📰 "Cross-species comparative connectomics reveals the evolution of an olfactory circuit"
https://www.biorxiv.org/content/10.1101/2025.06.11.659158v1?rss=1
#Neuroscience
#Connectomics
#Evolution
#Drosophila #Behaviour #Larva
#connectomics
Davi Bock on the fly connectome, its uses, its impact beyond – and his life.
https://www.uvm.edu/magazine/news/thoughts-fly
#neuroscience #neuroscientists #biographies #connectomics
"Single-cell type analysis of wing premotor circuits in the ventral nerve cord of Drosophila melanogaster", Ehrhardt et al. 2025
https://elifesciences.org/reviewed-preprints/106548
📰 "Drosophila larval Odd neurons process innate and learned information to regulate chemotaxis behavior"
https://www.biorxiv.org/content/10.1101/2025.06.04.657871v1?rss=1
#Connectomics
#Drosophila #Behaviour #Larva
Latest from Stefanie Hampel and Andrew Seeds's labs.
"we use a serial section electron microscopy reconstruction of a full adult fly brain to identify nearly all of BMN pre- and postsynaptic partners, uncovering circuit pathways that control head grooming"
From Elizabeth Marin at Zoology Dept., Cambridge University:
"Together with Greg Jefferis (MRC LMB, Cambridge), Wei-Chung Allen Lee (Harvard Medical School), and Meg Younger (Boston University), I have secured a £4.8M Wellcome Discovery Award to generate a mosquito brain connectome and investigate chemosensory circuits involved in human host-seeking."
"We are currently recruiting for two research assistant positions based in the Zoology department at Cambridge University. Please share this post with any likely candidates :)."
Arrived at the Volume Electron Microscopy
Gordon Research Conference
https://www.grc.org/volume-electron-microscopy-conference/2025/
near Barcelona. I will speak about Volume EM and #connectomics in marine zoomplankton.
"Comparative connectomics of Drosophila descending and ascending neurons", Tomke Stürner et al. 2025 (Greg Jefferis and Katharina Eichler's labs).
https://www.nature.com/articles/s41586-025-08925-z
Compares between males and females.
If you are going to #cosyne2025 do check out Yijie Yin's poster 2-040 in Poster Session 2:
"Connectome Interpreter: a toolkit for efficient connectome exploration and hypothesis generation"
https://www.cosyne.org/poster-session-2
See also her software repository for the Connectome Interpreter:
https://github.com/YijieYin/connectome_interpreter
This conundrum may originate in the cell type-centric view of connectivity, rather than considering the actual connectome into the analysis. The authors more or less say as much in the discussion:
"two cell types can have similar physiology and relatively similar connectivity without sharing input cell types. Overall, this analysis suggests that defining connection similarity by cell types may overly discretize the network, obscuring structure-function relationships."
"Infrequent strong connections constrain connectomic predictions of neuronal function", Currier and Clandinin
https://www.biorxiv.org/content/10.1101/2025.03.06.641774v1.abstract
Quite the reversal from studies showing that deriving connectomes from correlated neural activity is not accurate because of lacking a unique solution:
"we show that physiology is a stronger predictor of wiring than wiring is of physiology"
📰 "Serotonin selectively modulates visual responses of object motion detection in Drosophila"
https://www.biorxiv.org/content/10.1101/2025.03.21.644681v1?rss=1
#Connectomics
#Drosophila
A review: "C. elegans wired and wireless connectome: insights into principles of nervous system structure and function", by K Venkatesh, L Ripoll-Sánchez, I Beets, WR Schafer 2025
https://link.springer.com/article/10.1007/s12038-025-00513-7
Excited to announce Prof. Maxime Descoteaux from Université de Sherbrooke & SCIL, a pioneer in advanced diffusion MRI and connectomics, as a speaker at #DIPYworkshop2025! Join us to explore his cutting-edge presentation on Spherical Harmonic Reconstruction with derivative methods such as QBall, CSA and CSD. Don’t miss it! #Neuroimaging
#Connectomics #BrainMapping #MRI #DIPY #MedicalImaging #opensource #learn
Now that's a big deal, and from a very credible source:
"Self-supervised image restoration in coherent X-ray neuronal microscopy", Laugros et al. (Alexandra Pacureanu) 2025
https://www.biorxiv.org/content/10.1101/2025.02.10.633538v1.full
"we present a self-supervised image restoration approach that simultaneously improves spatial resolution, contrast, and data acquisition speed. This enables revealing synapses with XNH, marking a major milestone in the quest for generating connectomes of full mammalian brains."
X-ray nanoholography took a turn towards higher resolution and higher throughput.
@brembs This is very true as well in my field. Very incomplete data sets in #connectomics that then modelers pick up and run with, and don't understand when we show a lack of enthusiasm for their findings because the many limitations of the data weren't considered. To be fair, such limitations are as buried as possible in most manuscripts.
@katchwreck For sure we won't understand how the brain works until the role of astrocytes and other glial cells is fully understood.
The #connectome though is understood as the wiring diagram where neurons are nodes and edges are synaptic connections. For additional interactions there's the "#neuromodulome" for e.g., neuropeptide/neuromodulator vs. the corresponding receptor, like in this paper by Lidia Ripoll-Sánchez et al. 2023 on C. elegans:
"The neuropeptidergic connectome of C. elegans" https://www.cell.com/neuron/fulltext/S0896-6273(23)00756-0
#neuroscience #Celegans #connectomics
I spent time in my #neuroscience PhD painstakingly tracing neuron bodies and dendrites, and this is just amazing. What was that program called - Neurolucida? Did anyone else here trace neurons back in the days before #connectomics was a word?
https://e11.bio/news/roadmap
Excellent to see serious efforts at tackling the proofreading problem in #connectomics:
"Global Neuron Shape Reasoning with Point Affinity Transformers", Troidl et al. 2024 (Srini Turaga's lab)
https://www.biorxiv.org/content/10.1101/2024.11.24.625067v1
"we introduce a new framework that reasons over global neuron shape with a novel point affinity transformer. Our framework embeds a (multi-)neuron point cloud into a fixed-length feature set from which we can decode any point pair affinities, enabling clustering neuron point clouds for automatic proofreading. We also show that the learned feature set can easily be mapped to a contrastive embedding space that enables neuron type classification using a simple KNN classifier. Our approach excels in two demanding connectomics tasks: proofreading segmentation errors and classifying neuron types."
An approach suited for systems where cell types are fairly stereotyped like the #Drosophila brain.
@philiphubbard Quite the journey since Aljoscha Nern's early multi-color flp-out labelings of Drosophila optic lobe neurons!
Compare (2015):
"Optimized tools for multicolor stochastic labeling reveal diverse stereotyped cell arrangements in the fly visual system", Nern et al. 2015
https://www.pnas.org/doi/abs/10.1073/pnas.1506763112
With (2024):
"Connectome-driven neural inventory of a complete visual system", Nern et al. 2024
https://www.biorxiv.org/content/10.1101/2024.04.16.589741v2
Client Info
Server: https://mastodon.social
Version: 2025.04
Repository: https://github.com/cyevgeniy/lmst