scholarly journals A Large-Scale High-Density Weighted Structural Connectome of the Macaque Brain Acquired by Predicting Missing Links

2020 ◽  
Vol 30 (9) ◽  
pp. 4771-4789 ◽  
Author(s):  
Yuhan Chen ◽  
Zi-Ke Zhang ◽  
Yong He ◽  
Changsong Zhou
Keyword(s):  
Large Scale ◽  
High Density ◽  
Spatial Proximity ◽  
Weighted Network ◽  
Frontoparietal Network ◽  
Structural Connectome ◽  
Cortical Regions ◽  
Structural Networks ◽  
Tracing Techniques ◽  
Binary Network

Abstract As a substrate for function, large-scale brain structural networks are crucial for fundamental and systems-level understanding of primate brains. However, it is challenging to acquire a complete primate whole-brain structural connectome using track tracing techniques. Here, we acquired a weighted brain structural network across 91 cortical regions of a whole macaque brain hemisphere with a connectivity density of 59% by predicting missing links from the CoCoMac-based binary network with a low density of 26.3%. The prediction model combines three factors, including spatial proximity, topological similarity, and cytoarchitectural similarity—to predict missing links and assign connection weights. The model was tested on a recently obtained high connectivity density yet partial-coverage experimental weighted network connecting 91 sources to 29 target regions; the model showed a prediction sensitivity of 74.1% in the predicted network. This predicted macaque hemisphere-wide weighted network has module segregation closely matching functional domains. Interestingly, the areas that act as integrators linking the segregated modules are mainly distributed in the frontoparietal network and correspond to the regions with large wiring costs in the predicted weighted network. This predicted weighted network provides a high-density structural dataset for further exploration of relationships between structure, function, and metabolism in the primate brain.

scholarly journals Mobility patterns are associated with experienced income segregation in large US cities

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Esteban Moro ◽  
Dan Calacci ◽  
Xiaowen Dong ◽  
Alex Pentland
Keyword(s):  
Large Scale ◽  
Mobility Model ◽  
Coarse Grained ◽  
Spatial Proximity ◽  
Mobility Patterns ◽  
Data Set ◽  
Residential Patterns ◽  
Income Segregation ◽  
Mobility Data ◽  
Mobility Behavior

AbstractTraditional understanding of urban income segregation is largely based on static coarse-grained residential patterns. However, these do not capture the income segregation experience implied by the rich social interactions that happen in places that may relate to individual choices, opportunities, and mobility behavior. Using a large-scale high-resolution mobility data set of 4.5 million mobile phone users and 1.1 million places in 11 large American cities, we show that income segregation experienced in places and by individuals can differ greatly even within close spatial proximity. To further understand these fine-grained income segregation patterns, we introduce a Schelling extension of a well-known mobility model, and show that experienced income segregation is associated with an individual’s tendency to explore new places (place exploration) as well as places with visitors from different income groups (social exploration). Interestingly, while the latter is more strongly associated with demographic characteristics, the former is more strongly associated with mobility behavioral variables. Our results suggest that mobility behavior plays an important role in experienced income segregation of individuals. To measure this form of income segregation, urban researchers should take into account mobility behavior and not only residential patterns.

Large‐scale high‐density culture of hepatocytes in a liver microsystem with mimicked sinusoid blood flow

2018 ◽  
Vol 12 (12) ◽  
pp. 2266-2276
Author(s):  
Jing Liu ◽  
Chengpan Li ◽  
Shaohui Cheng ◽  
Shengnan Ya ◽  
Dayong Gao ◽  
...  
Keyword(s):  
Blood Flow ◽  
Large Scale ◽  
High Density ◽  
High Density Culture ◽  
Culture Of Hepatocytes

scholarly journals YAP Promotes Cell Proliferation and Stemness Maintenance of Porcine Muscle Stem Cells under High-Density Condition

Cells ◽  
2021 ◽  
Vol 10 (11) ◽  
pp. 3069
Author(s):  
Zheng Liu ◽  
Ling Lin ◽  
Haozhe Zhu ◽  
Zhongyuan Wu ◽  
Xi Ding ◽  
...  
Keyword(s):  
Stem Cells ◽  
Cell Proliferation ◽  
Large Scale ◽  
High Density ◽  
Meat Production ◽  
Density Condition ◽  
High Density Culture ◽  
Differentiation Potential ◽  
Muscle Stem Cells ◽  
Cultured Meat

Muscle stem cells (MuSCs) isolated ex vivo are essential original cells to produce cultured meat. Currently, one of the main obstacles for cultured meat production derives from the limited capacity of large-scale amplification of MuSCs, especially under high-density culture condition. Here, we show that at higher cell densities, proliferation and differentiation capacities of porcine MuSCs are impaired. We investigate the roles of Hippo-YAP signaling, which is important regulators in response to cell contact inhibition. Interestingly, abundant but not functional YAP proteins are accumulated in MuSCs seeded at high density. When treated with lysophosphatidic acid (LPA), the activator of YAP, porcine MuSCs exhibit increased proliferation and elevated differentiation potential compared with control cells. Moreover, constitutively active YAP with deactivated phosphorylation sites, but not intact YAP, promotes cell proliferation and stemness maintenance of MuSCs. Together, we reveal a potential molecular target that enables massive MuSCs expansion for large-scale cultured meat production under high-density condition.

scholarly journals Enhancing Form-Based Code: A parametric approach to urban volumetric morphology in high density cities

2021 ◽  
Author(s):  
◽  
Yingyi Zhang
Keyword(s):  
Hong Kong ◽  
Large Scale ◽  
Positive Impact ◽  
High Density ◽  
Original Form ◽  
Human Society ◽  
Parametric Modelling ◽  
Physical Form ◽  
Urban Regulation ◽  
Modelling System

<p>Parametric tools have been broadly implemented in Architecture, Engineering and Construction (AEC) industry. Recently, an increasing volume of research finds that parametric tools also have the capability to facilitate large-scale planning and urban design. Much of this research, however, focuses on parametric representation or environment simulation. There is insufficient research about using parametric tools to enhance urban regulation. Parametric tools can provide smart design procedures by integrating strategies, solutions and expressions in one system. They may allow alternative approaches to urban regulation that conventional tools do not process.  This research aims to create a parametric modelling system to aid urban regulation. The system offers a visualised coding interface to manipulate parameters and achieve interactive performance feedback at the early stage of urban regulation. Form-Based Code uses the modelling system in this research. It generates a specific morphology by controlling physical form with less focus on land use. With the rise of New Urbanism, Form-Based Code has been used in various American regulation projects. This research extends the application of Form-Based Code, adopting it for urban-peripheral environments outside of the USA. High-density cities where provide the volumetric morphology context is important for this work. Tsim Sha Tsui area of Hong Kong works as an experimental site.  The feasibility of parametric urban regulation is examined by developing a parametric modelling system for Form-Based Code in Hong Kong. Understanding the site’s form characteristics, the transect matrix of Form-Based Code is expanded by incorporating multi-layered zone types and regulating plans. Embedding the zones into parametric modelling software Rhinoceros 3D and Grasshopper 3D, a regenerative prototype works to create real-time scenarios responding to parameters, rules and geometry constraints. The results of parametric urban regulation are evaluated by both Form-Based Code standards and local urban regulation standards to assess its feasibility in context.  This research demonstrates that the parametric modelling system for Form-Based Code has both technological and implemental potential to work as an alternative approach to urban regulation, especially in complex developments. Form complexity is a reflection of sophisticated human-society systems and the sequential evolution of a dynamic morphology. Form-Based Code is enhanced by the parametric modelling system to describe and regulate form complexity in a logical manner. Additionally, although parametric Form-Based Code processing is based on the original Form-Based Code, it is not limited to that. Describing urban regulation with visualised models bridges specialists and the public in community demonstrations and code assembling. The parametric modelling system has a positive impact on resolving challenges, predicting outcomes, and applying urban regulation innovation to the volumetric morphology of high-density cities in Asia.</p>

scholarly journals Multimodal multilayer network centrality relates to executive functioning

2021 ◽  
Author(s):  
Lucas C. Breedt ◽  
Fernando A.N. Santos ◽  
Arjan Hillebrand ◽  
Liesbeth Reneman ◽  
Anne-Fleur van Rootselaar ◽  
...  
Keyword(s):  
Executive Functioning ◽  
Positive Association ◽  
Resting State Fmri ◽  
Brain Network ◽  
Network Organization ◽  
Eigenvector Centrality ◽  
Multilayer Network ◽  
Network Analyses ◽  
Frontoparietal Network ◽  
Structural Networks

Executive functioning is a higher-order cognitive process that is thought to depend on a brain network organization facilitating network integration across specialized subnetworks. The frontoparietal network (FPN), a subnetwork that has diverse connections to other brain modules, seems pivotal to this integration, and a more central role of regions in the FPN has been related to better executive functioning. Brain networks can be constructed using different modalities: diffusion MRI (dMRI) can be used to reconstruct structural networks, while resting-state fMRI (rsfMRI) and magnetoencephalography (MEG) yield functional networks. These networks are often studied in a unimodal way, which cannot capture potential complementary or synergistic modal information. The multilayer framework is a relatively new approach that allows for the integration of different modalities into one 'network of networks'. It has already yielded promising results in the field of neuroscience, having been related to e.g. cognitive dysfunction in Alzheimer's disease. Multilayer analyses thus have the potential to help us better understand the relation between brain network organization and executive functioning. Here, we hypothesized a positive association between centrality of the FPN and executive functioning, and we expected that multimodal multilayer centrality would supersede unilayer centrality in explaining executive functioning. We used dMRI, rsfMRI, MEG, and neuropsychological data obtained from 33 healthy adults (age range 22-70 years) to construct eight modality-specific unilayer networks (dMRI, fMRI, and six MEG frequency bands), as well as a multilayer network comprising all unilayer networks. Interlayer links in the multilayer network were present only between a node's counterpart across layers. We then computed and averaged eigenvector centrality of the nodes within the FPN for every uni- and multilayer network and used multiple regression models to examine the relation between uni- or multilayer centrality and executive functioning. We found that higher multilayer FPN centrality, but not unilayer FPN centrality, was related to better executive functioning. To further validate multilayer FPN centrality as a relevant measure, we assessed its relation with age. Network organization has been shown to change across the life span, becoming increasingly efficient up to middle age and regressing to a more segregated topology at higher age. Indeed, the relation between age and multilayer centrality followed an inverted-U shape. These results show the importance of FPN integration for executive functioning as well as the value of a multilayer framework in network analyses of the brain. Multilayer network analysis may particularly advance our understanding of the interplay between different brain network aspects in clinical populations, where network alterations differ across modalities.

scholarly journals Posterior Cingulate Cortex Network Predicts Alzheimer's Disease Progression

2020 ◽  
Vol 12 ◽  
Author(s):  
Pei-Lin Lee ◽  
Kun-Hsien Chou ◽  
Chih-Ping Chung ◽  
Tzu-Hsien Lai ◽  
Juan Helen Zhou ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Disease Progression ◽  
Large Scale ◽  
Cingulate Cortex ◽  
Posterior Cingulate Cortex ◽  
Cognitive Domains ◽  
Atrophy Rate ◽  
Posterior Cingulate ◽  
Structural Networks

Alzheimer's disease (AD) is a progressive neurodegenerative disorder characterized by the accumulation of toxic misfolded proteins, which are believed to have propagated from disease-specific epicenters through their corresponding large-scale structural networks in the brain. Although previous cross-sectional studies have identified potential AD-associated epicenters and corresponding brain networks, it is unclear whether these networks are associated with disease progression. Hence, this study aims to identify the most vulnerable epicenters and corresponding large-scale structural networks involved in the early stages of AD and to evaluate its associations with multiple cognitive domains using longitudinal study design. Annual neuropsychological and MRI assessments were obtained from 23 patients with AD, 37 patients with amnestic mild cognitive impairment (MCI), and 33 healthy controls (HC) for 3 years. Candidate epicenters were identified as regions with faster decline rate in the gray matter volume (GMV) in patients with MCI who progressed to AD as compared to those regions in patients without progression. These epicenters were then further used as pre-defined regions of interest to map the synchronized degeneration network (SDN) in HCs. Spatial similarity, network preference and clinical association analyses were used to evaluate the specific roles of the identified SDNs. Our results demonstrated that the hippocampus and posterior cingulate cortex (PCC) were the most vulnerable AD-associated epicenters. The corresponding PCC-SDN showed significant spatial association with the patterns of GMV atrophy rate in each patient group and the overlap of these patterns was more evident in the advanced stages of the disease. Furthermore, individuals with a higher GMV atrophy rate of the PCC-SDN also showed faster decline in multiple cognitive domains. In conclusion, our findings suggest the PCC and hippocampus are two vulnerable regions involved early in AD pathophysiology. However, the PCC-SDN, but not hippocampus-SDN, was more closely associated with AD progression. These results may provide insight into the pathophysiology of AD from large-scale network perspective.

scholarly journals Comparison of structural MRI brain measures between 1.5T and 3T: data from the Lothian Birth Cohort 1936

2021 ◽  
Author(s):  
Colin R Buchanan ◽  
Susana Munoz Maniega ◽  
Maria del Carmen Valdes Hernandez ◽  
Lucia Ballerini ◽  
Gayle Barclay ◽  
...  
Keyword(s):  
Cross Validation ◽  
Intraclass Correlation ◽  
Structural Mri ◽  
Community Dwelling ◽  
Lothian Birth Cohort ◽  
Cortical Regions ◽  
Absolute Measurements ◽  
Structural Networks ◽  
And Diffusion ◽  
Fold Cross Validation

Multi-scanner MRI studies are reliant on understanding the apparent differences in imaging measures between different scanners. We provide a comprehensive analysis of T1-weighted and diffusion MRI (dMRI) structural brain measures between a 1.5T GE Signa Horizon HDx and a 3T Siemens Magnetom Prisma using 91 community-dwelling older participants (aged 82 years). Although we found considerable differences in absolute measurements (global tissue volumes were measured as ~6-11% higher and fractional anisotropy was 33% higher at 3T than at 1.5T), between-scanner consistency was good to excellent for global volumetric and dMRI measures (intraclass correlation coefficient [ICC] range: 0.612-0.993) and fair to good for 68 cortical regions (FreeSurfer) and cortical surface measures (mean ICC: 0.504-0.763). Between-scanner consistency was fair for dMRI measures of 12 major white matter tracts (mean ICC: 0.475-0.564), and the general factors of these tracts provided excellent consistency (ICC > 0.769). Whole-brain structural networks provided good to excellent consistency for global metrics (ICC > 0.612). Although consistency was poor for individual network connections (mean ICCs: 0.275-0.280), this was driven by a large difference in network sparsity (0.599 versus 0.334), and consistency was improved when comparing only the connections present in every participant (mean ICCs: 0.533-0.647). Regression-based k-fold cross-validation showed that, particularly for global volumes, between-scanner differences could be largely eliminated (R2 range 0.615-0.991). We conclude that low granularity measures of brain structure can be reliably matched between the scanners tested, but caution is warranted when combining high granularity information from different scanners.

CO2 in large-scale and high-density CHO cell perfusion culture

1996 ◽  
Vol 22 (1-3) ◽  
pp. 65-78 ◽  
Author(s):  
David R. Gray ◽  
Su Chen ◽  
William Howarth ◽  
Duane Inlow ◽  
Brian L. Maiorella
Keyword(s):  
Large Scale ◽  
Perfusion Culture ◽  
High Density ◽  
Cho Cell

Functional Connectivity Relationships Predict Similarities in Task Activation and Pattern Information during Associative Memory Encoding

2014 ◽  
Vol 26 (5) ◽  
pp. 1085-1099 ◽  
Author(s):  
Maureen Ritchey ◽  
Andrew P. Yonelinas ◽  
Charan Ranganath
Keyword(s):  
Functional Connectivity ◽  
Functional Properties ◽  
Associative Memory ◽  
Large Scale ◽  
Memory Task ◽  
Neural Systems ◽  
General Level ◽  
Specific Information ◽  
Memory Encoding ◽  
Cortical Regions

Neural systems may be characterized by measuring functional interactions in the healthy brain, but it is unclear whether components of systems defined in this way share functional properties. For instance, within the medial temporal lobes (MTL), different subregions show different patterns of cortical connectivity. It is unknown, however, whether these intrinsic connections predict similarities in how these regions respond during memory encoding. Here, we defined brain networks using resting state functional connectivity (RSFC) then quantified the functional similarity of regions within each network during an associative memory encoding task. Results showed that anterior MTL regions affiliated with a network of anterior temporal cortical regions, whereas posterior MTL regions affiliated with a network of posterior medial cortical regions. Importantly, these connectivity relationships also predicted similarities among regions during the associative memory task. Both in terms of task-evoked activation and trial-specific information carried in multivoxel patterns, regions within each network were more similar to one another than were regions in different networks. These findings suggest that functional heterogeneity among MTL subregions may be related to their participation in distinct large-scale cortical systems involved in memory. At a more general level, the results suggest that components of neural systems defined on the basis of RSFC share similar functional properties in terms of recruitment during cognitive tasks and information carried in voxel patterns.

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