Biology subjects

Eckstein, N.

Publications and source records attributed to Eckstein, N..

6 recordsLinked to original sources

bioRxiv2024

Quantitative Attributions with Counterfactuals

We address the problem of explaining the decision process of deep neural network classifiers on images, which is of particular importance in biomedical datasets where class-relevant differences are not always obvious to a human observer. Our proposed solution, termed quantitative attribution with counterfactuals (QuAC), generates visual explanations that highlight class-relevant differences by attributing the classifier decision to changes of visual features in small parts of an image. To that end, we train a separate network to generate counterfactual images (i.e., to translate images between different classes). We then find the most important differences using novel discriminative attribution methods. Crucially, QuAC allows scoring of the attribution and thus provides a measure to quantify and compare the fidelity of a visual explanation. We demonstrate the suitability and limitations of QuAC on two datasets: (1) a synthetic dataset with known class differences, representing different levels of protein aggregation in cells and (2) an electron microscopy dataset of D. melanogaster synapses with different neurotransmitters, where QuAC reveals so far unknown visual differences. We further discuss how QuAC can be used to interrogate mispredictions to shed light on unexpected inter-class similarities and intra-class differences.

bioinformatics↗

bioRxiv2023

Network Statistics of the Whole-Brain Connectome of Drosophila

Brains comprise complex networks of neurons and connections. Network analysis applied to the wiring diagrams of brains can offer insights into how brains support computations and regulate information flow. The completion of the first whole-brain connectome of an adult Drosophila, the largest connectome to date, containing 130,000 neurons and millions of connections, offers an unprecedented opportunity to analyze its network properties and topological features. To gain insights into local connectivity, we computed the prevalence of two- and three-node network motifs, examined their strengths and neurotransmitter compositions, and compared these topological metrics with wiring diagrams of other animals. We discovered that the network of the fly brain displays rich club organization, with a large population (30% percent of the connectome) of highly connected neurons. We identified subsets of rich club neurons that may serve as integrators or broadcasters of signals. Finally, we examined subnetworks based on 78 anatomically defined brain regions or neuropils. These data products are shared within the FlyWire Codex and will serve as a foundation for models and experiments exploring the relationship between neural activity and anatomical structure.

neuroscience↗

bioRxiv2023

Neuronal wiring diagram of an adult brain

Connections between neurons can be mapped by acquiring and analyzing electron microscopic (EM) brain images. In recent years, this approach has been applied to chunks of brains to reconstruct local connectivity maps that are highly informative, yet inadequate for understanding brain function more globally. Here, we present the first neuronal wiring diagram of a whole adult brain, containing 5x107 chemical synapses between [~]130,000 neurons reconstructed from a female Drosophila melanogaster. The resource also incorporates annotations of cell classes and types, nerves, hemilineages, and predictions of neurotransmitter identities. Data products are available by download, programmatic access, and interactive browsing and made interoperable with other fly data resources. We show how to derive a projectome, a map of projections between regions, from the connectome. We demonstrate the tracing of synaptic pathways and the analysis of information flow from inputs (sensory and ascending neurons) to outputs (motor, endocrine, and descending neurons), across both hemispheres, and between the central brain and the optic lobes. Tracing from a subset of photoreceptors all the way to descending motor pathways illustrates how structure can uncover putative circuit mechanisms underlying sensorimotor behaviors. The technologies and open ecosystem of the FlyWire Consortium set the stage for future large-scale connectome projects in other species.

neuroscience↗

bioRxiv2023

A leaky integrate-and-fire computational model based on the connectome of the entire adult Drosophila brain reveals insights into sensorimotor processing

The forthcoming assembly of the adult Drosophila melanogaster central brain connectome, containing over 125,000 neurons and 50 million synaptic connections, provides a template for examining sensory processing throughout the brain. Here, we create a leaky integrate-and-fire computational model of the entire Drosophila brain, based on neural connectivity and neurotransmitter identity, to study circuit properties of feeding and grooming behaviors. We show that activation of sugar-sensing or water-sensing gustatory neurons in the computational model accurately predicts neurons that respond to tastes and are required for feeding initiation. Computational activation of neurons in the feeding region of the Drosophila brain predicts those that elicit motor neuron firing, a testable hypothesis that we validate by optogenetic activation and behavioral studies. Moreover, computational activation of different classes of gustatory neurons makes accurate predictions of how multiple taste modalities interact, providing circuit-level insight into aversive and appetitive taste processing. Our computational model predicts that the sugar and water pathways form a partially shared appetitive feeding initiation pathway, which our calcium imaging and behavioral experiments confirm. Additionally, we applied this model to mechanosensory circuits and found that computational activation of mechanosensory neurons predicts activation of a small set of neurons comprising the antennal grooming circuit that do not overlap with gustatory circuits, and accurately describes the circuit response upon activation of different mechanosensory subtypes. Our results demonstrate that modeling brain circuits purely from connectivity and predicted neurotransmitter identity generates experimentally testable hypotheses and can accurately describe complete sensorimotor transformations.

neuroscience↗

bioRxiv2020

Automatic whole cell organelle segmentation in volumetric electron microscopy

Cells contain hundreds of different organelle and macromolecular assemblies intricately organized relative to each other to meet any cellular demands. Obtaining a complete understanding of their organization is challenging and requires nanometer-level, threedimensional reconstruction of whole cells. Even then, the immense size of datasets and large number of structures to be characterized requires generalizable, automatic methods. To meet this challenge, we developed an analysis pipeline for comprehensively reconstructing and analyzing the cellular organelles in entire cells imaged by focused ion beam scanning electron microscopy (FIB-SEM) at a near-isotropic size of 4 or 8 nm per voxel. The pipeline involved deep learning architectures trained on diverse samples for automatic reconstruction of 35 different cellular organelle classes - ranging from endoplasmic reticulum to microtubules to ribosomes - from multiple cell types. Automatic reconstructions were used to directly quantify various previously inaccessible metrics about these structures, including their spatial interactions. We show that automatic organelle reconstructions can also be used to automatically register light and electron microscopy images for correlative studies. We created an open data and open source web repository, OpenOrganelle, to share the data, computer code, and trained models, enabling scientists everywhere to query and further reconstruct the datasets.

cell biology↗

bioRxiv2020

Neurotransmitter Classification from Electron Microscopy Images at Synaptic Sites in Drosophila

High-resolution electron microscopy of nervous systems enables the reconstruction of connectomes. A key piece of missing information from connectomes is the synaptic sign. We show that for D. melanogaster, artificial neural networks can predict the transmitter type released at synapses from electron micrographs and thus add putative signs to connections. Our network discriminates between six transmitters (acetylcholine, glutamate, GABA, serotonin, dopamine, octopamine) with an average accuracy of 87%/94% for synapses/entire neurons. We developed an explainability method to reveal which features our network is using and found significant ultrastructural differences between the classical transmitters. We predict transmitters in two connectomes and characterize morphological and connection properties of tens of thousands of neurons classed by predicted transmitter expression. We find that hemilineages in D. melanogaster largely express only one fastacting transmitter among their neurons. Furthermore, we show that neurons with different transmitters may differ in features like polarization and projection targets.

neuroscience↗