque brain using MRI

Abstract

Magnetic resonance imaging (MRI) is often used by electrophysiologists to target specific brain regions for placement of microelectrodes. However, the effectiveness of this technique has been limited by few methods to quantify in three dimensions the relative locations of brain structures, recording chambers and microelectrode trajectories. Here we present such a method. After surgical implantation, recording chambers are fitted with a plastic cylinder that is filled with a high-contrast agent to aid in the segmentation of the cylinder from brain matter in an MRI volume. The resulting images of the filled cylinder correspond to a virtual cylinder that is projected along its long axis – parallel to the trajectories of microelectrodes advanced through the recording chamber – through the three-dimensional image of the brain. This technique, which does not require a stereotaxic coordinate system, can be used to quantify the coverage of an implanted recording chamber relative to anatomical landmarks at any depth or orientation. We have used this technique in conjunction with Caret [Van Essen DC, Drury HA, Dickson J, Harwell J, Hanlon D, Anderson CH. An integrated software suite for surface-based analyses of cerebral cortex. J Am Med Inform Assoc 2001;8:443–59] and AFNI [Cox RW. AFNI: software for analysis and visualization of functional magnetic resonance neuroimages. Comput Biomed Res 1996;29:162–73] brain-mapping software to successfully localize several regions of macaque cortex, including the middle temporal area, the lateral intraparietal area and the frontal eye field, and one subcortical structure, the locus coeruleus, for electrophysiological recordings.

Introduction

Over the past decade and a half, magnetic resonance imaging (MRI) has become the de facto standard for anatomical localization of electrophysiological targets in the monkey brain (Alvarez-Royo et al., 1991, Asahi et al., 2003, Blaizot et al., 1999, Christensen et al., 1997, Freed et al., 2001, Glimcher et al., 2001, Nahm et al., 1994, Rebert et al., 1991, Saunders et al., 1990, Subramanian et al., 2005). In contrast to post-mortem histology, MRI is neither invasive nor terminal and thus can be taken at multiple times to both guide and confirm microelectrode placement. MRI also provides better spatial resolution and better contrast between gray and white matter compared to other imaging techniques that have been used, including conventional X-ray (Kennedy and Ross, 1980), ventriculography (X-ray combined with high-contrast agents injected into the cerebral ventricles: Clifton et al., 1975, Dubach et al., 1985, Ilinsky and Kultas-Ilinsky, 1982, Percheron, 1975, Percheron et al., 1996, Percheron et al., 1986), pneumoencephalography (in which cerebrospinal fluid is drained from around the brain and replaced with air or gas in order to view the ventricles more clearly in an X-ray: Kraemer et al., 1978), computed tomography (CT, which provides a three-dimensional X-ray: Risher et al., 1997) and, more recently, ultrasonic imaging (Glimcher et al., 2001, Tokuno and Chiken, 2004, Tokuno et al., 2000).

MRI is typically used to help electrophysiological studies solve two distinct but related problems. The first is to determine the appropriate location and orientation to surgically implant a recording chamber to ensure access to a targeted brain region. A recent report introduced a novel set of methods that use MRI to solve this problem (Miocinovic et al., 2007). These methods involve a sophisticated software tool called Cicerone that can register MRI and CT images taken before surgery with each other and three-dimensional brain atlases. The registered images can then be used not only to plan the locations of recording chambers, craniotomies and microelectrodes, but also to visualize microelectrode trajectories and combine physiological and anatomical data. These features all rely on the ability to represent and visualize these disparate datasets in a common reference frame, the stereotaxic coordinate system of the monkey.

The second problem is to verify that a recording chamber, once implanted, is positioned appropriately. A closely related issue is to determine where within the chamber microelectrodes should be placed and advanced to intercept the intended neural target. Fig. 1 shows a two-dimensional image corresponding to a single plane of section through the head of a monkey. From this image it is apparent that the recording chamber mounted to the skull is positioned approximately above the intraparietal sulcus. However, it is impossible to determine the three-dimensional trajectory of a microelectrode advanced from a particular location in the recording chamber relative to anatomical targets on or near the sulcus.

One effective solution to this problem has been to leave one or two microelectrodes in the brain during imaging (e.g., Paton et al., 2006, Liu and Richmond, 2000). The microelectrodes can be placed using the same microdrives or grid-based positioning systems that are used for electrophysiological recordings. Images can then be taken in a plane parallel to the microelectrode, providing a clear view of its trajectory relative to nearby brain structures. This image plane can also be used to estimate the depth of targeted structures relative to surface features like the bottom of the recording chamber. A particularly appealing feature of this technique is that it does not rely on the stereotaxic coordinate system but instead assesses directly the three-dimensional trajectory of the microelectrode relative to nearby brain structures.

Here we describe an extension of this method that replaces microelectrode placement with an image-processing algorithm to visualize the three-dimensional trajectory of the recording chamber through the brain. In addition to providing the same benefits as the microelectrode-placement method, our method is not invasive, does not require imaging in any particular plane and provides information about the complete coverage of the recording chamber relative to underlying brain areas. Our method can be used to identify the total volume of potential coverage within the brain for a particular recording chamber and within that coverage predict where a microelectrode should be placed for its trajectory to intercept an identified brain region.