scholarly journals NFTsim: Theory and Simulation of Multiscale Neural Field Dynamics

2017 ◽  
Author(s):  
P. Sanz-Leon ◽  
P. A. Robinson ◽  
S. A. Knock ◽  
P. M. Drysdale ◽  
R. G. Abeysuriya ◽  
...  
Keyword(s):  
Large Scale ◽  
Multiple Scales ◽  
Brain Activity ◽  
Field Potential ◽  
Neural Field ◽  
Neural Systems ◽  
Unified Framework ◽  
Plain Text ◽  
Wide Range ◽  
Key Aspects

AbstractA user ready, portable, documented software package, NFTsim, is presented to facilitate numerical simulations of a wide range of brain systems using continuum neural field modeling. NFTsim enables users to simulate key aspects of brain activity at multiple scales. At the microscopic scale, it incorporates characteristics of local interactions between cells, neurotransmitter effects, synaptodendritic delays and feedbacks. At the mesoscopic scale, it incorporates information about medium to large scale axonal ranges of fibers, which are essential to model dissipative wave transmission and to produce synchronous oscillations and associated cross-correlation patterns as observed in local field potential recordings of active tissue. At the scale of the whole brain, NFTsim allows for the inclusion of long range pathways, such as thalamocortical projections, when generating macroscopic activity fields. The multiscale nature of the neural activity produced by NFTsim has the potential to enable the modeling of resulting quantities measurable via various neuroimaging techniques. In this work, we give a comprehensive description of the design and implementation of the software. Due to its modularity and flexibility, NFTsim enables the systematic study of an unlimited number of neural systems with multiple neural populations under a unified framework and allows for direct comparison with analytic and experimental predictions. The code is written in C++ and bundled with Matlab routines for a rapid quantitative analysis and visualization of the outputs. The output of NFTsim is stored in plain text file enabling users to select from a broad range of tools for offline analysis. This software enables a wide and convenient use of powerful physiologically-based neural field approaches to brain modeling. NFTsim is distributed under the Apache 2.0 license.

scholarly journals Brain activity mapping at multiple scales with silicon microprobes containing 1,024 electrodes

2015 ◽  
Vol 114 (3) ◽  
pp. 2043-2052 ◽  
Author(s):  
Justin L. Shobe ◽  
Leslie D. Claar ◽  
Sepideh Parhami ◽  
Konstantin I. Bakhurin ◽  
Sotiris C. Masmanidis
Keyword(s):  
Multiple Scales ◽  
Functional Organization ◽  
Brain Activity ◽  
Three Dimensional ◽  
Field Potential ◽  
Neural Systems ◽  
Mammalian Brain ◽  
Neural Signals ◽  
Neural Ensembles ◽  
Potential Activity

The coordinated activity of neural ensembles across multiple interconnected regions has been challenging to study in the mammalian brain with cellular resolution using conventional recording tools. For instance, neural systems regulating learned behaviors often encompass multiple distinct structures that span the brain. To address this challenge we developed a three-dimensional (3D) silicon microprobe capable of simultaneously measuring extracellular spike and local field potential activity from 1,024 electrodes. The microprobe geometry can be precisely configured during assembly to target virtually any combination of four spatially distinct neuroanatomical planes. Here we report on the operation of such a device built for high-throughput monitoring of neural signals in the orbitofrontal cortex and several nuclei in the basal ganglia. We perform analysis on systems-level dynamics and correlations during periods of conditioned behavioral responding and rest, demonstrating the technology's ability to reveal functional organization at multiple scales in parallel in the mouse brain.

scholarly journals Highly scalable multichannel mesh electronics for stable chronic brain electrophysiology

2017 ◽  
Vol 114 (47) ◽  
pp. E10046-E10055 ◽  
Author(s):  
Tian-Ming Fu ◽  
Guosong Hong ◽  
Robert D. Viveros ◽  
Tao Zhou ◽  
Charles M. Lieber
Keyword(s):  
Large Scale ◽  
Single Neuron ◽  
Neurological Diseases ◽  
Field Potential ◽  
Brain Regions ◽  
Long Term Stability ◽  
High Density Recording ◽  
Local Circuits ◽  
Key Aspects

Implantable electrical probes have led to advances in neuroscience, brain−machine interfaces, and treatment of neurological diseases, yet they remain limited in several key aspects. Ideally, an electrical probe should be capable of recording from large numbers of neurons across multiple local circuits and, importantly, allow stable tracking of the evolution of these neurons over the entire course of study. Silicon probes based on microfabrication can yield large-scale, high-density recording but face challenges of chronic gliosis and instability due to mechanical and structural mismatch with the brain. Ultraflexible mesh electronics, on the other hand, have demonstrated negligible chronic immune response and stable long-term brain monitoring at single-neuron level, although, to date, it has been limited to 16 channels. Here, we present a scalable scheme for highly multiplexed mesh electronics probes to bridge the gap between scalability and flexibility, where 32 to 128 channels per probe were implemented while the crucial brain-like structure and mechanics were maintained. Combining this mesh design with multisite injection, we demonstrate stable 128-channel local field potential and single-unit recordings from multiple brain regions in awake restrained mice over 4 mo. In addition, the newly integrated mesh is used to validate stable chronic recordings in freely behaving mice. This scalable scheme for mesh electronics together with demonstrated long-term stability represent important progress toward the realization of ideal implantable electrical probes allowing for mapping and tracking single-neuron level circuit changes associated with learning, aging, and neurodegenerative diseases.

scholarly journals Adversarial domain translation networks enable fast and accurate large-scale atlas-level single-cell data integration

2021 ◽  
Author(s):  
Jia Zhao ◽  
Gefei Wang ◽  
Jingsi Ming ◽  
Zhixiang Lin ◽  
Yang Wang ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Large Scale ◽  
Superior Performance ◽  
Brain Atlas ◽  
Unified Framework ◽  
Fine Grained ◽  
Wide Range ◽  
Shared Information ◽  
Single Nucleus

The rapid emergence of large-scale atlas-level single-cell RNA-sequencing (scRNA-seq) datasets from various sources presents remarkable opportunities for broad and deep biological investigations through integrative analyses. However, harmonizing such datasets requires integration approaches to be not only computationally scalable, but also capable of preserving a wide range of fine-grained cell populations. We created Portal, a unified framework of adversarial domain translation to learn harmonized representations of datasets. With innovation in model and algorithm designs, Portal achieves superior performance in preserving biological variation during integration, while having significantly reduced running time and memory compared to existing approaches, achieving integration of millions of cells in minutes with low memory consumption. We demonstrate the efficiency and accuracy of Portal using diverse datasets ranging from mouse brain atlas projects, the Tabula Muris project, and the Tabula Microcebus project. Portal has broad applicability and in addition to integrating multiple scRNA-seq datasets, it can also integrate scRNA-seq with single-nucleus RNA-sequencing (snRNA-seq) data. Finally, we demonstrate the utility of Portal by applying it to the integration of cross-species datasets with limited shared-information between them, and are able to elucidate biological insights into the similarities and divergences in the spermatogenesis process between mouse, macaque, and human.

scholarly journals Brain networks and cognitive impairment in Parkinson′s disease

2020 ◽  
Author(s):  
Rosaria Rucco ◽  
Anna Lardone ◽  
marianna Liparoti ◽  
Emahnuel Troisi Lopez ◽  
Rosa De Micco ◽  
...  
Keyword(s):  
Cognitive Impairment ◽  
Functional Connectivity ◽  
Large Scale ◽  
Brain Activity ◽  
Brain Networks ◽  
Motor Impairment ◽  
Brain Network ◽  
Topological Features ◽  
Delta Band ◽  
Wide Range

Aim The aim of the present study is to investigate the relations between both functional connectivity and brain networks with cognitive decline, in patients with Parkinson′s disease (PD). Introduction PD phenotype is not limited to motor impairment but, rather, a wide range of non-motor disturbances can occur, cognitive impairment being one of the commonest. However, how the large-scale organization of brain activity differs in cognitively impaired patients, as opposed to cognitively preserved ones, remains poorly understood. Methods Starting from source-reconstructed resting-state magnetoencephalography data, we applied the PLM to estimate functional connectivity, globally and between brain areas, in PD patients with and without cognitive impairment (respectively PD-CI and PD-NC), as compared to healthy subjects (HS). Furthermore, using graph analysis, we characterized the alterations in brain network topology and related these, as well as the functional connectivity, to cognitive performance. Results We found reduced global and nodal PLM in several temporal (fusiform gyrus, Heschl′s gyrus and inferior temporal gyrus), parietal (postcentral gyrus), and occipital (lingual gyrus) areas within the left hemisphere, in the gamma band, in PD-CI patients, as compared to PD-NC and HS. With regard to the global topological features, PD-CI patients, as compared to HS and PD-NC patients, showed differences in multi frequencies bands (delta, alpha, gamma) in the Leaf fraction, Tree hierarchy (both higher in PD-CI) and Diameter (lower in PD-CI). Finally, we found statistically significant correlations between the MoCA test and both the Diameter in delta band and the Tree Hierarchy in the alpha band. Conclusion Our work points to specific large-scale rearrangements that occur selectively in cognitively compromised PD patients and correlated to cognitive impairment.

scholarly journals Functional MRI of large scale activity in behaving mice

2020 ◽  
Author(s):  
Madalena S. Fonseca ◽  
Mattia G. Bergomi ◽  
Zachary F. Mainen ◽  
Noam Shemesh
Keyword(s):  
Large Scale ◽  
Brain Activity ◽  
Spatial Scales ◽  
Distributed Processing ◽  
Brain Regions ◽  
Visually Guided ◽  
Dynamic Interactions ◽  
Complex Behaviour ◽  
Wide Range ◽  
Circuit Techniques

ABSTRACTBehaviour involves complex dynamic interactions across many brain regions. Detecting whole-brain activity in mice performing sophisticated behavioural tasks could facilitate insights into distributed processing underlying behaviour, guide local targeting, and help bridge the disparate spatial scales between rodent and human studies. Here, we present a comprehensive approach for recording brain-wide activity with functional magnetic resonance imaging (fMRI) compatible with a wide range of behavioural paradigms and neuroscience questions. We introduce hardware and procedural advances to allow multi-sensory, multi-action behavioural paradigms in the scanner. We identify signal artifacts arising from task-related body movements and propose novel strategies to suppress them. We validate and explore our approach in a 4-odour classical conditioning and a visually-guided operant task, illustrating how it can be used to extract information insofar intangible to rodent behaviour studies. Our work paves the way for future studies combining fMRI and local circuit techniques during complex behaviour to tackle multi-scale behavioural neuroscience questions.

scholarly journals In vivo brain activity imaging of interactively locomoting mice

2017 ◽  
Author(s):  
Shigenori Inagaki ◽  
Masakazu Agetsuma ◽  
Shinya Ohara ◽  
Toshio Iijima ◽  
Tetsuichi Wazawa ◽  
...  
Keyword(s):  
Visual Cortex ◽  
Primary Visual Cortex ◽  
Brain Activity ◽  
Field Potential ◽  
Brain Functions ◽  
Wide Range ◽  
Novel Method ◽  
Freely Moving ◽  
Voltage Indicator

AbstractElectrophysiological field potential dynamics have been widely used to investigate brain functions and related psychiatric disorders. Conversely, however, various technical limitations of conventional recording methods have limited its applicability to freely moving subjects, especially when they are in a group and socially interacting with each other. Here, we propose a new method to overcome these technical limitations by introducing a bioluminescent voltage indicator called LOTUS-V. Using our simple and fiber-free recording method, named “SNIPA,” we succeeded in capturing brain activity in freely-locomotive mice, without the need for complicated instruments. This novel method further allowed us to simultaneously record from multiple independently-locomotive animals that were interacting with one another. Further, we successfully demonstrated that the primary visual cortex was activated during the interaction. This methodology will further facilitate a wide range of studies in neurobiology and psychiatry.

scholarly journals Chromosomal dynamics predicted by an elastic network model explains genome-wide accessibility and long-range couplings

2016 ◽  
Author(s):  
Natalie Sauerwald ◽  
She Zhang ◽  
Carl Kingsford ◽  
Ivet Bahar
Keyword(s):  
Long Range ◽  
Network Model ◽  
Structural Dynamics ◽  
Large Scale ◽  
Multiple Scales ◽  
Structural Basis ◽  
Unified Framework ◽  
Modeling Framework ◽  
Chromatin Dynamics ◽  
Genome Wide

AbstractUnderstanding the three-dimensional (3D) architecture of the chromatin and its relation to gene expression and regulation is fundamental to understanding how the genome functions. Advances in Hi-C technology now permit us to have a glimpse into the 3D genome organization and identify topologically associated domains (TADs), but we still lack an understanding of the structural dynamics of chromosomes. The dynamic couplings between regions separated by large genomic distances (> 50 megabases) have yet to be characterized. We adapted a well-established protein-modeling framework, the Gaussian Network Model (GNM), to the task of modeling chromatin dynamics using Hi-C contact data. We show that the GNM can identify structural dynamics at multiple scales: it can quantify the fluctuations in the positions of gene loci, find large genomic compartments and smaller TADs that undergo en-bloc movements, and identify dynamically coupled distal regions along the chromosomes. We show that the predictions of the GNM correlate well with DNase-seq and ATAC-seq measurements on accessibility, the previously identified A and B compartments of chromatin structure, and pairs of interacting loci identified by ChIA-PET. We describe a method to use the GNM to identify novel cross-correlated distal domains (CCDDs) representing regions of long-range dynamic coupling and show that CCDDs are often associated with increased gene coexpression using a large-scale analysis of 212 expression experiments. Together, these results show that GNM provides a mathematically well-founded unified framework for assessing chromatin dynamics and the structural basis of genome-wide observations.

scholarly journals Generative Adversarial Networks (GAN) for the simulation of central-place foraging trajectories

2021 ◽  
Author(s):  
Amédée Roy ◽  
Sophie Lanco Bertrand ◽  
Ronan Fablet
Keyword(s):  
Large Scale ◽  
Animal Movement ◽  
Multiple Scales ◽  
Electronic Device ◽  
Central Place ◽  
Generative Adversarial Networks ◽  
Central Place Foraging ◽  
Ecological Processes ◽  
Adversarial Networks ◽  
Wide Range

1. Miniature electronic device such as GPS have enabled ecologists to document relatively large amount of animal trajectories. Modeling such trajectories may attempt (1) to explain mechanisms underlying observed behaviors and (2) to elucidate ecological processes at the population scale by simulating multiple trajectories. Existing approaches to animal movement modeling mainly addressed the first objective and they are yet soon limited when used for simulation. Individual-based models based on ad-hoc formulation and empirical parametrization lack of generability, while state-space models and stochastic differential equations models, based on rigorous statistical inference, consist in 1st order Markovian models calibrated at the local scale which can lead to overly simplistic description of trajectories. 2. We introduce a 'state-of-the-art' tool from artificial intelligence - Generative Adversarial Networks (GAN) - for the simulation of animal trajectories. GAN consist in a pair of deep neural networks that aim at capturing the data distribution of some experimental dataset, and that enable the generation of new instances of data that share statistical similarity. In this study, we aim on one hand to identify relevant deep networks architecture for simulating central-place foraging trajectories and on the second hand to evaluate GAN benefits over classical methods such as state-switching Hidden Markov Models (HMM). 3. We demonstrate the outstanding ability of GAN to simulate 'realistic' seabirds foraging trajectories. In particular, we show that deep convolutional networks are more efficient than LSTM networks and that GAN-derived synthetic trajectories reproduce better the Fourier spectral density of observed trajectories than those simulated using HMM. Therefore, unlike HMM, GAN capture the variability of large-scale descriptive statistics such as foraging trips distance, duration and tortuosity. 4. GAN offer a relevant alternative to existing approaches to modeling animal movement since it is calibrated to reproduce multiple scales at the same time, thus freeing ecologists from the assumption of first-order markovianity. GAN also provide an ultra-flexible and robust framework that could further take environmental conditions, social interactions or even bio-energetics model into account and tackle a wide range of key challenges in movement ecology.

scholarly journals Brain networks in human conscious visual perception

2021 ◽  
Author(s):  
Sharif I. Kronemer ◽  
Mark Aksen ◽  
Julia Ding ◽  
Jun Hwan Ryu ◽  
Qilong Xin ◽  
...  
Keyword(s):  
Visual Perception ◽  
Large Scale ◽  
Brain Activity ◽  
Event Related Potentials ◽  
Neural Mechanisms ◽  
Neural Systems ◽  
Functional Magnetic Resonance ◽  
Specific Sequence ◽  
Single Mechanism ◽  
Related Potentials

AbstractConsciousness is not explained by a single mechanism, rather it involves multiple specialized neural systems overlapping in space and time. We hypothesize that synergistic, large-scale subcortical and cortical attention and signal processing networks encode conscious experiences. To identify brain activity in conscious perception without overt report, we classified visual stimuli as perceived or not using eye measurements. Report-independent event-related potentials and functional magnetic resonance imaging (fMRI) signals both occurred at early times after stimuli. Direct recordings revealed a novel thalamic awareness potential linked to conscious visual perception based on report. fMRI showed thalamic and cortical detection, arousal, attentional salience, task-positive, and default mode networks were involved independent of overt report. These findings identify a specific sequence of neural mechanisms in human conscious visual perception.One-Sentence SummaryHuman conscious visual perception engages large-scale subcortical and cortical networks even without overt report.

scholarly journals Chronic, Multi-Site Recordings Supported by Two Low-Cost, Stationary Probe Designs Optimized to Capture Either Single Unit or Local Field Potential Activity in Behaving Rats

2021 ◽  
Vol 12 ◽  
Author(s):  
Miranda J. Francoeur ◽  
Tianzhi Tang ◽  
Leila Fakhraei ◽  
Xuanyu Wu ◽  
Sidharth Hulyalkar ◽  
...  
Keyword(s):  
Local Field ◽  
Single Unit ◽  
Large Scale ◽  
Brain Activity ◽  
Low Cost ◽  
Local Field Potentials ◽  
Field Potential ◽  
Brain Regions ◽  
Field Potentials ◽  
Brain Target

Rodent models of cognitive behavior have greatly contributed to our understanding of human neuropsychiatric disorders. However, to elucidate the neurobiological underpinnings of such disorders or impairments, animal models are more useful when paired with methods for measuring brain function in awake, behaving animals. Standard tools used for systems-neuroscience level investigations are not optimized for large-scale and high-throughput behavioral battery testing due to various factors including cost, time, poor longevity, and selective targeting limited to measuring only a few brain regions at a time. Here we describe two different “user-friendly” methods for building extracellular electrophysiological probes that can be used to measure either single units or local field potentials in rats performing cognitive tasks. Both probe designs leverage several readily available, yet affordable, commercial products to facilitate ease of production and offer maximum flexibility in terms of brain-target locations that can be scalable (32–64 channels) based on experimental needs. Our approach allows neural activity to be recorded simultaneously with behavior and compared between micro (single unit) and more macro (local field potentials) levels of brain activity in order to gain a better understanding of how local brain regions and their connected networks support cognitive functions in rats. We believe our novel probe designs make collecting electrophysiology data easier and will begin to fill the gap in knowledge between basic and clinical research.

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