scholarly journals High resolution data-driven model of the mouse connectome

2018 ◽  
Author(s):  
Joseph E. Knox ◽  
Kameron Decker Harris ◽  
Nile Graddis ◽  
Jennifer D. Whitesell ◽  
Hongkui Zeng ◽  
...  
Keyword(s):  
Brain Connectivity ◽  
Structural Connectivity ◽  
Weighted Average ◽  
Data Driven ◽  
Scale Model ◽  
Wild Type ◽  
High Resolution Data ◽  
Spatial Connectivity ◽  
Voxel Model ◽  
Region Information

AbstractKnowledge of mesoscopic brain connectivity is important for understanding inter- and intra-region information processing. Models of structural connectivity are typically constructed and analyzed with the assumption that regions are homogeneous. We instead use the Allen Mouse Brain Connectivity Atlas to construct a model of whole brain connectivity at the scale of 100 µm voxels. The dataset used consists of 366 anterograde tracing experiments in wild type C7BL/6 mice, mapping fluorescently-labeled neuronal projections brain-wide. Inferring spatial connectivity with this dataset remains underdetermined, since the approximately 2 × 105 source voxels outnumber the number of experiments. To address this, we assume that connection patterns and strengths vary smoothly across major brain divisions. We model the connectivity at each voxel as a radial basis kernel-weighted average of the projection patterns of nearby injections. The voxel model outperforms a previous regional model in predicting held-out experiments and compared to a human-curated dataset. This voxel-scale model of the mouse connectome permits researchers to extend their previous analyses of structural connectivity to unprecedented levels of resolution, and allows for comparison with functional imaging and other datasets.

scholarly journals High-resolution data-driven model of the mouse connectome

2019 ◽  
Vol 3 (1) ◽  
pp. 217-236 ◽  
Author(s):  
Joseph E. Knox ◽  
Kameron Decker Harris ◽  
Nile Graddis ◽  
Jennifer D. Whitesell ◽  
Hongkui Zeng ◽  
...  
Keyword(s):  
Brain Connectivity ◽  
Structural Connectivity ◽  
Weighted Average ◽  
Regional Model ◽  
Data Driven ◽  
Scale Model ◽  
Wild Type ◽  
High Resolution Data ◽  
Spatial Connectivity ◽  
Voxel Model

Knowledge of mesoscopic brain connectivity is important for understanding inter- and intraregion information processing. Models of structural connectivity are typically constructed and analyzed with the assumption that regions are homogeneous. We instead use the Allen Mouse Brain Connectivity Atlas to construct a model of whole-brain connectivity at the scale of 100 μm voxels. The data consist of 428 anterograde tracing experiments in wild type C57BL/6J mice, mapping fluorescently labeled neuronal projections brain-wide. Inferring spatial connectivity with this dataset is underdetermined, since the approximately 2 × 105 source voxels outnumber the number of experiments. To address this issue, we assume that connection patterns and strengths vary smoothly across major brain divisions. We model the connectivity at each voxel as a radial basis kernel-weighted average of the projection patterns of nearby injections. The voxel model outperforms a previous regional model in predicting held-out experiments and compared with a human-curated dataset. This voxel-scale model of the mouse connectome permits researchers to extend their previous analyses of structural connectivity to much higher levels of resolution, and it allows for comparison with functional imaging and other datasets.

scholarly journals Non-Negative Data-Driven Mapping of Structural Connections in the Neonatal Brain

2020 ◽  
Author(s):  
E. Thompson ◽  
A.R. Mohammadi-Nejad ◽  
E.C. Robinson ◽  
M.F. Glasser ◽  
S. Jbabdi ◽  
...  
Keyword(s):  
White Matter ◽  
Grey Matter ◽  
Brain Connectivity ◽  
Structural Connectivity ◽  
Adult Brain ◽  
Data Driven ◽  
Neonatal Brain ◽  
Human Connectome Project ◽  
Tractography Data ◽  
Matrix Factorisation

AbstractMapping connections in the neonatal brain can provide insight into the crucial early stages of neurodevelopment that shape brain organisation and lay the foundations for cognition and behaviour. Diffusion MRI and tractography provide unique opportunities for such explorations, through estimation of white matter bundles and brain connectivity. Atlas-based tractography protocols, i.e. a priori defined sets of masks and logical operations in a template space, have been commonly used in the adult brain to drive such explorations. However, rapid growth and maturation of the brain during early development make it challenging to ensure correspondence and validity of such atlas-based tractography approaches in the developing brain. An alternative can be provided by data-driven methods, which do not depend on predefined regions of interest. Here, we develop a novel data-driven framework to extract white matter bundles and their associated grey matter networks from neonatal tractography data, based on non-negative matrix factorisation that is inherently suited to the non-negative nature of structural connectivity data. We also develop a non-negative dual regression framework to map group-level components to individual subjects. Using in-silico simulations, we evaluate the accuracy of our approach in extracting connectivity components and compare with an alternative data-driven method, independent component analysis. We apply non-negative matrix factorisation to whole-brain connectivity obtained from publicly available datasets from the Developing Human Connectome Project, yielding grey matter components and their corresponding white matter bundles. We assess the validity and interpretability of these components against traditional tractography results and grey matter networks obtained from resting-state fMRI in the same subjects. We subsequently use them to generate a parcellation of the neonatal cortex using data from 323 new-born babies and we assess the robustness and reproducibility of this connectivity-driven parcellation.

scholarly journals High-Resolution Traffic Sensing with Probe Autonomous Vehicles: A Data-Driven Approach

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 464
Author(s):  
Wei Ma ◽  
Sean Qian
Keyword(s):  
High Resolution ◽  
Autonomous Vehicles ◽  
Penetration Rate ◽  
Data Driven ◽  
Traffic Information ◽  
Flow Density ◽  
Massive Data ◽  
Market Penetration ◽  
High Resolution Data ◽  
Data Driven Approach

Recent decades have witnessed the breakthrough of autonomous vehicles (AVs), and the sensing capabilities of AVs have been dramatically improved. Various sensors installed on AVs will be collecting massive data and perceiving the surrounding traffic continuously. In fact, a fleet of AVs can serve as floating (or probe) sensors, which can be utilized to infer traffic information while cruising around the roadway networks. Unlike conventional traffic sensing methods relying on fixed location sensors or moving sensors that acquire only the information of their carrying vehicle, this paper leverages data from AVs carrying sensors for not only the information of the AVs, but also the characteristics of the surrounding traffic. A high-resolution data-driven traffic sensing framework is proposed, which estimates the fundamental traffic state characteristics, namely, flow, density and speed in high spatio-temporal resolutions and of each lane on a general road, and it is developed under different levels of AV perception capabilities and for any AV market penetration rate. Experimental results show that the proposed method achieves high accuracy even with a low AV market penetration rate. This study would help policymakers and private sectors (e.g., Waymo) to understand the values of massive data collected by AVs in traffic operation and management.

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.

A data-driven subgrid scale model in Large Eddy Simulation of turbulent premixed combustion

2021 ◽  
Vol 231 ◽  
pp. 111486
Author(s):  
Junsu Shin ◽  
Yipeng Ge ◽  
Arne Lampmann ◽  
Michael Pfitzner
Keyword(s):  
Large Eddy Simulation ◽  
Premixed Combustion ◽  
Data Driven ◽  
Scale Model ◽  
Subgrid Scale ◽  
Turbulent Premixed Combustion ◽  
Eddy Simulation ◽  
Large Eddy ◽  
Subgrid Scale Model ◽  
Turbulent Premixed

scholarly journals Salience Network Disruption in US Army Soldiers with PTSD

2018 ◽  
Author(s):  
Chadi Abdallah ◽  
Christopher Averill ◽  
Amy Ramage ◽  
Lynnette Averill ◽  
Selin Goktas ◽  
...  
Keyword(s):  
Resting State ◽  
Right Hemisphere ◽  
Brain Connectivity ◽  
Brain Network ◽  
Data Driven ◽  
Salience Network ◽  
Symptom Provocation ◽  
Us Army ◽  
Data Driven Approach ◽  
Army Soldiers

BACKGROUND: Better understanding of the neurobiology of posttraumatic stress disorder (PTSD) may be critical to developing novel, effective therapeutics. Here, we conducted a data-driven investigation using a well-established, graph- based topological measure of nodal strength to determine the extent of functional dysconnectivity in a cohort of active duty US Army soldiers with PTSD compared to controls. METHODS: 102 participants with (n=50) or without PTSD (n=52) completed functional magnetic resonance imaging (fMRI) at rest and during symptom provocation using subject-specific script imagery. Vertex/voxel global brain connectivity with global signal regression (GBCr), a measure of nodal strength, was calculated as the average of its functional connectivity with all other vertices/voxels in the brain gray matter. RESULTS: In contrast to during resting-state, where there were no group differences, we found a significantly higher GBCr, in PTSD participants compared to controls, in areas within the right hemisphere, including anterior insula, caudal- ventrolateral prefrontal, and rostral-ventrolateral parietal cortices. Overall, these clusters overlapped with the ventral and dorsal salience networks. Post hoc analysis showed increased GBCr in these salience clusters during symptom provocation compared to resting-state. In addition, resting-state GBCr in the salience clusters predicted GBCr during symptom provocation in PTSD participants but not in controls. CONCLUSION: In PTSD, increased connectivity within the salience network has been previously hypothesized, based primarily on seed-based connectivity findings. The current results strongly support this hypothesis using whole-brain network measure in a fully data-driven approach. It remains to be seen in future studies whether these identified salience disturbances would normalize following treatment.

scholarly journals Plasmodium falciparum Kelch13 and its artemisinin-resistant mutants assemble as hexamers in solution: a SAXS data driven shape restoration study

2021 ◽  
Author(s):  
Nainy Goel ◽  
Kanika Dhiman ◽  
Nidhi Kalidas ◽  
Anwesha Mukhopadhyay ◽  
Ashish ◽  
...  
Keyword(s):  
Artemisinin Resistance ◽  
Resistant Mutant ◽  
Data Driven ◽  
Wild Type ◽  
Saxs Data ◽  
Mutant Proteins ◽  
X Ray ◽  
X Ray Scattering ◽  
Shape Restoration ◽  
Spatial Positioning

AbstractArtemisinin-resistant mutations in PfKelch13 identified worldwide are mostly confined to its BTB/POZ and KRP domains. To date, only two crystal structures of the BTB/POZ-KRP domains as tight dimers are available, which limits structure-based interpretations of its functionality. Our solution Small-Angle X-ray Scattering (SAXS) data driven shape restoration of larger length of protein brought forth that: i) PfKelch13 forms a stable hexamer in P6 symmetry, ii) interactions of the N-termini drive the hexameric assembly, and iii) the six KRP domains project independently in space, forming a cauldron-like architecture. While artemisinin-sensitive mutant A578S packed like the wild-type, hexameric assemblies of dominant artemisinin-resistant mutant proteins R539T and C580Y displayed detectable differences in spatial positioning of their BTB/POZ-KRP domains. Lastly, mapping of mutations known to enable artemisinin resistance explained that most mutations exist mainly in these domains because they are non-detrimental to assembly of mutant PfKelch13 and yet can alter the flux of downstream events essential for susceptibility to artemisinin.

scholarly journals Asymmetric high-order anatomical brain connectivity sculpts effective connectivity

2020 ◽  
Vol 4 (3) ◽  
pp. 871-890
Author(s):  
Arseny A. Sokolov ◽  
Peter Zeidman ◽  
Adeel Razi ◽  
Michael Erb ◽  
Philippe Ryvlin ◽  
...  
Keyword(s):  
White Matter ◽  
Dynamic Range ◽  
Diffusion Processes ◽  
Brain Connectivity ◽  
Effective Connectivity ◽  
Structural Connectivity ◽  
Laplacian Matrix ◽  
Graph Laplacian ◽  
Brain Regions ◽  
High Order

Bridging the gap between symmetric, direct white matter brain connectivity and neural dynamics that are often asymmetric and polysynaptic may offer insights into brain architecture, but this remains an unresolved challenge in neuroscience. Here, we used the graph Laplacian matrix to simulate symmetric and asymmetric high-order diffusion processes akin to particles spreading through white matter pathways. The simulated indirect structural connectivity outperformed direct as well as absent anatomical information in sculpting effective connectivity, a measure of causal and directed brain dynamics. Crucially, an asymmetric diffusion process determined by the sensitivity of the network nodes to their afferents best predicted effective connectivity. The outcome is consistent with brain regions adapting to maintain their sensitivity to inputs within a dynamic range. Asymmetric network communication models offer a promising perspective for understanding the relationship between structural and functional brain connectomes, both in normalcy and neuropsychiatric conditions.

scholarly journals Data-Driven Models Reveal Mutant Cell Behaviors Important for Myxobacterial Aggregation

mSystems ◽  
2020 ◽  
Vol 5 (4) ◽  
Author(s):  
Zhaoyang Zhang ◽  
Christopher R. Cotter ◽  
Zhe Lyu ◽  
Lawrence J. Shimkets ◽  
Oleg A. Igoshin
Keyword(s):  
Cell Tracking ◽  
Mutant Cell ◽  
Myxococcus Xanthus ◽  
Data Driven ◽  
Self Organization ◽  
Cell Behavior ◽  
Significant Heterogeneity ◽  
Wild Type ◽  
Content Type ◽  
Mutant Strains

ABSTRACT Single mutations frequently alter several aspects of cell behavior but rarely reveal whether a particular statistically significant change is biologically significant. To determine which behavioral changes are most important for multicellular self-organization, we devised a new methodology using Myxococcus xanthus as a model system. During development, myxobacteria coordinate their movement to aggregate into spore-filled fruiting bodies. We investigate how aggregation is restored in two mutants, csgA and pilC, that cannot aggregate unless mixed with wild-type (WT) cells. To this end, we use cell tracking to follow the movement of fluorescently labeled cells in combination with data-driven agent-based modeling. The results indicate that just like WT cells, both mutants bias their movement toward aggregates and reduce motility inside aggregates. However, several aspects of mutant behavior remain uncorrected by WT, demonstrating that perfect recreation of WT behavior is unnecessary. In fact, synergies between errant behaviors can make aggregation robust. IMPORTANCE Self-organization into spatial patterns is evident in many multicellular phenomena. Even for the best-studied systems, our ability to dissect the mechanisms driving coordinated cell movement is limited. While genetic approaches can identify mutations perturbing multicellular patterns, the diverse nature of the signaling cues coupled to significant heterogeneity of individual cell behavior impedes our ability to mechanistically connect genes with phenotype. Small differences in the behaviors of mutant strains could be irrelevant or could sometimes lead to large differences in the emergent patterns. Here, we investigate rescue of multicellular aggregation in two mutant strains of Myxococcus xanthus mixed with wild-type cells. The results demonstrate how careful quantification of cell behavior coupled to data-driven modeling can identify specific motility features responsible for cell aggregation and thereby reveal important synergies and compensatory mechanisms. Notably, mutant cells do not need to precisely recreate wild-type behaviors to achieve complete aggregation.

scholarly journals Data-Driven Clustering Reveals a Link Between Symptoms and Functional Brain Connectivity in Depression

2019 ◽  
Vol 4 (1) ◽  
pp. 16-26 ◽  
Author(s):  
Luigi A. Maglanoc ◽  
Nils Inge Landrø ◽  
Rune Jonassen ◽  
Tobias Kaufmann ◽  
Aldo Córdova-Palomera ◽  
...  
Keyword(s):  
Brain Connectivity ◽  
Data Driven ◽  
Functional Brain
Sign in / Sign up

Export Citation Format

Share Document