Filter
Associated Lab
- Ahrens Lab (2) Apply Ahrens Lab filter
- Aso Lab (1) Apply Aso Lab filter
- Betzig Lab (7) Apply Betzig Lab filter
- Bock Lab (1) Apply Bock Lab filter
- Branson Lab (1) Apply Branson Lab filter
- Clapham Lab (1) Apply Clapham Lab filter
- Dudman Lab (1) Apply Dudman Lab filter
- Fetter Lab (3) Apply Fetter Lab filter
- Harris Lab (57) Apply Harris Lab filter
- Hess Lab (4) Apply Hess Lab filter
- Jayaraman Lab (3) Apply Jayaraman Lab filter
- Ji Lab (1) Apply Ji Lab filter
- Keller Lab (1) Apply Keller Lab filter
- Lavis Lab (3) Apply Lavis Lab filter
- Lee (Albert) Lab (7) Apply Lee (Albert) Lab filter
- Leonardo Lab (1) Apply Leonardo Lab filter
- Lippincott-Schwartz Lab (1) Apply Lippincott-Schwartz Lab filter
- Looger Lab (7) Apply Looger Lab filter
- Magee Lab (2) Apply Magee Lab filter
- Pachitariu Lab (3) Apply Pachitariu Lab filter
- Rubin Lab (3) Apply Rubin Lab filter
- Saalfeld Lab (3) Apply Saalfeld Lab filter
- Scheffer Lab (1) Apply Scheffer Lab filter
- Schreiter Lab (4) Apply Schreiter Lab filter
- Singer Lab (2) Apply Singer Lab filter
- Spruston Lab (4) Apply Spruston Lab filter
- Svoboda Lab (6) Apply Svoboda Lab filter
- Tjian Lab (1) Apply Tjian Lab filter
- Zlatic Lab (1) Apply Zlatic Lab filter
Associated Project Team
- Fly Functional Connectome (1) Apply Fly Functional Connectome filter
- Fly Olympiad (1) Apply Fly Olympiad filter
- FlyEM (1) Apply FlyEM filter
- FlyLight (1) Apply FlyLight filter
- GENIE (5) Apply GENIE filter
- MouseLight (1) Apply MouseLight filter
- Tool Translation Team (T3) (1) Apply Tool Translation Team (T3) filter
- Transcription Imaging (3) Apply Transcription Imaging filter
Publication Date
- 2024 (1) Apply 2024 filter
- 2023 (7) Apply 2023 filter
- 2022 (1) Apply 2022 filter
- 2021 (2) Apply 2021 filter
- 2020 (1) Apply 2020 filter
- 2019 (4) Apply 2019 filter
- 2018 (5) Apply 2018 filter
- 2017 (5) Apply 2017 filter
- 2016 (5) Apply 2016 filter
- 2015 (7) Apply 2015 filter
- 2014 (2) Apply 2014 filter
- 2013 (3) Apply 2013 filter
- 2012 (3) Apply 2012 filter
- 2010 (1) Apply 2010 filter
- 2009 (1) Apply 2009 filter
- 2008 (1) Apply 2008 filter
- 1996 (1) Apply 1996 filter
- 1994 (3) Apply 1994 filter
- 1993 (1) Apply 1993 filter
- 1992 (1) Apply 1992 filter
- 1991 (2) Apply 1991 filter
Type of Publication
57 Publications
Showing 11-20 of 57 resultsMeasuring the dynamics of neural processing across time scales requires following the spiking of thousands of individual neurons over milliseconds and months. To address this need, we introduce the Neuropixels 2.0 probe together with newly designed analysis algorithms. The probe has more than 5000 sites and is miniaturized to facilitate chronic implants in small mammals and recording during unrestrained behavior. High-quality recordings over long time scales were reliably obtained in mice and rats in six laboratories. Improved site density and arrangement combined with newly created data processing methods enable automatic post hoc correction for brain movements, allowing recording from the same neurons for more than 2 months. These probes and algorithms enable stable recordings from thousands of sites during free behavior, even in small animals such as mice.
We review recent progress in neural probes for brain recording, with a focus on the Neuropixels platform. Historically the number of neurons’ recorded simultaneously, follows a Moore’s law like behavior, with numbers doubling every 6.7 years. Using traditional techniques of probe fabrication, continuing to scale up electrode densities is very challenging. We describe a custom CMOS process technology that enables electrode counts well beyond 1000 electrodes; with the aim to characterize large neural populations with single neuron spatial precision and millisecond timing resolution. This required integrating analog and digital circuitry with the electrode array, making it a standalone integrated electrophysiology recording system. Input referred noise and power per channel is 7.5µV and <50µW respectively to ensure tissue heating <1°C. This approach enables doubling the number of measured neurons every 12 months.
Although CMOS fabrication has enabled a quick evolution in the design of high-density neural probes and neural-recording chips, the scaling and miniaturization of the complete data-acquisition systems has happened at a slower pace. This is mainly due to the complexity and the many requirements that change depending on the specific experimental settings. In essence, the fundamental challenge of a neural-recording system is getting the signals describing the largest possible set of neurons out of the brain and down to data storage for analysis. This requires a complete system optimization that considers the physical, electrical, thermal and signal-processing requirements, while accounting for available technology, manufacturing constraints and budget. Here we present a scalable and open-standards-based open-source data-acquisition system capable of recording from over 10,000 channels of raw neural data simultaneously. The components and their interfaces have been optimized to ensure robustness and minimum invasiveness in small-rodent electrophysiology.
The classic approach to measure the spiking response of neurons involves the use of metal electrodes to record extracellular potentials. Starting over 60 years ago with a single recording site, this technology now extends to ever larger numbers and densities of sites. We argue, based on the mechanical and electrical properties of existing materials, estimates of signal-to-noise ratios, assumptions regarding extracellular space in the brain, and estimates of heat generation by the electronic interface, that it should be possible to fabricate rigid electrodes to concurrently record from essentially every neuron in the cortical mantle. This will involve fabrication with existing yet nontraditional materials and procedures. We further emphasize the need to advance materials for improved flexible electrodes as an essential advance to record from neurons in brainstem and spinal cord in moving animals.
Electrophysiology is the most used approach for the collection of functional data in basic and translational neuroscience, but it is typically limited to either intracellular or extracellular recordings. The integration of multiple physiological modalities for the routine acquisition of multimodal data with microelectrodes could be useful for biomedical applications, yet this has been challenging owing to incompatibilities of fabrication methods. Here, we present a suite of glass pipettes with integrated microelectrodes for the simultaneous acquisition of multimodal intracellular and extracellular information in vivo, electrochemistry assessments, and optogenetic perturbations of neural activity. We used the integrated devices to acquire multimodal signals from the CA1 region of the hippocampus in mice and rats, and show that these data can serve as ground-truth validation for the performance of spike-sorting algorithms. The microdevices are applicable for basic and translational neurobiology, and for the development of next-generation brain-machine interfaces.
Optical and electron microscopy have made tremendous inroads toward understanding the complexity of the brain. However, optical microscopy offers insufficient resolution to reveal subcellular details, and electron microscopy lacks the throughput and molecular contrast to visualize specific molecular constituents over millimeter-scale or larger dimensions. We combined expansion microscopy and lattice light-sheet microscopy to image the nanoscale spatial relationships between proteins across the thickness of the mouse cortex or the entire Drosophila brain. These included synaptic proteins at dendritic spines, myelination along axons, and presynaptic densities at dopaminergic neurons in every fly brain region. The technology should enable statistically rich, large-scale studies of neural development, sexual dimorphism, degree of stereotypy, and structural correlations to behavior or neural activity, all with molecular contrast.
PURPOSE: To develop switchable and tunable labels with high contrast ratio for MRI using magnetocaloric materials that have sharp first-order magnetic phase transitions at physiological temperatures and typical MRI magnetic field strengths. METHODS: A prototypical magnetocaloric material iron-rhodium (FeRh) was prepared by melt mixing, high-temperature annealing, and ice-water quenching. Temperature- and magnetic field-dependent magnetization measurements of wire-cut FeRh samples were performed on a vibrating sample magnetometer. Temperature-dependent MRI of FeRh samples was performed on a 4.7T MRI. RESULTS: Temperature-dependent MRI clearly demonstrated image contrast changes due to the sharp magnetic state transition of the FeRh samples in the MRI magnetic field (4.7T) and at a physiologically relevant temperature (~37°C). CONCLUSION: A magnetocaloric material, FeRh, was demonstrated to act as a high contrast ratio switchable MRI contrast agent due to its sharp first-order magnetic phase transition in the DC magnetic field of MRI and at physiologically relevant temperatures. A wide range of magnetocaloric materials are available that can be tuned by materials science techniques to optimize their response under MRI-appropriate conditions and be controllably switched in situ with temperature, magnetic field, or a combination of both.
The fruit fly Drosophila melanogaster is an important model organism for neuroscience with a wide array of genetic tools that enable the mapping of individuals neurons and neural subtypes. Brain templates are essential for comparative biological studies because they enable analyzing many individuals in a common reference space. Several central brain templates exist for Drosophila, but every one is either biased, uses sub-optimal tissue preparation, is imaged at low resolution, or does not account for artifacts. No publicly available Drosophila ventral nerve cord template currently exists. In this work, we created high-resolution templates of the Drosophila brain and ventral nerve cord using the best-available technologies for imaging, artifact correction, stitching, and template construction using groupwise registration. We evaluated our central brain template against the four most competitive, publicly available brain templates and demonstrate that ours enables more accurate registration with fewer local deformations in shorter time.
Drosophila melanogaster has a rich repertoire of innate and learned behaviors. Its 100,000-neuron brain is a large but tractable target for comprehensive neural circuit mapping. Only electron microscopy (EM) enables complete, unbiased mapping of synaptic connectivity; however, the fly brain is too large for conventional EM. We developed a custom high-throughput EM platform and imaged the entire brain of an adult female fly at synaptic resolution. To validate the dataset, we traced brain-spanning circuitry involving the mushroom body (MB), which has been extensively studied for its role in learning. All inputs to Kenyon cells (KCs), the intrinsic neurons of the MB, were mapped, revealing a previously unknown cell type, postsynaptic partners of KC dendrites, and unexpected clustering of olfactory projection neurons. These reconstructions show that this freely available EM volume supports mapping of brain-spanning circuits, which will significantly accelerate Drosophila neuroscience..
Success in the projects aimed at providing an advanced understanding of the brain is directly predicated on making critical advances in nanotechnology. This Perspective addresses the unique interface of neuroscience and nanomaterials by considering the foundational problem of sensing neuron membrane voltage and offers a potential solution that may be facilitated by a prototypical nanomaterial. Despite substantial improvements, the visualization of instantaneous voltage changes within individual neurons, whether in cell culture or in vivo, at both the single-cell and network level at high speed remains complex and problematic. The unique properties of semiconductor quantum dots (QDs) have made them powerful fluorophores for bioimaging. What is not widely appreciated, however, is that QD photoluminescence is exquisitely sensitive to proximal electric fields. This property should be suitable for sensing voltage changes that occur in the active neuronal membrane. Here, we examine the potential role of QDs in addressing the important challenge of real-time optical voltage imaging.