scholarly journals Polymer skulls with integrated transparent electrode arrays for cortex-wide opto-electrophysiological recordings

2021 ◽  
Author(s):  
Preston D Donaldson ◽  
Zahra S Navabi ◽  
Russell E Carter ◽  
Skylar M. L. Fausner ◽  
Leila Ghanbari ◽  
...  
Keyword(s):  
Optical Imaging ◽  
Large Scale ◽  
High Spatial Resolution ◽  
Transparent Electrode ◽  
Brain Regions ◽  
Electrode Arrays ◽  
Sensory Stimuli ◽  
Cortical Dynamics ◽  
Multimodal Sensing ◽  
Electrophysiological Recordings

Electrophysiology and optical imaging provide complementary neural sensing capabilities; electrophysiological recordings have the highest temporal resolution, while optical imaging allows recording the activities of genetically defined populations at high spatial resolution. Combining these complementary, yet orthogonal modalities to perform simultaneous large-scale, multimodal sensing of neural activity across multiple brain regions would be very powerful. Here we show that transparent, inkjet-printed electrocorticography (ECoG) electrode arrays can be seamlessly integrated with morphologically conformant transparent polymer skulls for multimodal recordings across the cortex. These eSee-Shells, were implanted on transgenic mice expressing the Ca2+ indicator GCaMP6f in cortical excitatory cells and provided a robust opto-electrophysiological interface for over 100 days. eSee-Shells enable simultaneous mesoscale Ca2+ imaging and ECoG acquisition under anesthesia as well as in awake animals presented with sensory stimuli. eSee-Shells further show sufficient clarity and transparency to observe single-cell Ca2+ signals directly below the electrodes and interconnects. Simultaneous multimodal measurement of cortical dynamics reveals changes in both ECoG and Ca2+ signals that depend on the behavioral state.

scholarly journals High-density, long-lasting, and multi-region electrophysiological recordings using polymer electrode arrays

2018 ◽  
Author(s):  
Jason E. Chung ◽  
Hannah R. Joo ◽  
Jiang Lan Fan ◽  
Daniel F. Liu ◽  
Alex H. Barnett ◽  
...  
Keyword(s):  
Large Scale ◽  
Brain Activity ◽  
Brain Regions ◽  
Electrode Arrays ◽  
Long Term Stability ◽  
Electrophysiological Recordings ◽  
Spatial Coverage ◽  
Recording Channels ◽  
Distributed Circuits ◽  
Polymer Electrodes

AbstractThe brain is a massive neuronal network, organized into anatomically distributed sub-circuits, with functionally relevant activity occurring at timescales ranging from milliseconds to months. Current methods to monitor neural activity, however, lack the necessary conjunction of anatomical spatial coverage, temporal resolution, and long-term stability to measure this distributed activity. Here we introduce a large-scale, multi-site recording platform that integrates polymer electrodes with a modular stacking headstage design supporting up to 1024 recording channels in freely behaving rats. This system can support months-long recordings from hundreds of well-isolated units across multiple brain regions. Moreover, these recordings are stable enough to track 25% of single units for over a week. This platform enables large-scale electrophysiological interrogation of the fast dynamics and long-timescale evolution of anatomically distributed circuits, and thereby provides a new tool for understanding brain activity.

scholarly journals Methods for recording and imaging spreading depolarizations and seizure-like events in mouse hippocampal slices

2021 ◽  
Author(s):  
Yi-Ling Lu ◽  
Helen E Scharfman
Keyword(s):  
Dentate Gyrus ◽  
Optical Imaging ◽  
Hippocampal Slices ◽  
Brain Regions ◽  
Electrophysiological Recordings ◽  
Artificial Cerebrospinal Fluid ◽  
Vitro Model ◽  
Acute Hippocampal Slices ◽  
Molecular Layers

Spreading depolarization (SD) is a sudden and synchronized depolarization of principal cells followed by depression of activity, which slowly propagates across brain regions like cortex or hippocampus. SD is considered to be mechanistically relevant to migraine, epilepsy, and traumatic brain injury. Interestingly, research into SD typically uses SD triggered immediately after a focal stimulus. Here we optimize an in vitro experimental model allowing us to record SD without focal stimulation. This method uses electrophysiological recordings and intrinsic optical imaging in slices. The method is also relatively easy and inexpensive. Acute hippocampal slices from mice or rats were prepared and used for extracellular and whole-cell recordings. Recordings were made in a submerged-style chamber with flow of artificial cerebrospinal fluid (aCSF) above and below the slices. Flow was fast (> 5ml/min), and temperature was 32°C. As soon as slices were placed in the chamber, aCSF containing 0 mM Mg2+ and 5 mM K+ (0 Mg2+/5 K+ aCSF) was used. Two major types of activity were observed: SD and seizure-like events (SLEs). Both occurred after many minutes of recording. Although both mouse and rat slices showed SLEs, only mouse slices developed SD and did so in the first hour of 0 Mg2+/5 K+ aCSF exposure. Intrinsic optical imaging showed that most SDs initiated in CA3 and could propagate into CA1 and dentate gyrus. In dentate gyrus, SD propagated in two separate waves: (1) into the hilus and (2) into granule cell and molecular layers simultaneously. This in vitro model can be used to better understand the mechanisms and relationship between SD and SLEs. It could also be useful in preclinical drug screening.

scholarly journals Higher-order thalamic circuits channel parallel streams of visual information in mice

2018 ◽  
Author(s):  
Corbett Bennett ◽  
Samuel D. Gale ◽  
Marina E. Garrett ◽  
Melissa L. Newton ◽  
Edward M. Callaway ◽  
...  
Keyword(s):  
Functional Properties ◽  
Visual Information ◽  
Large Scale ◽  
Dorsal Stream ◽  
Visual Space ◽  
Brain Regions ◽  
Higher Order ◽  
Thalamic Nuclei ◽  
Cortical Function ◽  
Electrophysiological Recordings

SummaryHigher-order thalamic nuclei, such as the visual pulvinar, play essential roles in cortical function by connecting functionally-related cortical and subcortical brain regions. Yet a coherent framework describing pulvinar function remains elusive due to its anatomical complexity and involvement in diverse cognitive processes. Here we combined large-scale anatomical circuit mapping with high-density electrophysiological recordings to dissect a homolog of pulvinar in mice, the lateral posterior nucleus (LP). We define three broad LP subregions based on correspondence between input/output connectivity and functional properties. These subregions form corticothalamic loops biased towards ventral or dorsal stream cortical areas and contain separate representations of visual space. To reveal which input sources drive LP activity, we silenced visual cortex or superior colliculus and found they drive visual tuning properties in separate LP subregions. Thus, by specifying the driving input sources, functional properties, and downstream targets of LP circuits, our data provide a roadmap for understanding the mechanisms of higher-order thalamic function in vision.

scholarly journals High-Resolution Three-Dimensional Extracellular Recording of Neuronal Activity With Microfabricated Electrode Arrays

2009 ◽  
Vol 101 (3) ◽  
pp. 1671-1678 ◽  
Author(s):  
Jiangang Du ◽  
Ingmar H. Riedel-Kruse ◽  
Janna C. Nawroth ◽  
Michael L. Roukes ◽  
Gilles Laurent ◽  
...  
Keyword(s):  
Neuronal Activity ◽  
Large Scale ◽  
Microelectrode Array ◽  
3D Structure ◽  
Three Dimensional ◽  
Electrode Array ◽  
Extracellular Recording ◽  
Brain Regions ◽  
Electrode Arrays ◽  
The Brain

Microelectrode array recordings of neuronal activity present significant opportunities for studying the brain with single-cell and spike-time precision. However, challenges in device manufacturing constrain dense multisite recordings to two spatial dimensions, whereas access to the three-dimensional (3D) structure of many brain regions appears to remain a challenge. To overcome this limitation, we present two novel recording modalities of silicon-based devices aimed at establishing 3D functionality. First, we fabricated a dual-side electrode array by patterning recording sites on both the front and back of an implantable microstructure. We found that the majority of single-unit spikes could not be simultaneously detected from both sides, suggesting that in addition to providing higher spatial resolution measurements than that of single-side devices, dual-side arrays also lead to increased recording yield. Second, we obtained recordings along three principal directions with a multilayer array and demonstrated 3D spike source localization within the enclosed measurement space. The large-scale integration of such dual-side and multilayer arrays is expected to provide massively parallel recording capabilities in the brain.

scholarly journals A fully adapted headstage for electrophysiological experiments with custom and scalable electrode arrays to record widely distributed brain regions

2021 ◽  
Author(s):  
Flávio Afonso Gonçalves Mourão ◽  
Leonardo de Oliveira Guarnieri ◽  
Paulo Aparecido Amaral Júnior ◽  
Vinícius Rezende Carvalho ◽  
Eduardo Mazoni Andrade Marçal Mendes ◽  
...  
Keyword(s):  
State Of The Art ◽  
Brain Regions ◽  
Electrode Arrays ◽  
Tungsten Electrode ◽  
Neural Ensembles ◽  
Electrophysiological Recordings ◽  
Freely Moving ◽  
Cranial Implants ◽  
The Brain

ABSTRACTElectrophysiological recordings lead amongst the techniques that aim to investigate the dynamics of neural activity sampled from large neural ensembles. However, the financial costs associated with the state-of-the-art technology used to manufacture probes and multi-channel recording systems make these experiments virtually inaccessible to small laboratories, especially if located in developing countries. Here, we describe a new method for implanting several tungsten electrode arrays, widely distributed over the brain. Moreover, we designed a headstage system, using the Intan® RHD2000 chipset, associated with a connector (replacing the expensive commercial Omnetics connector), that allows the usage of disposable and cheap cranial implants. Our results showed high-quality multichannel recording in freely moving animals (detecting local field, evoked responses and unit activities) and robust mechanical connections ensuring long-term continuous recordings. Our project represents an open source and inexpensive alternative to develop customized extracellular records from multiple brain regions.

scholarly journals Kilosort: realtime spike-sorting for extracellular electrophysiology with hundreds of channels

2016 ◽  
Author(s):  
Marius Pachitariu ◽  
Nicholas Steinmetz ◽  
Shabnam Kadir ◽  
Matteo Carandini ◽  
Harris Kenneth D.
Keyword(s):  
Large Scale ◽  
Superior Performance ◽  
Spike Sorting ◽  
Electrode Arrays ◽  
Manual Curation ◽  
Electrophysiological Recordings ◽  
Dimensional Approximation ◽  
Low Dimensional ◽  
Channel Electrode

AbstractAdvances in silicon probe technology mean that in vivo electrophysiological recordings from hundreds of channels will soon become commonplace. To interpret these recordings we need fast, scalable and accurate methods for spike sorting, whose output requires minimal time for manual curation. Here we introduce Kilosort, a spike sorting framework that meets these criteria, and show that it allows rapid and accurate sorting of large-scale in vivo data. Kilosort models the recorded voltage as a sum of template waveforms triggered on the spike times, allowing overlapping spikes to be identified and resolved. Rapid processing is achieved thanks to a novel low-dimensional approximation for the spatiotemporal distribution of each template, and to batch-based optimization on GPUs. A novel post-clustering merging step based on the continuity of the templates substantially reduces the requirement for subsequent manual curation operations. We compare Kilosort to an established algorithm on data obtained from 384-channel electrodes, and show superior performance, at much reduced processing times. Data from 384-channel electrode arrays can be processed in approximately realtime. Kilosort is an important step towards fully automated spike sorting of multichannel electrode recordings, and is freely available (github.com/cortex-lab/Kilosort).

scholarly journals A Functional MRI Paradigm for Efficient Mapping of Memory Encoding Across Sensory Conditions

2021 ◽  
Vol 14 ◽  
Author(s):  
Meta M. Boenniger ◽  
Kersten Diers ◽  
Sibylle C. Herholz ◽  
Mohammad Shahid ◽  
Tony Stöcker ◽  
...  
Keyword(s):  
Large Scale ◽  
Mixed Model ◽  
Recognition Task ◽  
Time Frame ◽  
Brain Regions ◽  
Memory Encoding ◽  
Mixed Design ◽  
Sensory Stimuli ◽  
Face Area ◽  
Visual Cortices

We introduce a new and time-efficient memory-encoding paradigm for functional magnetic resonance imaging (fMRI). This paradigm is optimized for mapping multiple contrasts using a mixed design, using auditory (environmental/vocal) and visual (scene/face) stimuli. We demonstrate that the paradigm evokes robust neuronal activity in typical sensory and memory networks. We were able to detect auditory and visual sensory-specific encoding activities in auditory and visual cortices. Also, we detected stimulus-selective activation in environmental-, voice-, scene-, and face-selective brain regions (parahippocampal place and fusiform face area). A subsequent recognition task allowed the detection of sensory-specific encoding success activity (ESA) in both auditory and visual cortices, as well as sensory-unspecific positive ESA in the hippocampus. Further, sensory-unspecific negative ESA was observed in the precuneus. Among others, the parallel mixed design enabled sustained and transient activity comparison in contrast to rest blocks. Sustained and transient activations showed great overlap in most sensory brain regions, whereas several regions, typically associated with the default-mode network, showed transient rather than sustained deactivation. We also show that the use of a parallel mixed model had relatively little influence on positive or negative ESA. Together, these results demonstrate a feasible, versatile, and brief memory-encoding task, which includes multiple sensory stimuli to guarantee a comprehensive measurement. This task is especially suitable for large-scale clinical or population studies, which aim to test task-evoked sensory-specific and sensory-unspecific memory-encoding performance as well as broad sensory activity across the life span within a very limited time frame.

scholarly journals Pairwise maximum entropy model explains the role of white matter structure in shaping emergent co-activation states

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Arian Ashourvan ◽  
Preya Shah ◽  
Adam Pines ◽  
Shi Gu ◽  
Christopher W. Lynn ◽  
...  
Keyword(s):  
White Matter ◽  
Maximum Entropy ◽  
Large Scale ◽  
Structural Connectivity ◽  
Quantitative Relationship ◽  
Brain Regions ◽  
Maximum Entropy Model ◽  
Entropy Model ◽  
Activation Patterns ◽  
Co Activation

AbstractA major challenge in neuroscience is determining a quantitative relationship between the brain’s white matter structural connectivity and emergent activity. We seek to uncover the intrinsic relationship among brain regions fundamental to their functional activity by constructing a pairwise maximum entropy model (MEM) of the inter-ictal activation patterns of five patients with medically refractory epilepsy over an average of ~14 hours of band-passed intracranial EEG (iEEG) recordings per patient. We find that the pairwise MEM accurately predicts iEEG electrodes’ activation patterns’ probability and their pairwise correlations. We demonstrate that the estimated pairwise MEM’s interaction weights predict structural connectivity and its strength over several frequencies significantly beyond what is expected based solely on sampled regions’ distance in most patients. Together, the pairwise MEM offers a framework for explaining iEEG functional connectivity and provides insight into how the brain’s structural connectome gives rise to large-scale activation patterns by promoting co-activation between connected structures.

scholarly journals Classification of Very-High-Spatial-Resolution Aerial Images Based on Multiscale Features with Limited Semantic Information

2021 ◽  
Vol 13 (3) ◽  
pp. 364
Author(s):  
Han Gao ◽  
Jinhui Guo ◽  
Peng Guo ◽  
Xiuwan Chen
Keyword(s):  
Deep Learning ◽  
Land Cover ◽  
Spatial Resolution ◽  
Large Scale ◽  
High Spatial Resolution ◽  
Training Data ◽  
Aerial Images ◽  
Rural Landscapes ◽  
Feature Representations ◽  
Object Based

Recently, deep learning has become the most innovative trend for a variety of high-spatial-resolution remote sensing imaging applications. However, large-scale land cover classification via traditional convolutional neural networks (CNNs) with sliding windows is computationally expensive and produces coarse results. Additionally, although such supervised learning approaches have performed well, collecting and annotating datasets for every task are extremely laborious, especially for those fully supervised cases where the pixel-level ground-truth labels are dense. In this work, we propose a new object-oriented deep learning framework that leverages residual networks with different depths to learn adjacent feature representations by embedding a multibranch architecture in the deep learning pipeline. The idea is to exploit limited training data at different neighboring scales to make a tradeoff between weak semantics and strong feature representations for operational land cover mapping tasks. We draw from established geographic object-based image analysis (GEOBIA) as an auxiliary module to reduce the computational burden of spatial reasoning and optimize the classification boundaries. We evaluated the proposed approach on two subdecimeter-resolution datasets involving both urban and rural landscapes. It presented better classification accuracy (88.9%) compared to traditional object-based deep learning methods and achieves an excellent inference time (11.3 s/ha).

scholarly journals On the structural connectivity of large-scale models of brain networks at cellular level

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Giuseppe Giacopelli ◽  
Domenico Tegolo ◽  
Emiliano Spera ◽  
Michele Migliore
Keyword(s):  
Large Scale ◽  
Brain Networks ◽  
Structural Connectivity ◽  
Network Models ◽  
Brain Regions ◽  
Cellular Level ◽  
Scale Model ◽  
Mathematical Framework ◽  
Underlying Mechanisms ◽  
Experimental Findings

AbstractThe brain’s structural connectivity plays a fundamental role in determining how neuron networks generate, process, and transfer information within and between brain regions. The underlying mechanisms are extremely difficult to study experimentally and, in many cases, large-scale model networks are of great help. However, the implementation of these models relies on experimental findings that are often sparse and limited. Their predicting power ultimately depends on how closely a model’s connectivity represents the real system. Here we argue that the data-driven probabilistic rules, widely used to build neuronal network models, may not be appropriate to represent the dynamics of the corresponding biological system. To solve this problem, we propose to use a new mathematical framework able to use sparse and limited experimental data to quantitatively reproduce the structural connectivity of biological brain networks at cellular level.

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