High-fidelity Image Restoration of Large 3D Electron Microscopy Volume.

Volume electron microscopy (VEM) is an essential tool for studying biological structures. Due to the challenges of sample preparation and continuous volumetric imaging, image artifacts are almost inevitable. Such image artifacts complicate further processing both for automated computer vision methods and human experts. Unfortunately, the widely used contrast limited adaptive histogram equalization (CLAHE) can alter the essential relative contrast information about some biological structures. We developed an image-processing pipeline to remove the artifacts and enhance the images without CLAHE. We apply our method to VEM datasets of a Microwasp head. We demonstrate that our method restores the images with high fidelity while preserving the original relative contrast. This pipeline is adaptable to other VEM datasets.

WASPSYN: A Challenge for Domain Adaptive Synapse Detection in Microwasp Brain Connectomes.

The size of image volumes in connectomics studies now reaches terabyte and often petabyte scales with a great diversity of appearance due to different sample preparation procedures. However, manual annotation of neuronal structures (e.g., synapses) in these huge image volumes is time-consuming, leading to limited labeled training data often smaller than 0.001% of the large-scale image volumes in application. Methods that can utilize in-domain labeled data and generalize to out-of-domain unlabeled data are in urgent need. Although many domain adaptation approaches are proposed to address such issues in the natural image domain, few of them have been evaluated on connectomics data due to a lack of domain adaptation benchmarks. Therefore, to enable developments of domain adaptive synapse detection methods for large-scale connectomics applications, we annotated 14 image volumes from a biologically diverse set of Megaphragma viggianii brain regions originating from three different whole-brain datasets and organized the WASPSYN challenge at ISBI 2023. The annotations include coordinates of pre-synapses and post-synapses in the 3D space, together with their one-to-many connectivity information. This paper describes the dataset, the tasks, the proposed baseline, the evaluation method, and the results of the challenge. Limitations of the challenge and the impact on neuroscience research are also discussed. The challenge is and will continue to be available at https://codalab.lisn.upsaclay.fr/competitions/9169. Successful algorithms that emerge from our challenge may potentially revolutionize real-world connectomics research and further the cause that aims to unravel the complexity of brain structure and function.

A complete reconstruction of the early visual system of an adult insect.

For most model organisms in neuroscience, research into visual processing in the brain is difficult because of a lack of high-resolution maps that capture complex neuronal circuitry. The microinsect Megaphragma viggianii, because of its small size and non-trivial behavior, provides a unique opportunity for tractable whole-organism connectomics. We image its whole head using serial electron microscopy. We reconstruct its compound eye and analyze the optical properties of the ommatidia as well as the connectome of the first visual neuropil-the lamina. Compared with the fruit fly and the honeybee, Megaphragma visual system is highly simplified: it has 29 ommatidia per eye and 6 lamina neuron types. We report features that are both stereotypical among most ommatidia and specialized to some. By identifying the "barebones" circuits critical for flying insects, our results will facilitate constructing computational models of visual processing in insects.

A rational account of memory predicts left prefrontal activation during controlled retrieval.

What is the role of the left prefrontal cortex in the controlled retrieval of learned information? We present a theory of declarative retrieval that posits that the amount of control exerted by this region during retrieval is inversely proportional to 1) the frequency and recency of previous experiences with the retrieved memory and 2) the associative strength between the current context and the retrieved memory. This theory is rational in the sense that it claims that declarative retrieval is highly sensitive to the statistical regularities in the environment. We demonstrate how our theory produces precise predictions of response time and neural activity during recall and test these predictions in an experiment that manipulates the frequency of previous experiences and the associative strength to the retrieval cues. Our findings suggest that the control process performed by the left prefrontal cortex directly reflects the demands of the environment on memory.

Role of prefrontal and parietal cortices in associative learning.

Two studies were performed that compared a "Paired" condition in which participants studied paired associates with a "Generated" condition in which participants completed word fragments to produce paired associates. In both tasks, participants were responsible for memory of the material either studied or generated. The experiments revealed significant differences between the responses of a predefined prefrontal region and a predefined parietal region. The parietal region responded more in the Generated condition than the Paired condition, whereas there was no difference in the prefrontal region. On the other hand, the prefrontal region responded to the delay between study and test in both the Paired and Generated conditions, whereas the parietal region only responded to delay in the Generated condition. This pattern of results is consistent with the hypothesis that the parietal region is responsive to changes in problem representation and the prefrontal region to retrieval operations. An information-processing model embodying these assumptions was fit to the blood oxygen level-dependent responses in these regions.

CaImAn an open source tool for scalable calcium imaging data analysis.

Advances in fluorescence microscopy enable monitoring larger brain areas in-vivo with finer time resolution. The resulting data rates require reproducible analysis pipelines that are reliable, fully automated, and scalable to datasets generated over the course of months. We present CaImAn, an open-source library for calcium imaging data analysis. CaImAn provides automatic and scalable methods to address problems common to pre-processing, including motion correction, neural activity identification, and registration across different sessions of data collection. It does this while requiring minimal user intervention, with good scalability on computers ranging from laptops to high-performance computing clusters. CaImAn is suitable for two-photon and one-photon imaging, and also enables real-time analysis on streaming data. To benchmark the performance of CaImAn we collected and combined a corpus of manual annotations from multiple labelers on nine mouse two-photon datasets. We demonstrate that CaImAn achieves near-human performance in detecting locations of active neurons.

Sibling relationships of children with Down syndrome: perspectives of mothers, fathers, and siblings.

Fifty-four siblings of children with Down syndrome and their parents and an individually matched group of comparison children and parents provided data about the quality of sibling relationships. There were no group differences in parental reports, but siblings of children with Down syndrome reported less unkindness and, if in a same-sex dyad, more empathy than did comparison children. There were differences between same-sex and opposite-sex dyads regarding avoidance and frequency of sibling positive and negative interactions. There were no group or sex differences in children's reports of their interactions with parents or contribution to household chores. Siblings of a child with Down syndrome participated in more caregiving activities. Caregiving was associated with empathy and involvement on the relationship questionnaire.