scholarly journals Differences in Intrinsic Properties and Local Network Connectivity of Identified Layer 5 and Layer 6 Adult Mouse Auditory Corticothalamic Neurons Support a Dual Corticothalamic Projection Hypothesis

2009 ◽  
Vol 19 (12) ◽  
pp. 2810-2826 ◽  
Author(s):  
D. A. Llano ◽  
S. M. Sherman
Keyword(s):  
Adult Mouse ◽  
Network Connectivity ◽  
Local Network ◽  
Intrinsic Properties ◽  
Corticothalamic Projection

Size Does Matter: Generation of Intrinsic Network Rhythms in Thick Mouse Hippocampal Slices

2005 ◽  
Vol 93 (4) ◽  
pp. 2302-2317 ◽  
Author(s):  
Chiping Wu ◽  
Wah Ping Luk ◽  
Jesse Gillis ◽  
Frances Skinner ◽  
Liang Zhang
Keyword(s):  
In Vitro Model ◽  
Adult Mouse ◽  
Hippocampal Slices ◽  
Network Connectivity ◽  
Slice Preparation ◽  
Fundamental Properties ◽  
Mouse Hippocampus ◽  
Hippocampal Network ◽  
Vitro Model

Rodent hippocampal slices of ≤0.5 mm thickness have been widely used as a convenient in vitro model since the 1970s. However, spontaneous population rhythmic activities do not consistently occur in this preparation due to limited network connectivity. To overcome this limitation, we develop a novel slice preparation of 1 mm thickness from adult mouse hippocampus by separating dentate gyrus from CA3/CA1 areas but preserving dentate–CA3-CA1 connectivity. While superfused in vitro at 32 or 37°C, the thick slice exhibits robust spontaneous network rhythms of 1–4 Hz that originate from the CA3 area. Via assessing tissue O2, K+, pH, synaptic, and single-cell activities of superfused thick slices, we verify that these spontaneous rhythms are not a consequence of hypoxia and nonspecific experimental artifacts. We suggest that the thick slice contains a unitary circuitry sufficient to generate intrinsic hippocampal network rhythms and this preparation is suitable for exploring the fundamental properties and plasticity of a functionally defined hippocampal “lamella” in vitro.

scholarly journals Supervised learning is an accurate method for network-based gene classification

2020 ◽  
Vol 36 (11) ◽  
pp. 3457-3465 ◽  
Author(s):  
Renming Liu ◽  
Christopher A Mancuso ◽  
Anna Yannakopoulos ◽  
Kayla A Johnson ◽  
Arjun Krishnan
Keyword(s):  
Supervised Learning ◽  
Network Connectivity ◽  
Accurate Method ◽  
Label Propagation ◽  
Local Network ◽  
Supplementary Information ◽  
Grand Challenge ◽  
Disease Associated Genes ◽  
Propagation Technique ◽  
Full Network

Abstract Background Assigning every human gene to specific functions, diseases and traits is a grand challenge in modern genetics. Key to addressing this challenge are computational methods, such as supervised learning and label propagation, that can leverage molecular interaction networks to predict gene attributes. In spite of being a popular machine-learning technique across fields, supervised learning has been applied only in a few network-based studies for predicting pathway-, phenotype- or disease-associated genes. It is unknown how supervised learning broadly performs across different networks and diverse gene classification tasks, and how it compares to label propagation, the widely benchmarked canonical approach for this problem. Results In this study, we present a comprehensive benchmarking of supervised learning for network-based gene classification, evaluating this approach and a classic label propagation technique on hundreds of diverse prediction tasks and multiple networks using stringent evaluation schemes. We demonstrate that supervised learning on a gene’s full network connectivity outperforms label propagaton and achieves high prediction accuracy by efficiently capturing local network properties, rivaling label propagation’s appeal for naturally using network topology. We further show that supervised learning on the full network is also superior to learning on node embeddings (derived using node2vec), an increasingly popular approach for concisely representing network connectivity. These results show that supervised learning is an accurate approach for prioritizing genes associated with diverse functions, diseases and traits and should be considered a staple of network-based gene classification workflows. Availability and implementation The datasets and the code used to reproduce the results and add new gene classification methods have been made freely available. Contact [email protected] Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Determinants of functional synaptic connectivity among amygdala-projecting prefrontal cortical neurons

2021 ◽  
Author(s):  
Yoav Printz ◽  
Pritish Patil ◽  
Mathias Mahn ◽  
Asaf Benjamin ◽  
Anna Litvin ◽  
...  
Keyword(s):  
Prefrontal Cortex ◽  
Cortical Neurons ◽  
Large Scale ◽  
Basolateral Amygdala ◽  
Local Network ◽  
Synaptic Connectivity ◽  
Synaptic Organization ◽  
Intrinsic Properties ◽  
Subcortical Structures ◽  
Prefrontal Cortical

The medial prefrontal cortex (mPFC) mediates a variety of complex cognitive functions via its vast and diverse connections with cortical and subcortical structures. Understanding the patterns of synaptic connectivity that comprise the mPFC local network is crucial for deciphering how this circuit processes information and relays it to downstream structures. To elucidate the synaptic organization of the mPFC, we developed a high-throughput optogenetic method for mapping large-scale functional synaptic connectivity. We show that mPFC neurons that project to the basolateral amygdala display unique spatial patterns of local-circuit synaptic connectivity within the mPFC, which distinguish them from the general mPFC cell population. Moreover, the intrinsic properties of the postsynaptic mPFC cell and anatomical position of both cells jointly account for ~7.5% of the variation in probability of connection between mPFC neurons, with anatomical distance and laminar position explaining most of this fraction in variation. Our findings demonstrate a functional segregation of mPFC excitatory neuron subnetworks, and reveal the factors determining connectivity in the mPFC.

scholarly journals Real-Time Data Transmission and Visualization as a Powerful Technology to Reduce Non-Productive Time During Drilling Operations: Present Day Capabilities, Limitation, and Future Development

2020 ◽  
Vol 43 (3) ◽  
pp. 135-142
Author(s):  
Yustian Ekky Rahanjani ◽  
Budhi Nugraha
Keyword(s):  
Real Time ◽  
Data Transmission ◽  
Network Connectivity ◽  
Local Network ◽  
Time Data ◽  
Literature Study ◽  
Intelligence System ◽  
Real Time Data ◽  
Drilling Operations ◽  
Productive Time

This paper primarily is focusing on presenting the non-productive time overview and any kind of non-productive time that can be reduced by real-time data technology, real-time data transmission and visualization infrastructure which supports the processes of aggregation, transmission, and visualization; the example of multipurpose implementation and further innovation and improvements that can be made within the real-time data transmission and visualization, such as real-time reservoir footage calculation during geosteering and drill-time calculation to pick the formation tops and casing point; the challenges and limitation while using real-time data, such as VSAT and local network connectivity issue; and future target and improvement of real-time data usage especially to make an artifi cial intelligence system to predict the potential feature, such as formation or drilling problem while drilling. All of those stuff s could be found by literature study and direct professional experience while handling real-time data system. This technology will inspire the user to design their own solution for their operations. Despite the signifi cant advances on real-time data transmission and visualization, there is signifi cant room to fully use itspotential for advanced workfl ows and the usage of real-time data technology which was proven to reduce the Non-Productive Time that could save the operational cost. We believe that the utilization of real-time data transmission and visualization will defi nitely increase the effi ciency of the drilling operations, especially for multiple wells operations.

scholarly journals Longitudinal cortical network reorganization in early relapsing–remitting multiple sclerosis

2019 ◽  
Vol 12 ◽  
pp. 175628641983867 ◽  
Author(s):  
Vinzenz Fleischer ◽  
Nabin Koirala ◽  
Amgad Droby ◽  
René-Maxime Gracien ◽  
Ralf Deichmann ◽  
...  
Keyword(s):  
Disease Activity ◽  
Gray Matter ◽  
Network Dynamics ◽  
Network Connectivity ◽  
Clustering Coefficient ◽  
Local Network ◽  
Cortical Atrophy ◽  
Disease Manifestation ◽  
Local Efficiency ◽  
Relapsing Remitting

Background: Network science provides powerful access to essential organizational principles of the brain. The aim of this study was to investigate longitudinal evolution of gray matter networks in early relapsing–remitting MS (RRMS) compared with healthy controls (HCs) and contrast network dynamics with conventional atrophy measurements. Methods: For our longitudinal study, we investigated structural cortical networks over 1 year derived from 3T MRI in 203 individuals (92 early RRMS patients with mean disease duration of 12.1 ± 14.5 months and 101 HCs). Brain networks were computed based on cortical thickness inter-regional correlations and fed into graph theoretical analysis. Network connectivity measures (modularity, clustering coefficient, local efficiency, and transitivity) were compared between patients and HCs, and between patients with and without disease activity. Moreover, we calculated longitudinal brain volume changes and cortical atrophy patterns. Results: Our analyses revealed strengthening of local network properties shown by increased modularity, clustering coefficient, local efficiency, and transitivity over time. These network dynamics were not detectable in the cortex of HCs over the same period and occurred independently of patients’ disease activity. Most notably, the described network reorganization was evident beyond detectable atrophy as characterized by conventional morphometric methods. Conclusion: In conclusion, our findings provide evidence for gray matter network reorganization subsequent to clinical disease manifestation in patients with early RRMS. An adaptive cortical response with increased local network characteristics favoring network segregation could play a primordial role for maintaining brain function in response to neuroinflammation.

scholarly journals Resting State Functional Connectivity is Decreased Globally Across the C9orf72 Mutation Spectrum

2020 ◽  
Author(s):  
Rachel F. Smallwood Shoukry ◽  
Michael G Clark ◽  
Mary Kay Floeter
Keyword(s):  
Resting State ◽  
Brain Connectivity ◽  
Brain Mri ◽  
Network Connectivity ◽  
Local Network ◽  
Healthy Controls ◽  
Resting State Functional Connectivity ◽  
Behavioral Impairment ◽  
Mutation Carriers ◽  
C9orf72 Mutation

A repeat expansion mutation in the C9orf72 gene causes amyotrophic lateral sclerosis (ALS), frontotemporal dementia (FTD), or symptoms of both, and has been associated with gray and white matter changes in brain MRI scans. We used graph theory to examine the network properties of brain function at rest in a population of mixed-phenotype C9orf72 mutation carriers (C9+). Twenty-five C9+ subjects (presymptomatic, or diagnosed with ALS, behavioral variant FTD (bvFTD), or both ALS and FTD) and twenty-six healthy controls underwent resting state fMRI. When comparing all C9+ subjects with healthy controls, both global and connection-specific decreases in resting state connectivity were observed, with no substantial reorganization of network hubs. However, when analyzing subgroups of the symptomatic C9+ patients, those with bvFTD (with and without comorbid ALS) show remarkable reorganization of hubs compared to patients with ALS alone (without bvFTD), indicating that subcortical regions become more connected in the network relative to other regions. Additionally, network connectivity measures of the right hippocampus and bilateral thalami increased with increasing scores on the Frontal Behavioral Inventory, indicative of worsening behavioral impairment. These results indicate that while C9orf72 mutation carriers across the ALS-FTD spectrum have global decreased resting state brain connectivity, phenotype-specific effects can also be observed at more local network levels.

scholarly journals The transformation of synaptic to system plasticity in motor output from the sacral cord of the adult mouse

2015 ◽  
Vol 114 (3) ◽  
pp. 1987-2004 ◽  
Author(s):  
Mingchen C. Jiang ◽  
Sherif M. Elbasiouny ◽  
William F. Collins ◽  
C. J. Heckman
Keyword(s):  
Synaptic Plasticity ◽  
Adult Mouse ◽  
Motor Output ◽  
Network Activity ◽  
System Level ◽  
Local Network ◽  
System Transformation ◽  
Short Term ◽  
Local Networks

Synaptic plasticity is fundamental in shaping the output of neural networks. The transformation of synaptic plasticity at the cellular level into plasticity at the system level involves multiple factors, including behavior of local networks of interneurons. Here we investigate the synaptic to system transformation for plasticity in motor output in an in vitro preparation of the adult mouse spinal cord. System plasticity was assessed from compound action potentials (APs) in spinal ventral roots, which were generated simultaneously by the axons of many motoneurons (MNs). Synaptic plasticity was assessed from intracellular recordings of MNs. A computer model of the MN pool was used to identify the middle steps in the transformation from synaptic to system behavior. Two input systems that converge on the same MN pool were studied: one sensory and one descending. The two synaptic input systems generated very different motor outputs, with sensory stimulation consistently evoking short-term depression (STD) whereas descending stimulation had bimodal plasticity: STD at low frequencies but short-term facilitation (STF) at high frequencies. Intracellular and pharmacological studies revealed contributions from monosynaptic excitation and stimulus time-locked inhibition but also considerable asynchronous excitation sustained from local network activity. The computer simulations showed that STD in the monosynaptic excitatory input was the primary driver of the system STD in the sensory input whereas network excitation underlies the bimodal plasticity in the descending system. These results provide insight on the roles of plasticity in the monosynaptic and polysynaptic inputs converging on the same MN pool to overall motor plasticity.

scholarly journals Structural redundancy in supracellular actomyosin networks enables robust tissue folding

2019 ◽  
Author(s):  
Hannah G. Yevick ◽  
Pearson W. Miller ◽  
Jörn Dunkel ◽  
Adam C. Martin
Keyword(s):  
Large Scale ◽  
Transportation Networks ◽  
Network Connectivity ◽  
Ventral Side ◽  
Local Network ◽  
Scale Function ◽  
Structural Redundancy ◽  
Cytoskeletal Network ◽  
Network Properties ◽  
Proper Orientation

SummaryTissue morphogenesis is strikingly reproducible. Yet, how tissues are robustly sculpted, even under challenging conditions, is unknown. Here, we combined network analysis, experimental perturbations, and computational modeling to determine how network connectivity between hundreds of contractile cells on the ventral side of the Drosophila embryo ensures robust tissue folding. We identified two network properties that mechanically promote robustness. First, redundant supracellular cytoskeletal network paths ensure global connectivity, even with network degradation. By forming many more connections than are required, morphogenesis is not disrupted by local network damage, analogous to the way redundancy guarantees the large-scale function of vasculature and transportation networks. Second, directional stiffening of edges oriented orthogonal to the folding axis promotes furrow formation at lower contractility levels. Structural redundancy and directional network stiffening ensure robust tissue folding with proper orientation.

scholarly journals Patient-specific network connectivity combined with a next generation neural mass model to test clinical hypothesis of seizure propagation

2021 ◽  
Author(s):  
Moritz Gerster ◽  
Halgurd Taher ◽  
Jaroslav Hlinka ◽  
Maxime Guye ◽  
Fabrice Bartolomei ◽  
...  
Keyword(s):  
Network Connectivity ◽  
Network Activity ◽  
Local Network ◽  
Neural Mass Model ◽  
Patient Specific ◽  
Connectivity Matrix ◽  
Epileptogenic Zone ◽  
Brain Disorders ◽  
Mean Field Equations ◽  
Seizure Propagation

ABSTRACTModulations of the neuronal subthreshold activity, giving rise to rhythms at high firing rate, represent the high signal complexity of the brain dynamic repertoire. Together with neural network oscillations, they are a fundamental mechanism for cognition, perception and consciousness. Consequently, perturbations of network activity play an important role in the pathophysiology of brain disorders. Here we combine structural information from non-invasive brain imaging with mathematical modeling, thus leveraging an in-silico platform for the exploration of causal mechanisms of brain function and clinical hypothesis testing. In particular we use a recently derived set of exact mean-field equations for networks of quadratic integrate-and-fire neurons to provide a comprehensive study of the effect of external drives or perturbations on neuronal networks exhibiting multistability in order to investigate the role played by the neuroanatomical connectivity matrix in shaping the emergent dynamics.We demonstrate, along the example of 20 diffusion-weighted magnetic resonance imaging (MRI) connectomes of healthy subjects, that individual variations in structural connectivity, when linked with mathematical dynamic models, have the capacity to explain changes in spatiotemporal organization of brain dynamics, as observed in network-based brain disorders. Moreover we studied patient-specific brain network models of 15 drug-resistant epilepsy patients with implanted stereotactic electroencephalography (SEEG) electrodes. Each personalized brain model was derived from structural data of MRI and diffusion tensor weighted imaging (DTI), while each patient’s virtual brain was further personalized through the integration of the clinically hypothesized epileptogenic zone (EZ), i.e. the local network where highly synchronous seizures originate. Across patients, it turns out that patient-specific network connectivity is predictive for the subsequent seizure propagation pattern thus opening the possibility of improving diagnosis and surgery outcome.

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