Organoid intelligence (OI): the new frontier in biocomputing and intelligence-in-a-dish

Frontiers in Science, Год журнала: 2023, Номер 1

Опубликована: Фев. 28, 2023

Recent advances in human stem cell-derived brain organoids promise to replicate critical molecular and cellular aspects of learning memory possibly cognition vitro . Coining the term “organoid intelligence” (OI) encompass these developments, we present a collaborative program implement vision multidisciplinary field OI. This aims establish OI as form genuine biological computing that harnesses using scientific bioengineering an ethically responsible manner. Standardized, 3D, myelinated can now be produced with high cell density enriched levels glial cells gene expression for learning. Integrated microfluidic perfusion systems support scalable durable culturing, spatiotemporal chemical signaling. Novel 3D microelectrode arrays permit high-resolution electrophysiological signaling recording explore capacity recapitulate mechanisms formation and, ultimately, their computational potential. Technologies could enable novel biocomputing models via stimulus-response training organoid-computer interfaces are development. We envisage complex, networked whereby connected real-world sensors output devices, ultimately each other sensory organ (e.g. retinal organoids), trained biofeedback, big-data warehousing, machine methods. In parallel, emphasize embedded ethics approach analyze ethical raised by research iterative, manner involving all relevant stakeholders. The many possible applications this urge strategic development discipline. anticipate OI-based allow faster decision-making, continuous during tasks, greater energy data efficiency. Furthermore, “intelligence-in-a-dish” help elucidate pathophysiology devastating developmental degenerative diseases (such dementia), potentially aiding identification therapeutic approaches address major global unmet needs.

Язык: Английский

---

brainlife.io: a decentralized and open-source cloud platform to support neuroscience research

Nature Methods, Год журнала: 2024, Номер 21(5), С. 809 - 813

Опубликована: Апрель 11, 2024

Neuroscience is advancing standardization and tool development to support rigor transparency. Consequently, data pipeline complexity has increased, hindering FAIR (findable, accessible, interoperable reusable) access. brainlife.io was developed democratize neuroimaging research. The platform provides standardization, management, visualization processing automatically tracks the provenance history of thousands objects. Here, described evaluated for validity, reliability, reproducibility, replicability scientific utility using four modalities 3,200 participants.

Язык: Английский

---

Petascale pipeline for precise alignment of images from serial section electron microscopy

Nature Communications, Год журнала: 2024, Номер 15(1)

Опубликована: Янв. 4, 2024

Abstract The reconstruction of neural circuits from serial section electron microscopy (ssEM) images is being accelerated by automatic image segmentation methods. Segmentation accuracy often limited the preceding step aligning 2D to create a 3D stack. Precise and robust alignment in presence artifacts challenging, especially as datasets are attaining petascale. We present computational pipeline for ssEM with several key elements. Self-supervised convolutional nets trained via metric learning encode align pairs, they used initialize iterative fine-tuning alignment. A procedure called vector voting increases robustness or missing data. For speedup series divided into blocks that distributed workers aligned each other composing transformations decay, which achieves global without resorting time-consuming optimization. apply our whole fly brain dataset, show improved relative prior state art. also demonstrate scales cubic millimeter mouse visual cortex. Our publicly available through two open source Python packages.

Язык: Английский

---

EM-Compressor: Electron Microscopy Image Compression in Connectomics with Variational Autoencoders

Lecture notes in computer science, Год журнала: 2025, Номер unknown, С. 160 - 169

Опубликована: Янв. 1, 2025

Язык: Английский

---

How innovations in methodology offer new prospects for volume electron microscopy

Journal of Microscopy, Год журнала: 2022, Номер 287(3), С. 114 - 137

Опубликована: Июль 10, 2022

Detailed knowledge of biological structure has been key in understanding biology at several levels organisation, from organs to cells and proteins. Volume electron microscopy (volume EM) provides high resolution 3D structural information about tissues on the nanometre scale. However, throughput rate conventional microscopes limited volume size number samples that can be imaged. Recent improvements methodology are currently driving a revolution EM, making possible imaging whole small organisms. In turn, these recent developments image acquisition have created or stressed bottlenecks other parts pipeline, like sample preparation, analysis data management. While progress is stunning due advent automatic segmentation server-based annotation tools, challenges remain. Here we discuss trends emerging methods for increasing implications

Язык: Английский

---

DVID: Distributed Versioned Image-Oriented Dataservice

Frontiers in Neural Circuits, Год журнала: 2019, Номер 13

Опубликована: Фев. 5, 2019

Open-source software development has skyrocketed in part due to community tools like github.com, which allows publication of code as well the ability create branches and push accepted modifications back original repository. As number size EM-based datasets increases, connectomics faces similar issues when we publish snapshot data corresponding a publication. Ideally, there would be mechanism where remote collaborators could modify then flexibly reintegrate results via moderated acceptance changes. The DVID system provides web-based API first steps toward such distributed versioning approach datasets. Through its use central resource for Janelia's FlyEM team, have integrated concepts into reconstruction workflows, allowing support proofreader training segmentation experiments through branched, versioned data. also supports persistence variety storage systems from high-speed local SSDs cloud-based object stores, deployment on laptops large servers. tailoring backend each type leads efficient fast queries. is freely available open-source with an increasing supported options.

Язык: Английский

---

A Survey of Visualization and Analysis in High‐Resolution Connectomics

Computer Graphics Forum, Год журнала: 2022, Номер 41(3), С. 573 - 607

Опубликована: Июнь 1, 2022

Abstract The field of connectomics aims to reconstruct the wiring diagram Neurons and synapses enable new insights into workings brain. Reconstructing analyzing Neuronal connectivity, however, relies on many individual steps, starting from high‐resolution data acquisition automated segmentation, proofreading, interactive exploration, circuit analysis. All these steps have handle large complex datasets rely or benefit integrated visualization methods. In this state‐of‐the‐art report, we describe methods that can be applied throughout pipeline, We first define different pipeline focus how is currently steps. also survey open science initiatives in connectomics, including usable open‐source tools publicly available datasets. Finally, discuss challenges possible future directions exciting research field.

Язык: Английский

---

Mapping causal pathways from genetics to neuropsychiatric disorders using genome‐wide imaging genetics: Current status and future directions

Psychiatry and Clinical Neurosciences, Год журнала: 2019, Номер 73(7), С. 357 - 369

Опубликована: Март 13, 2019

Imaging genetics aims to identify genetic variants associated with the structure and function of human brain. Recently, collaborative consortia have been successful in this goal, identifying replicating common influencing gross brain as measured through magnetic resonance imaging. In review, we contextualize imaging associations one important link understanding causal chain from variant increased risk for neuropsychiatric disorders. We provide examples other fields how disease multiple phenotypes along has revealed a mechanistic risk, implications can be similarly applied. discuss current findings research domain, including that slightly larger effect on than disorders like schizophrenia, indicating somewhat simpler architecture. Also, measurements share basis some, but not all, disorders, invalidating previously held belief they are broad endophenotypes, yet pinpointing regions likely involved pathology specific Finally, suggest order build more detailed effects brain, future directions will require observations cellular synaptic beyond resolution expect integrating at biological levels synapse sulcus reveal pathways impacting

Язык: Английский

---

Spyglass: a framework for reproducible and shareable neuroscience research

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2024, Номер unknown

Опубликована: Янв. 26, 2024

Abstract Scientific progress depends on reliable and reproducible results. Progress can also be accelerated when data are shared re-analyzed to address new questions. Current approaches storing analyzing neural typically involve bespoke formats software that make replication, as well the subsequent reuse of data, difficult if not impossible. To these challenges, we created Spyglass , an open-source framework enables analyses sharing both intermediate final results within across labs. uses Neurodata Without Borders (NWB) standard includes pipelines for several core in neuroscience, including spectral filtering, spike sorting, pose tracking, decoding. It easily extended apply existing newly developed datasets from multiple sources. We demonstrate features context a cross-laboratory replication by applying advanced state space decoding algorithms publicly available data. New users try out Jupyter Hub hosted HHMI 2i2c: https://spyglass.hhmi.2i2c.cloud/ .

Язык: Английский

---

FAIR African brain data: challenges and opportunities

Frontiers in Neuroinformatics, Год журнала: 2025, Номер 19

Опубликована: Март 3, 2025

Introduction The effectiveness of research and innovation often relies on the diversity or heterogeneity datasets that are Findable, Accessible, Interoperable Reusable (FAIR). However, global landscape brain data is yet to achieve desired levels can facilitate generalisable outputs. Brain from low-and middle-income countries Africa still missing in open science ecosystem. This mean decades may not be populations Africa. Methods combined experiential learning with a survey questionnaire. involved deriving insights direct, hands-on experiences collecting African view making it FAIR. was critical process action, reflection, doing collection. A questionnaire then used validate findings provide wider contexts for these findings. Results revealed major challenges FAIR categorised as socio-cultural, economic, technical, ethical legal challenges. It also highlighted opportunities growth include capacity development, development technical infrastructure, funding well policy regulatory changes. showed neuroscience community believes ranked order priority follows: Technical, socio-cultural Conclusion We conclude researchers need work together address way maximise efforts build thriving ecosystem socially acceptable, ethically responsible, technically robust legally compliant.

Язык: Английский

---