On the cortical connectivity in the macaque brain: A comparison of diffusion tractography and histological tracing data.

Diffusion-weighted magnetic resonance imaging (DW-MRI) tractography is a non-invasive tool to probe neural connections and the structure of the white matter. It has been applied successfully in studies of neurological disorders and normal connectivity. Recent work has revealed that tractography produces a high incidence of false-positive connections, often from "bottleneck" white matter configurations. The rich literature in histological connectivity analysis studies in the macaque monkey enables quantitative evaluation of the performance of tractography algorithms. In this study, we use the intricate connections of frontal, cingulate, and parietal areas, well established by the anatomical literature, to derive a symmetrical histological connectivity matrix composed of 59 cortical areas. We evaluate the performance of fifteen diffusion tractography algorithms, including global, deterministic, and probabilistic state-of-the-art methods for the connectivity predictions of 1711 distinct pairs of areas, among which 680 are reported connected by the literature. The diffusion connectivity analysis was performed on a different ex-vivo macaque brain, acquired using multi-shell DW-MRI protocol, at high spatial and angular resolutions. Across all tested algorithms, the true-positive and true-negative connections were dominant over false-positive and false-negative connections, respectively. Moreover, three-quarters of streamlines had endpoints location in agreement with histological data, on average. Furthermore, probabilistic streamline tractography algorithms show the best performances in predicting which areas are connected. Altogether, we propose a method for quantitative evaluation of tractography algorithms, which aims at improving the sensitivity and the specificity of diffusion-based connectivity analysis. Overall, those results confirm the usefulness of tractography in predicting connectivity, although errors are produced. Many of the errors result from bottleneck white matter configurations near the cortical grey matter and should be the target of future implementation of methods.

Neuronal chemo-architecture of the entorhinal cortex: A comparative review.

The identification of neuronal markers, that is, molecules selectively present in subsets of neurons, contributes to our understanding of brain areas and the networks within them. Specifically, recognizing the distribution of different neuronal markers facilitates the identification of borders between functionally distinct brain areas. Detailed knowledge about the localization and physiological significance of neuronal markers may also provide clues to generate new hypotheses concerning aspects of normal and abnormal brain functioning. Here, we provide a comprehensive review on the distribution within the entorhinal cortex of neuronal markers and the morphology of the neurons they reveal. Emphasis is on the comparative distribution of several markers, with a focus on, but not restricted to rodent, monkey and human data, allowing to infer connectional features, across species, associated with these markers, based on what is revealed by mainly rodent data. The overall conclusion from this review is that there is an emerging pattern in the distribution of neuronal markers in the entorhinal cortex when aligning data along a comparable coordinate system in various species.

Automated tracing of neurites from light microscopy stacks of images.

Automating the process of neural circuit reconstruction on a large-scale is one of the foremost challenges in the field of neuroscience. In this study we examine the methodology for circuit reconstruction from three-dimensional light microscopy (LM) stacks of images. We show how the minimal error-rate of an ideal reconstruction procedure depends on the density of labeled neurites, giving rise to the fundamental limitation of an LM based approach for neural circuit research. Circuit reconstruction procedures typically involve steps related to neuron labeling and imaging, and subsequent image pre-processing and tracing of neurites. In this study, we focus on the last step--detection of traces of neurites from already pre-processed stacks of images. Our automated tracing algorithm, implemented as part of the Neural Circuit Tracer software package, consists of the following main steps. First, image stack is filtered to enhance labeled neurites. Second, centerline of the neurites is detected and optimized. Finally, individual branches of the optimal trace are merged into trees based on a cost minimization approach. The cost function accounts for branch orientations, distances between their end-points, curvature of the merged structure, and its intensity. The algorithm is capable of connecting branches which appear broken due to imperfect labeling and can resolve situations where branches appear to be fused due the limited resolution of light microscopy. The Neural Circuit Tracer software is designed to automatically incorporate ImageJ plug-ins and functions written in MatLab and provides roughly a 10-fold increases in speed in comparison to manual tracing.

Principal curves as skeletons of tubular objects: locally characterizing the structures of axons.

Developments in image acquisition technology make high volumes of neuron images available to neuroscientists for analysis. However, manual processing of these images is not practical and is infeasible for larger and larger scale studies. Reliable interpretation and analysis of high volume data requires accurate quantitative measures. This requires analysis algorithms to use mathematical models that inherit the underlying geometry of biological structures in order to extract topological information. In this paper, we first introduce principal curves as a model for the underlying skeleton of axons and branches, then describe a recursive principal curve tracing (RPCT) method to extract this topology information from 3D microscopy imagery. RPCT first finds samples on the one dimensional principal set of the intensity function in space. Then, given an initial direction and location, the algorithm iteratively traces the principal curve in space using our principal curve tracing (PCT) method. Recursive implementation of PCT provides a compact solution for extracting complex tubular structures that exhibit bifurcations.