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#connectomics

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Albert Cardona

@biorxiv_neursci

#POINTseq : "(projections of interest by sequencing), a high-throughput and user-friendly barcoded connectomics method that uses cell type specific barcoding and sequencing to rapidly map single-cell projections of a cell type of interest for thousands of neurons per animal."

"We then applied POINTseq to midbrain dopaminergic neurons and reconstructed the brain-wide single-cell projections of 3,813 dopaminergic neurons in ventral tegmental area (VTA) and substantia nigra pars compacta (SNc)."

From Justus Kebschull's lab.

#connectomics#neuroscience
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Albert Cardona

@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.

Public
Albert Cardona

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)
biorxiv.org/content/10.1101/20

"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.

bioRxiv · Global Neuron Shape Reasoning with Point Affinity TransformersConnectomics is a subfield of neuroscience that aims to map the brain’s intricate wiring diagram. Accurate neuron segmentation from microscopy volumes is essential for automating connectome reconstruction. However, current state-of-the-art algorithms use image-based convolutional neural networks that are limited to local neuron shape context. Thus, 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. Evaluated on three benchmark datasets derived from state-of-the-art connectomes, our method outperforms point transformers, graph neural networks, and unsupervised clustering baselines. [1][1] ### Competing Interest Statement The authors have declared no competing interest. [1]: #fn-3
#neuroscience
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Albert Cardona

@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
pnas.org/doi/abs/10.1073/pnas.

With (2024):

"Connectome-driven neural inventory of a complete visual system", Nern et al. 2024
biorxiv.org/content/10.1101/20

#neuroscience#Drosophila#vision
Public
Teixi

Top notch paper ever read:
Authors mastering readability & teachability!

Cannot choose a single figure or table to attach because all lookalike essential!

35pg to get up--to-date current theoretical grounds, biological morphology, technological devices, data acquisition, alignment error correction, compression, storage, analysis, labeling, querying, & more!

#Survey of #Visualization & #Analysis
High-Resolution #Connectomics
@hpfister ea.
doi.org/10.1111/cgf.14574

HT @uni_matrix
#neuroscience

Public
Albert Cardona

Today the peer-reviewed version of our preprint is out:

"The #connectome of an insect brain"
science.org/doi/10.1126/scienc

Congrats to co-first authors Michael Winding and Benjamin Pedigo, and to all our lab members and collaborators who made this work possible over the years. A journey that started over 10 years ago–and yet this is but a new beginning. So much more to come.

See my #tootprint on the preprint from back in the Autumn: mathstodon.xyz/@albertcardona/

The data is available both as supplements and directly via #CATMAID thanks to hosting by the #VirtualFlyBrain:
l1em.catmaid.virtualflybrain.o)

(The "Winding, Pedigo et al. 2023" annotation listing all included neurons will appear very soon in an upcoming update.)

The connectome of the Drosophila larval brain. The morphologies of all brain neurons, reconstructed from a synapse-resolution EM volume, and the synaptic connectivity matrix of an entire brain. This connectivity information was used to hierarchically cluster all brains into 93 cell types, which were internally consistent based on morphology and known function.
#neuroscience#connectomics#Drosophila
Public
Philipp Kittler

It's time for an #introduction.

In 2019, I changed my suit for a lab coat after 13 years as a #neuromarketing consultant. Since then, I have been studying #biophysics at Humboldt University in #Berlin with the goal of an master's degree in #neuroscience. My brain wants to understand brains. 😅

I am interested in #NMR / #MRI / #fMRI, #CryoElectronMicroscopy, #connectomics, cerebral #organoids, simulation and modeling of processes and networks respectively.

Public
Albert Cardona

3,013 neurons, half a million synapses: the complete #connectome of the whole #Drosophila larval brain!

Winding, Pedigo et al. 2022. "The connectome of an insect brain" biorxiv.org/content/10.1101/20

We’ve mapped and analysed its circuit architecture, from sensory neurons to brain output neurons, as reconstructed from volume electron microscopy, and here is what we found. 1/

Rendering of all 3,013 neurons that make up the brain of the larva of the vinegar fly, Drosophila melanogaster.
#neuroscience#connectomics#vEM
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Albert Cardona

#introduction To fill in my profile tags, a thread:

#TrakEM2 open source software mostly for #connectomics, and supports manual and automatically montaging and aligning overlapping 2D image tiles (with #SIFT features and rigid or elastic transformation models), and reconstructing by painting volumes or tracing branched neuronal arbors neurons plus synapses to map a #connectome from #vEM (volume electron microscopy).

See: journals.plos.org/plosone/arti

Git repository at: github.com/trakem2/

Figure 2 of Cardona et al. 2012 PLoS ONE, illustrating neuronal arbors reconstructed using TrakEM2 software.
Public
Wei-Chung Allen Lee

Hello! A brief #introduction - I’m an Asst Prof at Boston Children’s Hospital and Harvard Medical School. I study organizational principles of #neuralNetworks. We study #neuralCircuits underlying #motorControl, #sensoryIntegration, and #decisionMaking across species. We also develop methods for synapse-resolution #connectomics. Looking forward to learning more!

Lab: lee.hms.harvard.edu/

Lee Lab | Wei-Chung Allen Lee, PhDLee Lab | Wei-Chung Allen Lee, PhDUncovering the circuit basis of action selection, execution, and refinement
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