scholarly journals Low dimensional morphospace of topological motifs in human fMRI brain networks

2017 ◽  
Author(s):  
Sarah E. Morgan ◽  
Sophie Achard ◽  
Maite Termenon ◽  
Petra E. Vértes ◽  
Edward T. Bullmore
Keyword(s):  
Driving Forces ◽  
Average Length ◽  
Brain Networks ◽  
Small World ◽  
Generative Models ◽  
Data Driven ◽  
Long Distance ◽  
Network Properties ◽  
Low Dimensional ◽  
The Cost

AbstractWe present a low-dimensional morphospace of fMRI brain networks, where axes are defined in a data-driven manner based on the network motifs. The morphospace allows us to identify the key variations in healthy fMRI networks in terms of their underlying motifs and we observe that two principal components (PCs) can account for 97% of the motif variability. The first PC corresponds to the small-world axis and correlates strongly with the networks’ global efficiency. There is also some evidence that PC1 correlates with the average length of the 5% of longest edges in the network. Hence this axis represents the trade-off between the cost of long distance edges and their topological benefits. The second PC correlates with the networks’ assortativity. Finally, we show that the economical clustering generative model proposed by Vértes et al. can approximately reproduce the motif PC space of the real fMRI brain networks, in contrast to other generative models. Overall, the motif morphospace provides a powerful way to visualise the relationships between network properties and to study the driving forces behind the topology of fMRI brain networks.

scholarly journals Low-dimensional morphospace of topological motifs in human fMRI brain networks

2018 ◽  
Vol 2 (2) ◽  
pp. 285-302 ◽  
Author(s):  
Sarah E. Morgan ◽  
Sophie Achard ◽  
Maite Termenon ◽  
Edward T. Bullmore ◽  
Petra E. Vértes
Keyword(s):  
World Trade ◽  
Brain Networks ◽  
Small World ◽  
Generative Models ◽  
Data Driven ◽  
Functional Networks ◽  
Network Motifs ◽  
Trade Off ◽  
Network Properties ◽  
Low Dimensional

We present a low-dimensional morphospace of fMRI brain networks, where axes are defined in a data-driven manner based on the network motifs. The morphospace allows us to identify the key variations in healthy fMRI networks in terms of their underlying motifs, and we observe that two principal components (PCs) can account for 97% of the motif variability. The first PC of the motif distribution is correlated with efficiency and inversely correlated with transitivity. Hence this axis approximately conforms to the well-known economical small-world trade-off between integration and segregation in brain networks. Finally, we show that the economical clustering generative model proposed by Vértes et al. ( 2012 ) can approximately reproduce the motif morphospace of the real fMRI brain networks, in contrast to other generative models. Overall, the motif morphospace provides a powerful way to visualize the relationships between network properties and to investigate generative or constraining factors in the formation of complex human brain functional networks.

scholarly journals Fast reconfiguration of high-frequency brain networks in response to surprising changes in auditory input

2012 ◽  
Vol 107 (5) ◽  
pp. 1421-1430 ◽  
Author(s):  
Ruth M. Nicol ◽  
Sandra C. Chapman ◽  
Petra E. Vértes ◽  
Pradeep J. Nathan ◽  
Marie L. Smith ◽  
...  
Keyword(s):  
High Frequency ◽  
Brain Networks ◽  
Small World ◽  
Brain Network ◽  
Auditory Presentation ◽  
Long Distance ◽  
Sensory Environment ◽  
Response Network ◽  
Rapid Changes ◽  
Measure Synchronization

How do human brain networks react to dynamic changes in the sensory environment? We measured rapid changes in brain network organization in response to brief, discrete, salient auditory stimuli. We estimated network topology and distance parameters in the immediate central response period, <1 s following auditory presentation of standard tones interspersed with occasional deviant tones in a mismatch-negativity (MMN) paradigm, using magnetoencephalography (MEG) to measure synchronization of high-frequency (gamma band; 33–64 Hz) oscillations in healthy volunteers. We found that global small-world parameters of the networks were conserved between the standard and deviant stimuli. However, surprising or unexpected auditory changes were associated with local changes in clustering of connections between temporal and frontal cortical areas and with increased interlobar, long-distance synchronization during the 120- to 250-ms epoch (coinciding with the MMN-evoked response). Network analysis of human MEG data can resolve fast local topological reconfiguration and more long-range synchronization of high-frequency networks as a systems-level representation of the brain's immediate response to salient stimuli in the dynamically changing sensory environment.

Bayesian recurrent neural network as a tool for reconstructing dynamical systems from multidimensional data

2020 ◽  
Author(s):  
Alexander Feigin ◽  
Aleksei Seleznev ◽  
Dmitry Mukhin ◽  
Andrey Gavrilov ◽  
Evgeny Loskutov
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Cost Function ◽  
Recurrent Neural Network ◽  
Bayesian Optimization ◽  
Data Driven ◽  
High Dimensional ◽  
Model Learning ◽  
Low Dimensional ◽  
The Cost

&lt;p&gt;We suggest a new method for construction of data-driven dynamical models from observed multidimensional time series. The method is based on a recurrent neural network (RNN) with specific structure, which allows for the joint reconstruction of both a low-dimensional embedding for dynamical components in the data and an operator describing the low-dimensional evolution of the system. The key link of the method is a Bayesian optimization of both model structure and the hypothesis about the data generating law, which is needed for constructing the cost function for model learning. &amp;#160;The form of the model we propose allows us to construct a stochastic dynamical system of moderate dimension that copies dynamical properties of the original high-dimensional system. An advantage of the proposed method is the data-adaptive properties of the RNN model: it is based on the adjustable nonlinear elements and has easily scalable structure. The combination of the RNN with the Bayesian optimization procedure efficiently provides the model with statistically significant nonlinearity and dimension.&lt;br&gt;The method developed for the model optimization aims to detect the long-term connections between system&amp;#8217;s states &amp;#8211; the memory of the system: the cost-function used for model learning is constructed taking into account this factor. In particular, in the case of absence of interaction between the dynamical component and noise, the method provides unbiased reconstruction of the hidden deterministic system. In the opposite case when the noise has strong impact on the dynamics, the method yield a model in the form of a nonlinear stochastic map determining the Markovian process with memory. Bayesian approach used for selecting both the optimal model&amp;#8217;s structure and the appropriate cost function allows to obtain the statistically significant inferences about the dynamical signal in data as well as its interaction with the noise components.&lt;br&gt;Data driven model derived from the relatively short time series of the QG3 model &amp;#8211; the high dimensional nonlinear system producing chaotic behavior &amp;#8211; is shown be able to serve as a good simulator for the QG3 LFV components. The statistically significant recurrent states of the QG3 model, i.e. the well-known teleconnections in NH, are all reproduced by the model obtained. Moreover, statistics of the residence times of the model near these states is very close to the corresponding statistics of the original QG3 model. These results demonstrate that the method can be useful in modeling the variability of the real atmosphere.&lt;/p&gt;&lt;p&gt;The work was supported by the Russian Science Foundation (Grant No. 19-42-04121).&lt;/p&gt;

scholarly journals Using Pareto optimality to explore the topology and dynamics of the human connectome

2014 ◽  
Vol 369 (1653) ◽  
pp. 20130530 ◽  
Author(s):  
Andrea Avena-Koenigsberger ◽  
Joaquín Goñi ◽  
Richard F. Betzel ◽  
Martijn P. van den Heuvel ◽  
Alessandra Griffa ◽  
...  
Keyword(s):  
Human Brain ◽  
Network Architecture ◽  
Driving Forces ◽  
Brain Networks ◽  
Small World ◽  
Spatial Arrangement ◽  
Mathematical Framework ◽  
Network Efficiency ◽  
Network Cost ◽  
Communication Efficiency

Graph theory has provided a key mathematical framework to analyse the architecture of human brain networks. This architecture embodies an inherently complex relationship between connection topology, the spatial arrangement of network elements, and the resulting network cost and functional performance. An exploration of these interacting factors and driving forces may reveal salient network features that are critically important for shaping and constraining the brain's topological organization and its evolvability. Several studies have pointed to an economic balance between network cost and network efficiency with networks organized in an ‘economical’ small-world favouring high communication efficiency at a low wiring cost. In this study, we define and explore a network morphospace in order to characterize different aspects of communication efficiency in human brain networks. Using a multi-objective evolutionary approach that approximates a Pareto-optimal set within the morphospace, we investigate the capacity of anatomical brain networks to evolve towards topologies that exhibit optimal information processing features while preserving network cost. This approach allows us to investigate network topologies that emerge under specific selection pressures, thus providing some insight into the selectional forces that may have shaped the network architecture of existing human brains.

scholarly journals Disrupted resting-state brain network properties in obesity: decreased global and putaminal cortico-striatal network efficiency

2016 ◽  
Vol 47 (4) ◽  
pp. 585-596 ◽  
Author(s):  
K. Baek ◽  
L. S. Morris ◽  
P. Kundu ◽  
V. Voon
Keyword(s):  
Resting State ◽  
Brain Networks ◽  
Brain Network ◽  
Data Driven ◽  
Local Network ◽  
Healthy Controls ◽  
Network Efficiency ◽  
Whole Brain ◽  
Network Properties ◽  
Obese Subjects

BackgroundThe efficient organization and communication of brain networks underlie cognitive processing and their disruption can lead to pathological behaviours. Few studies have focused on whole-brain networks in obesity and binge eating disorder (BED). Here we used multi-echo resting-state functional magnetic resonance imaging (rsfMRI) along with a data-driven graph theory approach to assess brain network characteristics in obesity and BED.MethodMulti-echo rsfMRI scans were collected from 40 obese subjects (including 20 BED patients) and 40 healthy controls and denoised using multi-echo independent component analysis (ME-ICA). We constructed a whole-brain functional connectivity matrix with normalized correlation coefficients between regional mean blood oxygenation level-dependent (BOLD) signals from 90 brain regions in the Automated Anatomical Labeling atlas. We computed global and regional network properties in the binarized connectivity matrices with an edge density of 5%–25%. We also verified our findings using a separate parcellation, the Harvard–Oxford atlas parcellated into 470 regions.ResultsObese subjects exhibited significantly reduced global and local network efficiency as well as decreased modularity compared with healthy controls, showing disruption in small-world and modular network structures. In regional metrics, the putamen, pallidum and thalamus exhibited significantly decreased nodal degree and efficiency in obese subjects. Obese subjects also showed decreased connectivity of cortico-striatal/cortico-thalamic networks associated with putaminal and cortical motor regions. These findings were significant with ME-ICA with limited group differences observed with conventional denoising or single-echo analysis.ConclusionsUsing this data-driven analysis of multi-echo rsfMRI data, we found disruption in global network properties and motor cortico-striatal networks in obesity consistent with habit formation theories. Our findings highlight the role of network properties in pathological food misuse as possible biomarkers and therapeutic targets.

Extended-Range Prediction with Low-Dimensional, Stochastic-Dynamic Models: A Data-driven Approach

2012 ◽  
Author(s):  
Michael Ghil ◽  
Mickael D. Chekroun ◽  
Dmitri Kondrashov ◽  
Michael K. Tippett ◽  
Andrew Robertson ◽  
...  
Keyword(s):  
Dynamic Models ◽  
Data Driven ◽  
Stochastic Dynamic ◽  
Extended Range ◽  
Data Driven Approach ◽  
Low Dimensional

Network Embedding on Hierarchical Community Structure Network

2021 ◽  
Vol 15 (4) ◽  
pp. 1-23
Author(s):  
Guojie Song ◽  
Yun Wang ◽  
Lun Du ◽  
Yi Li ◽  
Junshan Wang
Keyword(s):  
Community Structure ◽  
Structural Information ◽  
Spherical Surface ◽  
Network Embedding ◽  
The Galaxy ◽  
Community Information ◽  
The Hierarchical Structure ◽  
Network Properties ◽  
Multi Class Classification ◽  
Low Dimensional

Network embedding is a method of learning a low-dimensional vector representation of network vertices under the condition of preserving different types of network properties. Previous studies mainly focus on preserving structural information of vertices at a particular scale, like neighbor information or community information, but cannot preserve the hierarchical community structure, which would enable the network to be easily analyzed at various scales. Inspired by the hierarchical structure of galaxies, we propose the Galaxy Network Embedding (GNE) model, which formulates an optimization problem with spherical constraints to describe the hierarchical community structure preserving network embedding. More specifically, we present an approach of embedding communities into a low-dimensional spherical surface, the center of which represents the parent community they belong to. Our experiments reveal that the representations from GNE preserve the hierarchical community structure and show advantages in several applications such as vertex multi-class classification, network visualization, and link prediction. The source code of GNE is available online.

scholarly journals Machine learning applied to simulations of collisions between rotating, differentiated planets

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Miles L. Timpe ◽  
Maria Han Veiga ◽  
Mischa Knabenhans ◽  
Joachim Stadel ◽  
Stefano Marelli
Keyword(s):  
Machine Learning ◽  
Planet Formation ◽  
Critical Role ◽  
Ensemble Methods ◽  
Terrestrial Planet ◽  
Data Driven ◽  
Analytic Methods ◽  
Accurate Treatment ◽  
Post Impact ◽  
The Cost

AbstractIn the late stages of terrestrial planet formation, pairwise collisions between planetary-sized bodies act as the fundamental agent of planet growth. These collisions can lead to either growth or disruption of the bodies involved and are largely responsible for shaping the final characteristics of the planets. Despite their critical role in planet formation, an accurate treatment of collisions has yet to be realized. While semi-analytic methods have been proposed, they remain limited to a narrow set of post-impact properties and have only achieved relatively low accuracies. However, the rise of machine learning and access to increased computing power have enabled novel data-driven approaches. In this work, we show that data-driven emulation techniques are capable of classifying and predicting the outcome of collisions with high accuracy and are generalizable to any quantifiable post-impact quantity. In particular, we focus on the dataset requirements, training pipeline, and classification and regression performance for four distinct data-driven techniques from machine learning (ensemble methods and neural networks) and uncertainty quantification (Gaussian processes and polynomial chaos expansion). We compare these methods to existing analytic and semi-analytic methods. Such data-driven emulators are poised to replace the methods currently used in N-body simulations, while avoiding the cost of direct simulation. This work is based on a new set of 14,856 SPH simulations of pairwise collisions between rotating, differentiated bodies at all possible mutual orientations.

scholarly journals Maximum Entropy Analysis of Flow Networks: Theoretical Foundation and Applications

Entropy ◽  
2019 ◽  
Vol 21 (8) ◽  
pp. 776 ◽  
Author(s):  
Robert K. Niven ◽  
Markus Abel ◽  
Michael Schlegel ◽  
Steven H. Waldrip
Keyword(s):  
Maximum Entropy ◽  
Joint Probability ◽  
Financial Economic ◽  
Flow Networks ◽  
Flow Network ◽  
Physical Constraints ◽  
Generalized Maximum Entropy ◽  
Deterministic Solution ◽  
Network Properties ◽  
The Cost

The concept of a “flow network”—a set of nodes and links which carries one or more flows—unites many different disciplines, including pipe flow, fluid flow, electrical, chemical reaction, ecological, epidemiological, neurological, communications, transportation, financial, economic and human social networks. This Feature Paper presents a generalized maximum entropy framework to infer the state of a flow network, including its flow rates and other properties, in probabilistic form. In this method, the network uncertainty is represented by a joint probability function over its unknowns, subject to all that is known. This gives a relative entropy function which is maximized, subject to the constraints, to determine the most probable or most representative state of the network. The constraints can include “observable” constraints on various parameters, “physical” constraints such as conservation laws and frictional properties, and “graphical” constraints arising from uncertainty in the network structure itself. Since the method is probabilistic, it enables the prediction of network properties when there is insufficient information to obtain a deterministic solution. The derived framework can incorporate nonlinear constraints or nonlinear interdependencies between variables, at the cost of requiring numerical solution. The theoretical foundations of the method are first presented, followed by its application to a variety of flow networks.

scholarly journals Proposal of resources optimization in the hospital treatment of heart failure by an increased utilization of cardiac rehabilitation

2016 ◽  
Vol 66 (4) ◽  
Author(s):  
Tommaso Diaco ◽  
Geremia Milanesi ◽  
Daniela Zaniboni ◽  
Massimo Gritti ◽  
Gianna Zavatteri ◽  
...  
Keyword(s):  
Cardiac Rehabilitation ◽  
Average Length ◽  
Mean Value ◽  
Hospital Treatment ◽  
Utilization Rate ◽  
Average Length Of Stay ◽  
Use Of Resources ◽  
Acute Patient ◽  
The Cost ◽  
Acute Patients

weight on social cost. An improved resources utilization could promote a reduction of the new hospitalization and a of medical costs. Working hypotesis: To analyze a model of increased utilization of our Cardiac Rehabilitation (CR) Unit, aiming at improving the cost/profit ratio through a better use of resources and a better assignment of care. With a reduction of average length of stay in the Operative Units for acute patients, we could promote a demand of post-acute hospitalization of 950.7 days of hospitalization that could be assigned to Cardiologic Rehabilitation Unit. Results: With the transfer of patients the utilization rate of CR would increase to 97%. With a mean period in bed of 15.3 days we could hospitalize 62 additional patients and the total margin of contribution would became positive: 69.817 euro. The break even analysis applied to costs and returns of the Unit shows a further indication to increase the hospitalization number in CR Unit with patients transfered from acute patient units. Under the same costs the recovery of efficiency leads to a reduction of variable costs. In the same time there is an increase of returns due to an increase of mean value for case and an increase of services. Conclusion: The increase in the efficiency in the utilization of CR Unit leads to an increase of the Hospital efficiency. The transfer of patients from acute units to CR Unit would allow an increased hospitalization rate for acute patients without requiring additional resources.

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