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Albert Cardona<p><span class="h-card" translate="no"><a href="https://biologists.social/@biorxiv_neursci" class="u-url mention" rel="nofollow noopener" target="_blank">@<span>biorxiv_neursci</span></a></span> </p><p>Latest from Stefanie Hampel and Andrew Seeds's labs.</p><p>"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"</p><p><a href="https://mathstodon.xyz/tags/neuroscience" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>neuroscience</span></a> <a href="https://mathstodon.xyz/tags/connectomics" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>connectomics</span></a> <a href="https://mathstodon.xyz/tags/Drosophila" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Drosophila</span></a></p>
Albert Cardona<p>From Elizabeth Marin at Zoology Dept., Cambridge University:</p><p>"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."</p><p>"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 :)."</p><p><a href="https://www.jobs.cam.ac.uk/job/51256/" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://www.</span><span class="">jobs.cam.ac.uk/job/51256/</span><span class="invisible"></span></a></p><p><a href="https://mathstodon.xyz/tags/neuroscience" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>neuroscience</span></a> <a href="https://mathstodon.xyz/tags/connectomics" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>connectomics</span></a> <a href="https://mathstodon.xyz/tags/mosquito" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>mosquito</span></a> <a href="https://mathstodon.xyz/tags/VolumeEM" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>VolumeEM</span></a></p>
Albert Cardona<p>"Comparative connectomics of Drosophila descending and ascending neurons", Tomke Stürner et al. 2025 (Greg Jefferis and Katharina Eichler's labs).<br><a href="https://www.nature.com/articles/s41586-025-08925-z" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://www.</span><span class="ellipsis">nature.com/articles/s41586-025</span><span class="invisible">-08925-z</span></a></p><p>Compares between males and females.</p><p><a href="https://mathstodon.xyz/tags/neuroscience" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>neuroscience</span></a> <a href="https://mathstodon.xyz/tags/Drosophila" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Drosophila</span></a> <a href="https://mathstodon.xyz/tags/connectomics" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>connectomics</span></a> <a href="https://mathstodon.xyz/tags/vEM" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>vEM</span></a> <a href="https://mathstodon.xyz/tags/volumeEM" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>volumeEM</span></a></p>
Albert Cardona<p>If you are going to <a href="https://mathstodon.xyz/tags/cosyne2025" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>cosyne2025</span></a> do check out Yijie Yin's poster 2-040 in Poster Session 2:</p><p>"Connectome Interpreter: a toolkit for efficient connectome exploration and hypothesis generation"<br><a href="https://www.cosyne.org/poster-session-2" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://www.</span><span class="">cosyne.org/poster-session-2</span><span class="invisible"></span></a></p><p>See also her software repository for the Connectome Interpreter:<br><a href="https://github.com/YijieYin/connectome_interpreter" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">github.com/YijieYin/connectome</span><span class="invisible">_interpreter</span></a></p><p><a href="https://mathstodon.xyz/tags/neuroscience" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>neuroscience</span></a> <a href="https://mathstodon.xyz/tags/connectomics" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>connectomics</span></a></p>
Albert Cardona<p>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:</p><p>"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."</p><p><a href="https://mathstodon.xyz/tags/neuroscience" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>neuroscience</span></a> <a href="https://mathstodon.xyz/tags/connectomics" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>connectomics</span></a> <a href="https://mathstodon.xyz/tags/CellTypes" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>CellTypes</span></a></p>
Albert Cardona<p>"Infrequent strong connections constrain connectomic predictions of neuronal function", Currier and Clandinin<br><a href="https://www.biorxiv.org/content/10.1101/2025.03.06.641774v1.abstract" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://www.</span><span class="ellipsis">biorxiv.org/content/10.1101/20</span><span class="invisible">25.03.06.641774v1.abstract</span></a></p><p>Quite the reversal from studies showing that deriving connectomes from correlated neural activity is not accurate because of lacking a unique solution:</p><p>"we show that physiology is a stronger predictor of wiring than wiring is of physiology"</p><p><a href="https://mathstodon.xyz/tags/neuroscience" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>neuroscience</span></a> <a href="https://mathstodon.xyz/tags/Drosophila" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Drosophila</span></a> <a href="https://mathstodon.xyz/tags/connectomics" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>connectomics</span></a></p>
flypapers<p>📰 "Serotonin selectively modulates visual responses of object motion detection in Drosophila"<br><a href="https://www.biorxiv.org/content/10.1101/2025.03.21.644681v1?rss=1" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://www.</span><span class="ellipsis">biorxiv.org/content/10.1101/20</span><span class="invisible">25.03.21.644681v1?rss=1</span></a><br> <a href="https://biologists.social/tags/Connectomics" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Connectomics</span></a><br> <a href="https://biologists.social/tags/Drosophila" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Drosophila</span></a></p>
Albert Cardona<p>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<br><a href="https://link.springer.com/article/10.1007/s12038-025-00513-7" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">link.springer.com/article/10.1</span><span class="invisible">007/s12038-025-00513-7</span></a></p><p><a href="https://mathstodon.xyz/tags/Celegans" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Celegans</span></a> <a href="https://mathstodon.xyz/tags/connectomics" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>connectomics</span></a> <a href="https://mathstodon.xyz/tags/neuroscience" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>neuroscience</span></a></p>
Diffusion Imaging in Python<p>Excited to announce Prof. Maxime Descoteaux from Université de Sherbrooke &amp; SCIL, a pioneer in advanced diffusion MRI and connectomics, as a speaker at <a href="https://mastodon.social/tags/DIPYworkshop2025" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>DIPYworkshop2025</span></a>! 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! <a href="https://mastodon.social/tags/Neuroimaging" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Neuroimaging</span></a> </p><p><a href="https://mastodon.social/tags/Connectomics" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Connectomics</span></a> <a href="https://mastodon.social/tags/BrainMapping" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>BrainMapping</span></a>&nbsp;<a href="https://mastodon.social/tags/MRI" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>MRI</span></a> <a href="https://mastodon.social/tags/DIPY" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>DIPY</span></a> <a href="https://mastodon.social/tags/MedicalImaging" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>MedicalImaging</span></a> <a href="https://mastodon.social/tags/opensource" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>opensource</span></a> <a href="https://mastodon.social/tags/learn" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>learn</span></a></p>
Albert Cardona<p>Now that's a big deal, and from a very credible source:</p><p>"Self-supervised image restoration in coherent X-ray neuronal microscopy", Laugros et al. (Alexandra Pacureanu) 2025<br><a href="https://www.biorxiv.org/content/10.1101/2025.02.10.633538v1.full" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://www.</span><span class="ellipsis">biorxiv.org/content/10.1101/20</span><span class="invisible">25.02.10.633538v1.full</span></a></p><p>"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."</p><p>X-ray nanoholography took a turn towards higher resolution and higher throughput.</p><p><a href="https://mathstodon.xyz/tags/XNH" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>XNH</span></a> <a href="https://mathstodon.xyz/tags/connectomics" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>connectomics</span></a> <a href="https://mathstodon.xyz/tags/neuroscience" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>neuroscience</span></a></p>
Albert Cardona<p><span class="h-card" translate="no"><a href="https://mastodon.social/@brembs" class="u-url mention" rel="nofollow noopener" target="_blank">@<span>brembs</span></a></span> This is very true as well in my field. Very incomplete data sets in <a href="https://mathstodon.xyz/tags/connectomics" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>connectomics</span></a> 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.</p>
Albert Cardona<p><span class="h-card" translate="no"><a href="https://mastodon.social/@katchwreck" class="u-url mention" rel="nofollow noopener" target="_blank">@<span>katchwreck</span></a></span> For sure we won't understand how the brain works until the role of astrocytes and other glial cells is fully understood.<br>The <a href="https://mathstodon.xyz/tags/connectome" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>connectome</span></a> though is understood as the wiring diagram where neurons are nodes and edges are synaptic connections. For additional interactions there's the "<a href="https://mathstodon.xyz/tags/neuromodulome" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>neuromodulome</span></a>" for e.g., neuropeptide/neuromodulator vs. the corresponding receptor, like in this paper by Lidia Ripoll-Sánchez et al. 2023 on C. elegans: <br>"The neuropeptidergic connectome of C. elegans" <a href="https://www.cell.com/neuron/fulltext/S0896-6273(23)00756-0" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://www.</span><span class="ellipsis">cell.com/neuron/fulltext/S0896</span><span class="invisible">-6273(23)00756-0</span></a><br><a href="https://mathstodon.xyz/tags/neuroscience" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>neuroscience</span></a> <a href="https://mathstodon.xyz/tags/Celegans" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Celegans</span></a> <a href="https://mathstodon.xyz/tags/connectomics" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>connectomics</span></a></p>
Albert Cardona<p>Excellent to see serious efforts at tackling the proofreading problem in <a href="https://mathstodon.xyz/tags/connectomics" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>connectomics</span></a>:</p><p>"Global Neuron Shape Reasoning with Point Affinity Transformers", Troidl et al. 2024 (Srini Turaga's lab)<br><a href="https://www.biorxiv.org/content/10.1101/2024.11.24.625067v1" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://www.</span><span class="ellipsis">biorxiv.org/content/10.1101/20</span><span class="invisible">24.11.24.625067v1</span></a></p><p>"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."</p><p>An approach suited for systems where cell types are fairly stereotyped like the <a href="https://mathstodon.xyz/tags/Drosophila" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Drosophila</span></a> brain.</p><p><a href="https://mathstodon.xyz/tags/neuroscience" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>neuroscience</span></a></p>
Albert Cardona<p><span class="h-card" translate="no"><a href="https://fediscience.org/@philiphubbard" class="u-url mention" rel="nofollow noopener" target="_blank">@<span>philiphubbard</span></a></span> Quite the journey since Aljoscha Nern's early multi-color flp-out labelings of Drosophila optic lobe neurons!</p><p>Compare (2015):</p><p>"Optimized tools for multicolor stochastic labeling reveal diverse stereotyped cell arrangements in the fly visual system", Nern et al. 2015<br><a href="https://www.pnas.org/doi/abs/10.1073/pnas.1506763112" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://www.</span><span class="ellipsis">pnas.org/doi/abs/10.1073/pnas.</span><span class="invisible">1506763112</span></a></p><p>With (2024):</p><p>"Connectome-driven neural inventory of a complete visual system", Nern et al. 2024<br><a href="https://www.biorxiv.org/content/10.1101/2024.04.16.589741v2" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://www.</span><span class="ellipsis">biorxiv.org/content/10.1101/20</span><span class="invisible">24.04.16.589741v2</span></a></p><p><a href="https://mathstodon.xyz/tags/neuroscience" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>neuroscience</span></a> <a href="https://mathstodon.xyz/tags/Drosophila" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Drosophila</span></a> <a href="https://mathstodon.xyz/tags/vision" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>vision</span></a> <a href="https://mathstodon.xyz/tags/connectomics" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>connectomics</span></a></p>
Albert Cardona<p><span class="h-card" translate="no"><a href="https://fediscience.org/@eLife" class="u-url mention" rel="nofollow noopener" target="_blank">@<span>eLife</span></a></span> Above, Markram et al. with a computational model of a column of the rat barrel cortex, building on their work from 2015. Still no densely reconstructed neurons, so the anatomy is collated largely from sparsely labeled neurons in light microscopy volumes. Synapses are inferred from sparse sampling with ephys and some educated guesswork, based also on data from volume electron microscopy from other brain areas in mouse.</p><p>The published reviews are rather on point, make very interesting reading.</p><p><a href="https://mathstodon.xyz/tags/neuroscience" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>neuroscience</span></a> <a href="https://mathstodon.xyz/tags/connectomics" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>connectomics</span></a> <a href="https://mathstodon.xyz/tags/CompNeurosci" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>CompNeurosci</span></a></p>
Replied in thread

@tdverstynen @ekmiller

Indeed – and functional imaging is much less than half the story too. Without transcriptomics we are all flying blind. And without theoretical neuroscience we won't make much sense of anything.

What's neat about connectomics is that it provides a framework onto which map activity, resolve ambiguities in activity data without enough temporal resolution, and map receptors and neurotransmitters. It's also dense: complete, all neurons are there, independently of the idiosyncrasies of genetic driver lines.

Whole adult fly brain connectome for FAFB (female adult fly brain) – last year in preprint form, today as an immersive feature in Nature.

140,000 neurons, over 50 million synapses – organised into over 8,000 cell types. (VNC not included.)

nature.com/immersive/d42859-02

The whole connectome: Dorkenwald et al. 2024 (Seung, Murthy) nature.com/articles/s41586-024

Cell types: Schlegel et al. 2024 (Jefferis) nature.com/articles/s41586-024 by @uni_matrix

Network statistics: Lin et al. 2024 (Murthy) nature.com/articles/s41586-024

Visual system: Garner et al. 2024 (Wernet, Kim) nature.com/articles/s41586-024 and Matsliah et al. (Murthy, Seung) nature.com/articles/s41586-024

Seung also put out a solo paper on predicting visual function from the connectome: nature.com/articles/s41586-024

Control of halting in walking: Sapkal et al. 2024 (Bidaye) nature.com/articles/s41586-024

FAFB imaged by @davi 's group back in 2018: cell.com/cell/fulltext/S0092-8