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# Statistical Atlases and Automatic Labeling Strategies to Accelerate the Analysis of Social Insect Brain Evolution

Sara Arganda, Ignacio Arganda-Carreras, Darcy Gordon, Andrew Hoadley, Alfonso Pérez-Escudero, Martin Giurfa, James Traniello

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## To cite this version:

Sara Arganda, Ignacio Arganda-Carreras, Darcy Gordon, Andrew Hoadley, Alfonso Pérez-Escudero, et al.. Statistical Atlases and Automatic Labeling Strategies to Accelerate the Analysis of Social Insect Brain Evolution. Frontiers in Ecology and Evolution, 2022, 9, 10.3389/fevo.2021.745707. [https://doi.org/10.38563/hal-03856353](https://doi.org/10.38563/hal-03856353)

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# Statistical Atlases and Automatic Labeling Strategies to Accelerate the Analysis of Social Insect Brain Evolution

Sara Arganda¹,²,³*, Ignacio Arganda-Carreras⁴,⁵,⁶, Darcy G. Gordon¹, Andrew P. Hoadley¹, Alfonso Pérez-Escudero², Martin Giurfa²,⁷ and James F. A. Traniello¹,⁸

¹ Department of Biology, Boston University, Boston, MA, United States, ² Centre de Recherches sur la Cognition Animale (CRCA), Centre de Biologie Intégrative (CBI), Université de Toulouse, CNRS, UPS, Toulouse, France, ³ Departamento de Biología y Geología, Física y Química Inorgánica, Área de Biodiversidad y Conservación, Universidad Rey Juan Carlos, Madrid, Spain, ⁴ Department of Computer Science and Artificial Intelligence, University of the Basque Country (UPV/EHU), San Sebastian, Spain, ⁵ IKERBASQUE, Basque Foundation for science, Bilbao, Spain, ⁶ Donostia International Physics Center (DIPC), San Sebastian, Spain, ⁷ Institut Universitaire de France, Paris, France, ⁸ Graduate Program for Neuroscience, Boston University, Boston, MA, United States

## OPEN ACCESS

**Edited by:**
Heikki Helanterä,
University of Oulu, Finland

**Reviewed by:**
Qike Wang,
The University of Melbourne, Australia
Jürgen Rybak,
Max Planck Institute for Chemical
Ecology, Germany

*Correspondence:
Sara Arganda
sarijuela@gmail.com

**Specialty section:**
This article was submitted to
Social Evolution,
a section of the journal
Frontiers in Ecology and Evolution

**Received:** 22 July 2021
**Accepted:** 28 December 2021
**Published:** 17 February 2022

**Citation:**
Arganda S, Arganda-Carreras I, Gordon DG, Hoadley AP, Pérez-Escudero A, Giurfa M and Traniello JFA (2022) Statistical Atlases and Automatic Labeling Strategies to Accelerate the Analysis of Social Insect Brain Evolution. *Front. Ecol. Evol.* 9:745707. doi: 10.3389/fevo.2021.745707

Current methods used to quantify brain size and compartmental scaling relationships in studies of social insect brain evolution involve manual annotations of images from histological samples, confocal microscopy or other sources. This process is susceptible to human bias and error and requires time-consuming effort by expert annotators. Standardized brain atlases, constructed through 3D registration and automatic segmentation, surmount these issues while increasing throughput to robustly sample diverse morphological and behavioral phenotypes. Here we design and evaluate three strategies to construct statistical brain atlases, or templates, using ants as a model taxon. The first technique creates a template by registering multiple brains of the same species. Brain regions are manually annotated on the template, and the labels are transformed back to each individual brain to obtain an automatic annotation, or to any other brain aligned with the template. The second strategy also creates a template from multiple brain images but obtains labels as a consensus from multiple manual annotations of individual brains comprising the template. The third technique is based on a template comprising brains from multiple species and the consensus of their labels. We used volume similarity as a metric to evaluate the automatic segmentation produced by each method against the inter- and intra-individual variability of human expert annotators. We found that automatic and manual methods are equivalent in volume accuracy, making the template technique an extraordinary tool to accelerate data collection and reduce human bias in the study of the evolutionary neurobiology of ants and other insects.

**Keywords:** standardized brain atlases, computational neuroimaging, evolutionary neurobiology, neuroethology, social brain evolution, Neuroanatomy, Ant brains
# INTRODUCTION

Our understanding of pattern and process in brain evolution in group-living animals benefits from sampling phylogenetically diverse species. Ants and other eusocial insects (primarily wasps, bees, and termites) have become important models to explore what is broadly conceptualized as “social brain evolution” (Dunbar, 1998; Lihoreau et al., 2012, 2019; Godfrey and Gronenberg, 2019; Muratore and Traniello, 2020; Coto and Traniello, 2021). Eusocial insects have exceptional reproductive and ergonomic polyphenisms associated with division of labor and highly cooperative behavior, and thus offer multiple opportunities and a rich array of species to examine how reproductive competence, sterility, and morphological and behavioral differentiation impact social roles and neuroarchitecture. Workers show extraordinary behavior as individuals as well as members of groups that act collectively, and individuals are so interdependent that the colony is considered to be a “superorganism” (Hölldobler and Wilson, 2009). The brains of colony members have evolved to respond as individuals but also as decision-making groups to cope socially with the environment and its challenges, as well as facilitate communication and coordinate foraging, defense, and nest construction and regulate task performance and nestmate recognition. Important questions integrating insect sociobiology and evolutionary neurobiology concern how selection may favor either an increase or reduction in brain size and structure (Wehner et al., 2007; Muscedere and Traniello, 2012; Riveros et al., 2012; O'Donnell et al., 2018; Arganda et al., 2020; DeSilva et al., 2021).

Ant brains and those of other insects can be adaptive allometric mosaics composed of functionally specialized brain compartment allometries. Neuropils are involved in primary sensory processing (e.g., the antennal, optic lobes, subesophageal zone), motor control and navigation (the central complex and subesophageal zone), and multi-sensorial higher-order processing and integration, learning and memory (the mushroom bodies) (Strausfeld, 2012). Immunohistochemistry, confocal microscopy, and other techniques are commonly used to image brains and neuropil volumes are quantified using image analysis software to examine brain structure within and across insect species. Methods to calculate neuropil volumes require allocating significant effort to manually annotate brain compartments and subregions because an anatomical label must be assigned to every pixel or voxel in 2D and 3D images, respectively (Figure 1). This technique of recording neuroanatomical data is both time consuming and susceptible to human bias and error.

Technical problems associated with imaging ant brains can be reduced by using methodologies developed to study the human brain (Talairach and Tournoux, 1988). These techniques usually combine images from multiple brains into a single reference brain or *template* (Figure 2). This method has been applied in studies on honey bees (e.g., Rybak, 2012), flies (e.g., Rein et al., 2002; Costa et al., 2016; Arganda-Carreras et al., 2018), and other insects (e.g., Kurylas et al., 2008; Menzel, 2012; el Jundi and Heinze, 2020). The use of several

brain images to build a template avoids potential biases arising during tissue fixation and imaging, and accounts for the natural variability among samples, allows a statistical representation of the brain of a species or worker phenotype. This type of template, as opposed to a reference brain derived from a single individual, is called a “group-wise template.” Because combining all samples in a single brain representation requires transforming them onto the same reference space, templates allow normalizing information from brains that might have been imaged under different conditions. In addition, group-wise templates are usually associated to annotations (labels) of brain subcompartments. These labels of the template are used to automatically segment (annotate or label) these subcompartments in new samples, by registering them against the template, which consist of transforming them to be in the same reference space as the template (e.g., Arganda-Carreras et al., 2018). An alternative to his strategy is that of Rybak (2012), where a template brain is created in a similar way to ours, but individual brains are first labeled using a statistical shape model and then registered against the template using the label volumes instead of the gray-value ones. This approach has the advantage of a label-oriented registration, where each anatomical region can be treated independently. However, its performance may be too sensitive to the segmentation result obtained by the model, which should correctly estimate the sometimes very large shape diversity of the dataset.

Although template strategies have been widely applied in mammals (Talairach and Tournoux, 1988; Evans et al., 1994; Mazziotta et al., 1995; Chen et al., 2006; Dogdas et al., 2007; Shattuck et al., 2008; Yu et al., 2010), their implementation in insect research has been less frequent. It has been expanded from *Drosophila* (Rein et al., 2002; Jefferis et al., 2007; Cachero et al., 2010; Costa et al., 2016; Arganda-Carreras et al., 2018) to other insects only in the last decade (e.g., Menzel, 2012; Rybak, 2012; el Jundi and Heinze, 2020). The application of this methodology to research focusing on ants has occurred more slowly, probably because of their high diversity (~15,000 species). In addition, intra-specific variability is an issue *per se*: workers may show greater variation in brain anatomy at the level of species and colony, thus constraining the image registration process needed to generate a template, which usually requires a minimum spatial overlap of positions between same sub-regions in the co-registered brain images. Another difficulty is that neuroanatomical studies performed on ants focus on more or less detailed brain subdivisions, creating different sets of compartmental anatomical labels (e.g., Muscedere and Traniello, 2012; Amador-Vargas et al., 2015; Bressan et al., 2015; O'Donnell et al., 2018; Gordon et al., 2019; Sheehan et al., 2019; Habenstein et al., 2020). Consequently, most studies describing ant brain organization have not aimed at building brain templates (e.g., Bressan et al., 2015; Habenstein et al., 2020).

Here, we describe and evaluate experimental strategies to generate brain templates in ants to promote standardized approaches for comparative neuroanatomical analysis. While finer descriptions of neuropil sub-compartments exist for ant brains (e.g., Bressan et al., 2015; Habenstein et al., 2020) and
<figure><img src="image_4.png" /><figcaption>**FIGURE 1** | Anatomy of a *P. spadonia* minor brain. MB-LC (mushroom body lateral calyx), MB-MC (mushroom body medial calyx), SEZ (subesophageal zone), OL (optic lobes), AL (antennal lobes), MB-P (mushroom body peduncle), CX (central complex), and ROCB (rest of the central brain). Scale bar = 100 um. Three brain slices have been selected to show all the subregions analyzed.</figcaption></figure>

other social Hymenoptera (e.g., Brandt et al., 2005; Rybak, 2012; Groothuis et al., 2019), we focused this first approach on major neuropils (which are commonly used to explore neuroanatomical differences among species, castes, subcastes and experimentally manipulated individuals, e.g., Kamhi et al., 2016; Seid and Junge, 2016; Gordon et al., 2017; Grob et al., 2021). We recently applied state-of-the-art imaging techniques to generate templates using brains from a single or multiple ant species (Arganda-Carreras et al., 2017; Gordon et al., 2019). Using careful annotations by trained researchers as our standard, we evaluate template-based strategies to automatically segment ant brain confocal images, allowing more efficient and less biased volumetric data acquisition. We validate the template method by evaluating its application to workers of species in the ant genus *Pheidole*.

## MATERIALS AND METHODS

We present three methods to produce and use templates for automatic segmentation (Figure 2). The first consists of building a template using confocal gray value whole brain images of a single species, and manually labeling brain compartments on the template (Figure 2A). This “direct label method” involves manually tracing a single anatomy (the one of the template) and automatically tracing other gray value brain images (by registration against the labeled template). The second “consensus label method” also uses a single-species template, but gray value brain images used to build the template contain manually annotated labels (Figure 2B). Then, these manual labels are used to create the final template labels. This method considers label values resulting from more than a single (potentially biased) tracing, and thus, it may be more accurate than the first method, at the expense of requiring more manual work. In this case, the method is only useful to trace new brains. The third possibility—the “multispecies template method”—is similar to the second but uses gray value brain images from several species (Figure 2C), thus enabling the expansion of species sampling. We next describe the ant brain dataset used, the methods to generate the different templates and labels, and how to evaluate the efficacies of the different methods.

## Brain Anatomy Dataset

We imaged brains of minor workers of four species of the hyperdiverse ant genus *Pheidole* (*P. spadonia*, *P. rhea*, *P. tepicana*, and *P. obtusospinosa*). While *Pheidole* is typically characterized by complete dimorphism in the worker caste (small minor and large major [soldier] workers) and in some basal species (*P. rhea*) trimorphism, which includes a third, larger worker subcaste (super soldiers), we used only minors for proof of concept. Minors and majors are easily discriminated by body size and head allometry (Wilson, 2003).

Minor workers were decapitated and their brains were dissected from the head capsule in ice cold HEPES-buffered saline. Brains were fixed and immunohistochemically stained using SYNORF1 (a monoclonal *Drosophila* synapsin I antibody obtained from the Developmental Studies Hybridoma Bank, catalog 3C11) and secondarily stained using Alexa Fluor 488 for visualization of neuropil (slightly modified from Ott, 2008). Mounted in methyl salicylate, brains were imaged on an Olympus Fluoview BX50 laser (488 nm) scanning confocal microscope with a × 20 air objective (NA = 0.5) at a resolution of ~0.7 × 0.7 × 5µm/voxel, producing gray images of 16 bits (in TIFF format). We imaged 10 brains from *P. spadonia*, and three each of *P. rhea*, *P. tepicana*, and *P. obtusospinosa* minor workers. Each brain image was manually labeled as described in the “Manual labeling of original brain images and template” section below.

## Standard Brain Image Method: Image Registration and Template Generation

Templates were built in a diffeomorphic space<fnref n="1" /> as an average-shape brain (“Template,” Figure 2). The diffeomorphic space allows for smooth invertible transformations from one anatomy to another ($T^1$ and $T^{-1}$, Figure 2). Our methodology is based

<footnote n="1">¹Diffeomorphism: differentiable transform that allows mapping the coordinates of one image onto the coordinates of another image in a smooth and invertible way.</footnote>
<figure><img src="image_5.png" /><figcaption>**FIGURE 2** | Automatic labeling methods. (A) Ten confocal images of brains of *P. spadonia* minors are combined on a single group-wise template, which is manually traced (creating "direct labels"). Each brain used to build the template (and other new brains) can be registered against the template with a (Continued)</figcaption></figure>

**FIGURE 2** | transformation function $T$. The inverse function $T^{-1}$ can be used on the manual labels of the template to automatically label the registered brain. (B) Nine confocal images of brains of *P. spadonia* minors are combined on a single group-wise template. The existing manual labels of each brain are registered against the template, and every voxel is assigned to one label by majority voting (creating "consensus labels"). New brains can be registered against the template with a transformation function $T$. The inverse function $T^{-1}$ can be used on the consensus labels of the template to automatically label the registered brain. (C) Twelve confocal images of brains of *P. spadonia*, *P. rhea*, *P. tepicana*, and *P. obtusospinosa* (three of each species) are combined on a single multispecies group-wise template. Consensus labels are created for the template as in B, and the same procedure is applied to automatically trace new brains. Scale bar = 100 um. A single slice per brain has been shown for illustration clarity.

on a two-step approach using symmetric diffeomorphic image registration<fnref n="2" /> (SyN, Avants et al., 2008) of a group of gray value brain images to one another by maximizing mutual information<fnref n="3" /> first and cross-correlation<fnref n="4" /> later. Following this optimization process, the group of images are warped into the same coordinate system. In the first step, all gray value brain images are registered against one randomly selected image by optimizing mutual information and allowing only affine transformations (translations and proportional changes in size). Transformed images are then averaged to build a preliminary "blurry" reference brain image. In the second step, the original gray value brain images are registered to this blurry average using non-rigid transformations (i.e., allowing local deformations) by maximizing the cross-correlation of the intensities of all brains. In this step, the registration is gradually improved at four resolution levels (sequentially at ¹/₈, ¹/₄, ¹/₂, and 1/1 of the original sizes, following a resolution pyramid strategy) and produces an optimal average template. The first registration compensates for large disparities in size while the second locally finds an optimal solution. The template was generated by the normalized voxel-wise median of the co-registered volumes (Arganda-Carreras et al., 2017). All steps were implemented in the Advanced Normalization Tools (ANTs) software (Avants et al., 2011) after transforming in Fiji (Schindelin et al., 2012) gray and label images to the open format NRRD. For a detailed description of the software methods used in this paper, we refer the reader to **Supplementary Material**.

Seven group-wise templates were generated for this study (**Supplementary Table 1**) with 9 ("consensus label method"), 10 ("direct label method") or 12 (multispecies template method) original gray value brain images. Six of them were single-species templates, built using only *P. spadonia* minor gray value brain images ("direct/consensus label methods"). One was a hybrid template, generated from brains of *P. spadonia*, *P. rhea*, *P. tepicana*, and *P. obtusospinosa* minors ("multispecies

<footnote n="2">²Image registration: process of transforming one image (usually known as moving image) into the coordinate system of another image (usually known as fixed image).</footnote>

<footnote n="3">³Mutual information: metric taken from information theory and used on image registration to measure the amount of information that one image contains about another image. It should be maximum when both image are perfectly aligned.</footnote>

<footnote n="4">⁴Cross-correlation: metric of the similarity of two images as a function of the displacement of one with respect to the other.</footnote>
<figure><img src="image_6.png" /></figure>

**FIGURE 3** | Variability between annotations for brain compartments and the whole brain. Variation (using volume similarity) given is for the (A) optic lobe (OL). (B) Antennal lobe (AL). (C) Mushroom body medial calyx (MB-MC). (D) Mushroom body lateral calyx (MB-LC). (E) Mushroom peduncle (MB-P). (F) Central complex (CX). (G) Subesophageal zone (SEZ). (H) For the rest of the central brain (ROCB). (I) For the whole brain. Statistical comparisons are made using bootstrapping tests for comparing the volume differences found between the manual and the automatic labels ("Direct labels," "Cons. Labels," and "Multisp. temp.") and between individuals ("Inter-Person") or within the same individual ("Intra-Person"). "*" indicates *p*-values smaller than 0.05.

Standardized average brain atlases (group-wise templates) are increasingly applied in insects (Rein et al., 2002; Brandt et al., 2005; El Jundi et al., 2009; Kvello et al., 2009; Rybak et al., 2010; Peng et al., 2011; Menzel, 2012; Rybak, 2012; Costa et al., 2016; Arganda-Carreras et al., 2017, 2018; Gordon et al., 2019; Groothuis et al., 2019; el Jundi and Heinze, 2020) to efficiently and accurately collect data required to test hypotheses of brain evolution and to facilitate the establishment of connectomes. They allow, for example, the registration of multiple marked neurons into standard anatomies to determine their spatial relationships and possible inclusion in common neuronal circuits (e.g., Brandt et al., 2005; Peng et al., 2011). Annotated atlases also provide information on the shape and size of the different brain compartments to make intra and interspecific comparisons (e.g., Rein et al., 2002; Heinze et al., 2013; De Vries et al., 2017) and generate and test hypotheses on the importance of particular modalities of sensory processing in insect behavior, ecology, and sociobiology, and life history.

In ants, most brain studies present 3D models based on representative individuals (e.g., Bressan et al., 2015; Habenstein et al., 2020) instead of standardized brain atlases. Aside from accounting for interindividual variability and reducing the possible bias of a single representative, the use of group-wise templates allows the rapid and accurate collection of volumetric neuroanatomical data. To our knowledge, we were the first to generate group-wise templates and consensus labels in ants to automatically trace similar brains (Arganda-Carreras et al., 2017).

In this work, we also presented for the first time a multispecies template. In another study, we used group-wise templates manually traced to reduce the time needed to trace 60 brains of three different brain phenotypes of the polymorphic turtle ant *Cephalotes varians* (Gordon et al., 2019). Here we validate these different methods using the variability of human annotations as the “gold standard.” All the methods presented reduce the time required for manually tracing each brain and help decrease potential errors of multiple annotators, either by allocating a single annotator to a large dataset or by combining labels that integrate variability between samples. Group-wise templates also advantageously ensure blind annotations for samples of different origins known to the annotator (for example, different treatments or species) thus minimizing biases. For this purpose, we plan to build single templates for polymorphic species in future studies. Each strategy might be more suitable to answer some research questions than others; for example, the “direct label method” is recommendable for blind studies comparing individuals under different treatments. The “consensus label method” might provide with robust reference anatomical atlases that consider interindividual variability. And the “multispecies template method” can make evolutionary and comparative studies requiring large datasets from multiple species more robust. While our methods have been evaluated using descriptions of major neuropils, testing them on finer neuropil sub-structures will be a logical next step that will increase their potentiality. Regardless of the neuroanatomical scale, the use of templates to accurately
and rapidly collect volumetric neuroanatomical data, combined with sociobiological, socioecological, phylogenetic, metabolic, or neurochemical analyses can help elucidate macroevolutionary and microevolutionary patterns of brain evolution. This will allow to better understand encephalization and allometric scaling in regard to the behavioral ecology and sociobiology of individual workers, and the impact of emergent colony-level processes on the brain.

## DATA AVAILABILITY STATEMENT

The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author/s.

## AUTHOR CONTRIBUTIONS

DG prepared the brains for imaging, registered images, and manually labeled all brain images and the templates. AH manually labeled a sample of brain images and the templates. SA manually labeled a sample of brain images and the templates and prepared all brain images for template creation. IA-C implemented the software to create the templates and to automatically label brain subregions. SA and AP-E designed the methodology to evaluate automatic labeling and performed the statistical analysis. SA, IA-C, and JT conceptualized and

designed the study and wrote the first draft of the manuscript. SA, IA-C, MG, and JT and secured funding. All authors edited and approved the final content of the manuscript.

## FUNDING

This research was supported by the National Science Foundation grants IOS 1354291 and IOS 1953393 to JT, a Marie Skłodowska-Curie Individual Fellowship BrainiAnts-660976 and Ayudas destinadas a la atracción de talento investigador a la Comunidad de Madrid en centros de I+D to SA, the University of the Basque Country UPV/EHU grant GIU19/027 to IA-C, and by the Institut Universitaire de France to MG.

## ACKNOWLEDGMENTS

We thank Dr. Ming Huang for kindly supplying us with colonies of *Pheidole spadonia*, *P. rhea*, *P. tepicana*, and *P. obtusospinosa*.

## SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fevo.2021.745707/full#supplementary-material

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