Ants

MRIQC is an open-source project, developed under the following software engineering principles. 1) Modularity and integrability: MRIQC implements a nipype [23 ] workflow (see Fig 4) to integrate modular sub-workflows that rely upon third party software toolboxes such as FSL [24 (link)], ANTs [25 ] and AFNI [26 (link)]. 2) Minimal preprocessing: the workflow should be as minimal as possible to estimate the IQMs. 3) Interoperability and standards: MRIQC is compatible with input data formatted according to the Brain Imaging Data Structure (BIDS, [27 (link)]) standard, and the software itself follows the BIDS Apps [28 (link)] standard. For more information on how to convert data to BIDS and run MRIQC, see Blocks 3 and 4 in S1 File respectively. 4) Reliability and robustness: the software undergoes frequent vetting sprints by testing its robustness against data variability (acquisition parameters, physiological differences, etc.) using images from the OpenfMRI resource. Reliability is checked and tracked with a continuous integration service.

In the well-known Klein comparative study (Klein et al., 2009 (link)), 14 image registration algorithms were evaluated based on performance on publicly available labeled brain data. For our evaluation, we used these same data. Specifically, we used the data sets denoted as:

CUMC12

IBSR18

LPBA40

MGH10

which are available for download from Arno Klein's website¹⁶.
The number of subjects per cohort is provided in the denotation. Table 1 summarizes core information about the data sets used. Further details of these first four labeled brain data (e.g., labeling protocol, data sources) are given in Klein et al. (2009 (link)). We also include the labeled brain data provided at the MICCAI 2012 Grand Challenge and Workshop on Multi-Atlas Labeling¹⁷ which we denote as MAL35. This T1-weighted MRI data set consists of 35 subject MRIs taken from the Oasis database¹⁸. The corresponding labels were provided by Neuromorphometrics, Inc¹⁹. under academic subscription.
Comparative evaluation of the two SyN registration approaches was performed within each cohort using a “pseudo-geodesic” approach. Instead of registering every subject to every other subject within a data set, we generated the transforms from each subject to a cohort-specific shape/intensity template. Not only does this reduce the computational time required for finding the pairwise transforms between subjects but prior work has demonstrated improvement in registration with this approach over direct pairwise registration (Klein et al., 2010a (link)). Since the two algorithms have been implemented within the same framework, all registration parameters are identical (i.e., linear registration stage parameters, winsorizing values, etc.) except for the parameters governing the smoothing of the gradient field.
The cohort templates were built using the ANTs script antsMultivariateTemplateConstruction.sh which is a multivariate implementation of the work described in Avants et al. (2010 (link)). Canonical views for each of the five templates used for this study are given in Figure 2. Since calculation of the transform from each subject to the template also includes generation of the corresponding inverse transform, the total transformation from a given subject to any other is determined from the composition of transforms mapping through the template. An example illustration of the geodesic approach is given in Figure 3.
Additionally, we refined the labelings for each subject of each cohort using the multi-atlas label fusion algorithm (MALF) developed by Wang et al. (2013 (link)) which is also distributed with ANTs. For a given subject within a data set, every other subject was mapped to that subject using the pseudo-geodesic transform. The set of transformed labelings were then used to determine a consensus labeling for that subject. This was to minimize the obvious observer dimensionality artifacts where manual raters observe and label in a single dimension at a time. This is most easily seen in the axial or sagittal views of the different cohorts as labelings were done primarily in the coronal view (see Figure 4). We include both sets of results. This provides two sets of labels per subject for evaluation²⁰.

To test how much allogrooming would be elicited by ergosterol vs sham application, we observed the behaviour of two individually colour-coded nestmates (by application of a dot on the gaster with marker pens (uniPOSCA, POSCA) on the day before the experiment) towards individual ants that had been treated either with acetone only (n = 23) or with the ergosterol solution (n = 22; ants treated with the chemicals were not colour-marked). Immediately after treatment, the ergosterol- or sham-treated individual was added to two nestmates in a round plastic box (Ø 3.5 cm; Bioswisstec, 10035) with humidified plastered floor and Fluon (Whitford, GP1-CLPBB1K)-coated walls, using forceps cleaned with methanol, followed by acetone. Videos were recorded for 30 min each (2 replicates per camera, 4 cameras in parallel, using digital microscope cameras (MicroDirect 1080p HD, Celestron) with VirtualDub software v1.10.4 (http://www.virtualdub.org/)). Videos were obtained in a randomized manner and labels did not contain treatment information so that the observer was blind to the application of the treated, unlabelled individual during behavioural observations. The number of allogrooming events towards the treated individual was noted per replicate as above, except here using manual annotation.

We tested for differences in the number of allogrooming events elicited in nestmates (1) between the individual vs social lines (Fig. 3a) and (2) between the sham-treated vs ergosterol- (Fig. 3c) or cholesterol- (Fig. 3d) treated ants in the two bioassays, using non-parametric WRST for independent samples. Note that in Fig. 3c,d we performed the statistics on the raw data but display the fold change of allogrooming for each treatment group relative to its acetone control. For each of the individual and social lines (Fig. 3a), we obtained a single sum of allogrooming events from the three biological replicates. Statistics are reported in Supplementary Table 1; for allogrooming elicitation of the subset of the R1-dominant lines only (highlighted symbols in Fig. 3a), see Supplementary Table 2.

We used workers of the invasive Argentine ant, Linepithema humile, as host species. As typical for invasive ants, populations of this species lack territorial structuring and instead consist of interconnected nests forming a single supercolony with constant exchange of individuals between nests^{40 (link)}. We collected L. humile queens, workers and brood in 2011, 2016 and 2022 from its main supercolony in Europe that extends more than 6,000 km along the coasts of Portugal, Spain and France^{40 (link)–42 (link)}, from a field population close to Sant Feliu de Guíxols, Spain (41° 49’ N, 3° 03’ E). Field-collected ants were reared in large stock colonies in the laboratory. For the experiments, we sampled worker ants from outside the brood chambers and placed them into petri dishes with plastered ground (Alabastergips, Boesner, BAG), subjected to their respective treatments as detailed below. Experiments were carried out in a temperature- and humidity-controlled room at 23 °C, 65% relative humidity and a 12 h day/night light cycle. During experiments, ants were provided with ad libitum access to a sucrose-water solution (100 g l⁻¹) and plaster was watered every 2–3 d to keep humidity high.
Collection of this unprotected species from the field was in compliance with international regulations, such as the Convention on Biological Diversity and the Nagoya Protocol on Access and Benefit-Sharing (ABS, permit numbers ABSCH-IRCC-ES-260624-1 ESNC126 and SF0171/22). All experimental work followed European and Austrian law and institutional ethical guidelines.