Spatial registration of neuron morphologies based on maximization of volume overlap

Background: Morphological features are widely used in the study of neuronal function and pathology. Invertebrate neurons are often structurally stereotypical, showing little variance in gross spatial features but larger variance in their fine features. Such variability can be quantified using detailed spatial analysis, which however requires the morphologies to be registered to a common frame of reference. Results: We outline here new algorithms - Reg-MaxS and Reg-MaxS-N - for co-registering pairs and groups of morphologies, respectively. Reg-MaxS applies a sequence of translation, rotation and scaling transformations, estimating at each step the transformation parameters that maximize spatial overlap between the volumes occupied by the morphologies. We test this algorithm with synthetic morphologies, showing that it can account for a wide range of transformation differences and is robust to noise. Reg-MaxS-N co-registers groups of more than two morphologies by iteratively calculating an average volume and registering all morphologies to this average using Reg-MaxS. We test Reg-MaxS-N using five groups of morphologies from the Droshophila melanogaster brain and identify the cases for which it outperforms existing algorithms and produce morphologies very similar to those obtained from registration to a standard brain atlas. Conclusions: We have described and tested algorithms for co-registering pairs and groups of neuron morphologies. We have demonstrated their application to spatial comparison of stereotypic morphologies and calculation of dendritic density profiles, showing how our algorithms for registering neuron morphologies can enable new approaches in comparative morphological analyses and visualization

Analysis and network simulations of honeybee interneurons responsive to waggle dance vibration signals

BACKGROUND: Honeybees have long fascinated neuroscientists with their highly evolved social structure and rich behavioral repertoire. They sense air vibrations with their antennae, which is vital for several activities during foraging, like waggle dance communication and flight. GOALS: This thesis presents the investigation of the function of an identified vibration-sensitive interneuron, DL-Int-1. Primary goals were the investigation of (i) adaptations during maturation and (ii) the role of DL-Int-1 in networks encoding distance information of waggle dance vibration signals. RESULTS: Visual inspection indicated that DL-Int-1 morphologies had similar gross structure, but were translated, rotated and scaled relative to each other. To enable detailed spatial comparison, an algorithm for the spatial co-registration of neuron morphologies, Reg-MaxS-N was developed and validated. Experimental data from DL-Int-1 was provided by our Japanese collaborators. Comparison of morphologies from newly emerged adult and forager DL-Int-1 revealed minor changes in gross dendritic features and consistent, region-dependent and spatially localized changes in dendritic density. Comparison of electrophysiological response properties showed an increase in firing rate differences between stimulus and non-stimulus periods during maturation. A putative disinhibitory network in the honeybee primary auditory center was proposed based on experimental evidence. Simulations showed that the network was consistent with experimental observations and clarified the central inhibitory role of DL-Int-1 in shaping the network output. RELEVANCE: Reg-MaxS-N presents a novel approach for the spatial co-registration of morphologies. Adaptations in DL-Int-1 morphology during maturation indicate improved connectivity and signal propagation. The central role of DL-Int-1 in a disinhibitory network in the honeybee primary auditory center combined with adaptions in its response properties during maturation could indicate better encoding of distance information from waggle dance vibration sig- nals

Metrics for comparing neuronal tree shapes based on persistent homology

As more and more neuroanatomical data are made available through efforts such as NeuroMorpho.Org and FlyCircuit.org, the need to develop computational tools to facilitate automatic knowledge discovery from such large datasets becomes more urgent. One fundamental question is how best to compare neuron structures, for instance to organize and classify large collection of neurons. We aim to develop a flexible yet powerful framework to support comparison and classification of large collection of neuron structures efficiently. Specifically we propose to use a topological persistence-based feature vectorization framework. Existing methods to vectorize a neuron (i.e, convert a neuron to a feature vector so as to support efficient comparison and/or searching) typically rely on statistics or summaries of morphometric information, such as the average or maximum local torque angle or partition asymmetry. These simple summaries have limited power in encoding global tree structures. Based on the concept of topological persistence recently developed in the field of computational topology, we vectorize each neuron structure into a simple yet informative summary. In particular, each type of information of interest can be represented as a descriptor function defined on the neuron tree, which is then mapped to a simple persistence-signature. Our framework can encode both local and global tree structure, as well as other information of interest (electrophysiological or dynamical measures), by considering multiple descriptor functions on the neuron. The resulting persistence-based signature is potentially more informative than simple statistical summaries (such as average/mean/max) of morphometric quantities-Indeed, we show that using a certain descriptor function will give a persistence-based signature containing strictly more information than the classical Sholl analysis. At the same time, our framework retains the efficiency associated with treating neurons as points in a simple Euclidean feature space, which would be important for constructing efficient searching or indexing structures over them. We present preliminary experimental results to demonstrate the effectiveness of our persistence-based neuronal feature vectorization framework

Analysis and network simulations of honeybee interneurons responsive to waggle dance vibration signals

BACKGROUND: Honeybees have long fascinated neuroscientists with their highly evolved social structure and rich behavioral repertoire. They sense air vibrations with their antennae, which is vital for several activities during foraging, like waggle dance communication and flight. GOALS: This thesis presents the investigation of the function of an identified vibration-sensitive interneuron, DL-Int-1. Primary goals were the investigation of (i) adaptations during maturation and (ii) the role of DL-Int-1 in networks encoding distance information of waggle dance vibration signals. RESULTS: Visual inspection indicated that DL-Int-1 morphologies had similar gross structure, but were translated, rotated and scaled relative to each other. To enable detailed spatial comparison, an algorithm for the spatial co-registration of neuron morphologies, Reg-MaxS-N was developed and validated. Experimental data from DL-Int-1 was provided by our Japanese collaborators. Comparison of morphologies from newly emerged adult and forager DL-Int-1 revealed minor changes in gross dendritic features and consistent, region-dependent and spatially localized changes in dendritic density. Comparison of electrophysiological response properties showed an increase in firing rate differences between stimulus and non-stimulus periods during maturation. A putative disinhibitory network in the honeybee primary auditory center was proposed based on experimental evidence. Simulations showed that the network was consistent with experimental observations and clarified the central inhibitory role of DL-Int-1 in shaping the network output. RELEVANCE: Reg-MaxS-N presents a novel approach for the spatial co-registration of morphologies. Adaptations in DL-Int-1 morphology during maturation indicate improved connectivity and signal propagation. The central role of DL-Int-1 in a disinhibitory network in the honeybee primary auditory center combined with adaptions in its response properties during maturation could indicate better encoding of distance information from waggle dance vibration sig- nals

Design and implementation of multi-signal and time-varying neural reconstructions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.Several efficient procedures exist to digitally trace neuronal structure from light microscopy, and mature community resources have emerged to store, share, and analyze these datasets. In contrast, the quantification of intracellular distributions and morphological dynamics is not yet standardized. Current widespread descriptions of neuron morphology are static and inadequate for subcellular characterizations. We introduce a new file format to represent multichannel information as well as an open-source Vaa3D plugin to acquire this type of data. Next we define a novel data structure to capture morphological dynamics, and demonstrate its application to different time-lapse experiments. Importantly, we designed both innovations as judicious extensions of the classic SWC format, thus ensuring full back-compatibility with popular visualization and modeling tools. We then deploy the combined multichannel/time-varying reconstruction system on developing neurons in live Drosophila larvae by digitally tracing fluorescently labeled cytoskeletal components along with overall dendritic morphology as they changed over time. This same design is also suitable for quantifying dendritic calcium dynamics and tracking arbor-wide movement of any subcellular substrate of interest.Peer reviewe

NBLAST: Rapid, Sensitive Comparison of Neuronal Structure and Construction of Neuron Family Databases.

Neural circuit mapping is generating datasets of tens of thousands of labeled neurons. New computational tools are needed to search and organize these data. We present NBLAST, a sensitive and rapid algorithm, for measuring pairwise neuronal similarity. NBLAST considers both position and local geometry, decomposing neurons into short segments; matched segments are scored using a probabilistic scoring matrix defined by statistics of matches and non-matches. We validated NBLAST on a published dataset of 16,129 single Drosophila neurons. NBLAST can distinguish neuronal types down to the finest level (single identified neurons) without a priori information. Cluster analysis of extensively studied neuronal classes identified new types and unreported topographical features. Fully automated clustering organized the validation dataset into 1,052 clusters, many of which map onto previously described neuronal types. NBLAST supports additional query types, including searching neurons against transgene expression patterns. Finally, we show that NBLAST is effective with data from other invertebrates and zebrafish. VIDEO ABSTRACT.This work was supported by the Medical Research Council [MRC file reference U105188491] and European Research Council Starting and Consolidator Grants to G.S.X.E.J., who is an EMBO Young Investigator.This is the final version of the article. It first appeared from Cell Press via http://dx.doi.org/10.1016/j.neuron.2016.06.01