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51 results for “Mushroom body”
Calcium imaging of odor responses in the fruit fly mushroom body
<p><strong>Abstract</strong></p> <p>This dataset contains olfactory responses in the third stage of the olfactory circuit in fruit flies: the mushroom body. The responses are recorded with the GCaMP3 sensor. The methods used to collect the data and the procedures to process them are presented in detail in Campbell et al., 2013, Journal of Neuroscience. The dataset was also used in a recent manuscript by Srinivasan et al., 2023.</p> <p><strong>Methods</strong></p> <p>Please refer to Campbell et al., 2013, Journal of Neuroscience for details. Here, we present a description of how the data was collected, the odors presented, and the analysis, excerpted from Campbell et al., 2013.</p> <p><strong>Animal preparation</strong></p> <p>Flies carrying the genetically encoded calcium sensor UAS-GCaMP3 (Tian et al., 2009) were crossed with OK107-Gal4 flies (Connolly et al., 1996) to drive GCaMP3 expression in essentially all KCs (Lee and Luo, 1999; Aso et al., 2009). All experiments were conducted on female F1 heterozygotes from this cross, aged 2–5 d post-eclosion. Procedures for animal preparation were as described previously (Turner et al., 2008; Murthy and Turner, 2010; Honegger et al., 2011). Flies were anesthetized temporarily on ice and inserted into a small hole cut in the recording platform. The animal’s head was tilted forward, exposing the olfactory organs to the odor delivery nozzle located on the underside of the plat- form. The fly was fixed in place with fast-drying epoxy (Devcon 5 min epoxy). The top of the fly was bathed in oxygenated saline (Wilson et al., 2004) and the cuticle overlying the brain was dissected away. Air sacs overlying the MBs were pushed aside, but we did not attempt to remove the perineural sheath. To minimize movement of the brain inside the head capsule, we removed the pulsatile organ at the neck and the probos- cis retractor muscles that pass over the caudal aspect of the optic lobes.</p> <p> </p> <p><strong>Odor delivery </strong></p> <p>The following chemicals were used as stimuli: 2-heptanone (CAS #110-43- 0), 3-octanol (CAS #589-98-0), 6-methyl-5-hepten-2-one (CAS #110-93-0), ␣-humulene (CAS #6753-98-6), benzaldehyde (CAS #100-52-7), ethyl lactate (CAS #97-64-3), ethyl octanoate (CAS #106-32-1), hexanal (CAS #66-25-1), isoamyl acetate (CAS #123-92-2), 4-methylcyclo- hexanol (CAS #589-91-3), methyl octanoate (CAS #111-11-5), diethyl suc- cinate (CAS #123-25-1), pentanal (CAS #110-62-3), butyl acetate (CAS #123-86-4), 1-octen-3-ol (CAS #3391-86-4), 1-hepten-3-ol (CAS #4938-52- 7), and pentyl acetate (CAS #628-63-7). Odors were presented using a custom-built delivery system that uses serial air dilutions to control odor concentration while maintaining a constant total airflow of 1 L/min at the fly. Experiments were conducted at an odor dilution of 1:100 or, where appropriate, adjusted to match the concentrations used behaviorally. We used a photo-ionization detector (Aurora Scientific) to match concentrations between the imaging rig and the T-maze and to monitor odor delivery throughout each imaging ex- periment. Odor pulses were created by switching between clean and odorized air streams using a synchronous two-way valve (N-Research). This final valve was located 50 cm from the fly, leading to a delay of 300 ms between valve switching and the odor reaching the fly. The flow path was 1/8 inch in diameter throughout, which enabled the system to work near atmospheric pressure at these flow rates. The distance of the valve from the fly and the large tubing diameter virtually eliminated pressure transients caused by valve switching, as measured by the photo-ionization detector and a hot-wire anemometer.</p> <p><strong>Calcium imaging</strong></p> <p>Two-photon imaging was performed using a Prairie Ultima system (Prairie Technologies) and a Ti-Sapphire laser (Chameleon XR; Coher- ent) tuned to 920 nm delivering 8 –10 mW at the sample. All images were acquired with Olympus water-immersion objectives (LUMPlanFl/IR, 60x, numerical aperture 0.9; LUMPlanFl/IR, 40x, numerical aperture 0.8). Imaging planes were selected to maximize the number of visibleKCs. Typically imaging frames were 300 x 300 pixels, acquired with a pixel dwell time of 1.6 s, yielding frame rates near 3.8 Hz. On average, 120 KCs (range: 60 –170) were monitored in one plane. Custom MATLAB (MathWorks) routines were used to control odor presentation and synchronize stimulus delivery with data acquisition. Data were acquired in 20 s sweeps with a 1 s odor pulse triggered 8 s after sweep onset. The interstimulus interval was 25 s. Stimuli were presented in randomly interleaved fashion, adjusted so that the same odor was never presented twice in succession.</p> <p><strong>Imaging analysis</strong></p> <p>Data were analyzed using MATLAB and R (http://www.R-project.org). To correct for motion within the field of view, frames were aligned using 2D image registration approaches. In many cases, a Fourier-based sub-pixel translation correction was sufficient (Guizar-Sicairos et al., 2008). Some animals required an affine transform to cope with global distortions, such as rotational movement of the brain (Thirion, 1998). Where necessary a nonrigid transform was used to correct more localized dis- tortions (Klein et al., 2010). Fluorescent neural tissue was automatically segmented from the surrounding regions. Pixel intensity values from the area outside this boundary were considered to represent background (tissue autofluorescence plus shot noise) and the mean pixel intensity value from the back- ground was then subtracted from the overall image. To quantify the response of the KCs a small, circular region of interest 6 – 8 pixels in diameter was applied to each cell body. This allowed aver- aging of the pixel intensity values from each cell, treating individual KCs as separate units. Care was taken to ensure that each selected cell re- mained within its region of interest over the whole imaging session. Response amplitudes were calculated as the mean change in fluorescence (dF/F) in the 0.5– 4.5 s window after stimulus onset. A statistical test originally described in Honegger et al. (2011) was used to determine whether a KC responded significantly on a given trial. Briefly, the SD of the baseline activity was obtained 8 s before stimulus onset. The response time course was then smoothed using a five-point running average to control for outliers. The peak dF/F in the 0.5– 4.5 s window after stimulus onset was determined. The response was judged to be significant if this peak was 2.33 SDs greater than the baseline, which corresponds to a one-tailed significance test where alpha = 0.01.</p> <p><br> <strong>References</strong></p> <p>Aso Y, Grübel K, Busch S, Friedrich AB, Siwanowicz I, Tanimoto H (2009) The mushroom body of adult Drosophila characterized by GAL4 drivers. J Neurogenet 23:156 –172. </p> <p>Connolly JB, Roberts IJ, Armstrong JD, Kaiser K, Forte M, Tully T, O’Kane CJ (1996) Associative learning disrupted by impaired Gs signaling in Drosophila mushroom bodies. Science 274:2104 –2107.</p> <p>Honegger KS, Campbell RA, Turner GC (2011) Cellular-resolution population imaging reveals robust sparse coding in the Drosophila mushroom body. J Neurosci 31:11772–11785.</p> <p>Lee T, Luo L (1999) Mosaic analysis with a repressible cell marker for studies of gene function in neuronal morphogenesis. Neuron 22:451– 461.</p> <p>Murthy M, Turner GC (2010) In vivo whole-cell recordings in the Drosophila brain. In: Drosophila neurobiology methods: a laboratory manual (Zhang B, Waddell S, Freeman M, eds). Cold Spring Harbor, NY: Cold Spring Harbor Laboratory.</p> <p>Srinivasan, S., Daste, S., Modi, M., Turner, G., Fleischmann, A. & Navlakha, S (2023). Stochastic coding: a conserved feature of odor representations and its implications for odor discrimination. bioRxiv.</p> <p>Thirion JP (1998) Image matching as a diffusion process: an analogy with Maxwell’s demons. Med Image Anal 2:243–260.</p> <p>Tian L, Hires SA, Mao T, Huber D, Chiappe ME, Chalasani SH, Petreanu L, Akerboom J, McKinney SA, Schreiter ER, Bargmann CI, Jayaraman V, Svoboda K, Looger LL (2009) Imaging neural activity in worms, flies and mice with improved GCaMP calcium indicators. Nat Methods 6:875–881.</p> <p>Turner GC, Bazhenov M, Laurent G (2008) Olfactory representations by Drosophila mushroom body neurons. J Neurophysiol 99:734 –746.</p> <p>Wilson RI, Turner GC, Laurent G (2004) Transformation of olfactory representations in the Drosophila antennal lobe. Science 303:366–370.</p> <p><strong>Usage notes</strong></p> <p>The files are all in csv format, and can be easily opened in R or Python or other programming languages.</p> <p>Please see the README.md file for directions on how to use the data.</p> <p>The dataset included here is broken into two parts. The main dataset was the one that was chiefly used in the Campbell and Srinivasan papers, with the second part containing 7 additional datasets that were used in some figures. A fuller description is available in the README.md file.</p>
Data from: Tissue-specific O-GlcNAcylation profiling identifies substrates in translational machinery in the Drosophila mushroom body contributing to olfactory learning
<p><em>O-</em>GlcNAcylation is a dynamic post-translational modification that diversifies the proteome. Its dysregulation is associated with neurological disorders that impair cognitive function, and yet identification of phenotype-relevant candidate substrates in a brain-region-specific manner remains unfeasible. By combining an <em>O-</em>GlcNAc binding activity derived from<em> Clostridium perfringens</em> OGA (<em>Cp</em>OGA) with TurboID proximity labeling in <em>Drosophila</em>, we developed an <em>O-</em>GlcNAcylation profiling tool that translates <em>O-</em>GlcNAc modification into biotin conjugation for tissue-specific candidate substrates enrichment. We mapped the <em>O-</em>GlcNAc interactome in major brain regions of <em>Drosophila</em> and found that components of the translational machinery, particularly ribosomal subunits, were abundantly <em>O-</em>GlcNAcylated in the mushroom body of <em>Drosophila</em> brain. Hypo-<em>O-</em>GlcNAcylation induced by ectopic expression of active <em>Cp</em>OGA in the mushroom body decreased local translational activity, leading to olfactory learning deficits that could be rescued by dMyc overexpression-induced increase of protein synthesis. Our study provides a useful tool for future dissection of tissue-specific functions of <em>O-</em>GlcNAcylation in <em>Drosophila</em> and suggests a possibility that <em>O-</em>GlcNAcylation impacts cognitive function via regulating regional translational activity in the brain.</p>
Data from: Tissue-specific O-GlcNAcylation profiling identifies substrates in translational machinery in the Drosophila mushroom body contributing to olfactory learning
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Patterns of host plant use do not explain mushroom body expansion in Heliconiini butterflies
<p>The selective pressures leading to the elaboration of downstream, integrative processing centres, such as the mammalian neocortex or insect mushroom bodies, are often unclear. In <em>Heliconius</em> butterflies, the mushroom bodies are three to four times larger than their Heliconiini, and the largest known in Lepidoptera. Heliconiini lay almost exclusively on <em>Passiflora</em>, which exhibit a remarkable diversity of leaf shape, and it has been suggested that the mushroom body expansion of <em>Heliconius</em> may have been driven by the cognitive demands of recognising and learning the leaf shapes of local host plants. We test this hypothesis using two complementary methods: i) phylogenetic comparative analyses to test whether variation in mushroom body size is associated with the morphological diversity of host plants exploited across the Heliconiini; and ii) shape learning experiments using six Heliconiini species. We found that variation in the range of leaf morphologies used by Heliconiini was not associated with mushroom body volume. Similarly, we find interspecific differences in shape learning ability, but <em>Heliconius</em> are not overall better shape learners than other Heliconiini. Together these results suggest that the visual recognition and learning of host plants was not a main factor driving the diversity of mushroom body size in this tribe.</p>
Data & R Code for "Enhanced long-term memory and increased mushroom body plasticity in Heliconius butterflies"
<p>This ZIP file contains the data and R code used to analyse it for the paper "Enhanced long-term memory and increased mushroom body plasticity in Heliconius butterflies". Behavioural and neuroanatomical data are in separate folders.</p>
Data from: Long-term social memory of mate copying in Drosophila melanogaster is localized in the mushroom bodies
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Hybrid neural networks in the mushroom body drive olfactory preference in Drosophila
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Patterns of host plant use do not explain mushroom body expansion in Heliconiini butterflies
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DeepCAD-RT dataset: Drosophila mushroom body
<p>DeepCAD-RT dataset: Drosophila mushroom body</p>
Data from: Age-related mushroom body expansion in male sweat bees and bumble bees
<p>A well-documented phenomenon among social insects is that brain changes occur prior to or at the onset of certain experiences, potentially serving to prime the brain for specific tasks. This insight comes almost exclusively from studies considering developmental maturation in females. As a result, it is unclear whether age-related brain plasticity is consistent across sexes, and to what extent developmental patterns differ. Using confocal microscopy and volumetric analyses, we investigated age-related brain changes coinciding with sexual maturation in the males of the facultatively eusocial sweat bee, <i>Megalopta genalis,</i> and the obligately eusocial bumble bee, <i>Bombus impatiens</i>. We compared volumetric measurements between newly eclosed and reproductively mature males kept isolated in the lab. We found expansion of the mushroom bodies—brain regions associated with learning and memory—with maturation, which were consistent across both species. This age-related plasticity may, therefore, play a functionally-relevant role in preparing male bees for mating, and suggests that developmentally-driven neural restructuring can occur in males, even in species where it is absent in females.</p>
Substituting Mushrooms for Meat to Control Body Weight
ClinicalTrials.gov study NCT01177085. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Data from: Age-related mushroom body expansion in male sweat bees and bumble bees
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mushroom body compartments
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Drosophila Fluorescent Mushroom Bodies
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Data from: Experience-dependent mushroom body plasticity in butterflies: consequences of search complexity and host range
An ovipositing insect experiences many sensory challenges during her search for a suitable host plant. These sensory challenges become exceedingly pronounced when host range increases, as larger varieties of sensory inputs have to be perceived and processed in the brain. Neural capacities can be exceeded upon information overload, inflicting costs on oviposition accuracy. One presumed generalist strategy to diminish information overload is the acquisition of a focused search during its lifetime based on experiences within the current environment, a strategy opposed to a more genetically determined focus expected to be seen in relative specialists. We hypothesized that a broader host range is positively correlated with mushroom body (MB) plasticity, a brain structure related to learning and memory. To test this hypothesis, butterflies with diverging host ranges (Polygonia c-album, Aglais io and Aglais urticae) were subjected to differential environmental complexities for oviposition, after which ontogenetic MB calyx volume differences were compared among species. We found that the relative generalist species exhibited remarkable plasticity in ontogenetic MB volumes; MB growth was differentially stimulated based on the complexity of the experienced environment. For relative specialists, MB volume was more canalized. All in all, this study strongly suggests an impact of host range on brain plasticity in Nymphalid butterflies.
Data from: Distributed cognition and social brains: reductions in mushroom body investment accompanied the origins of sociality in wasps (Hymenoptera: Vespidae)
The social brain hypothesis assumes the evolution of social behaviour changes animals' ecological environments, and predicts evolutionary shifts in social structure will be associated with changes in brain investment. Most social brain models to date assume social behaviour imposes additional cognitive challenges to animals, favouring the evolution of increased brain investment. Here, we present a modification of social brain models, which we term the distributed cognition hypothesis. Distributed cognition models assume group members can rely on social communication instead of individual cognition; these models predict reduced brain investment in social species. To test this hypothesis, we compared brain investment among 29 species of wasps (Vespidae family), including solitary species and social species with a wide range of social attributes (i.e. differences in colony size, mode of colony founding and degree of queen/worker caste differentiation). We compared species means of relative size of mushroom body (MB) calyces and the antennal to optic lobe ratio, as measures of brain investment in central processing and peripheral sensory processing, respectively. In support of distributed cognition predictions, and in contrast to patterns seen among vertebrates, MB investment decreased from solitary to social species. Among social species, differences in colony founding, colony size and caste differentiation were not associated with brain investment differences. Peripheral lobe investment did not covary with social structure. These patterns suggest the strongest changes in brain investment—a reduction in central processing brain regions—accompanied the evolutionary origins of eusociality in Vespidae.
Data from: Using an insect mushroom body circuit to encode route memory in complex natural environments
Ants, like many other animals, use visual memory to follow extended routes through complex environments, but it is unknown how their small brains implement this capability. The mushroom body neuropils have been identified as a crucial memory circuit in the insect brain, but their function has mostly been explored for simple olfactory association tasks. We show that a spiking neural model of this circuit originally developed to describe fruitfly (Drosophila melanogaster) olfactory association, can also account for the ability of desert ants (Cataglyphis velox) to rapidly learn visual routes through complex natural environments. We further demonstrate that abstracting the key computational principles of this circuit, which include one-shot learning of sparse codes, enables the theoretical storage capacity of the ant mushroom body to be estimated at hundreds of independent images.
Data from: Experience-dependent mushroom body plasticity in butterflies: consequences of search complexity and host range
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Data from: Using an insect mushroom body circuit to encode route memory in complex natural environments
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Data from: Distributed cognition and social brains: reductions in mushroom body investment accompanied the origins of sociality in wasps (Hymenoptera: Vespidae)
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