scholarly journals Adaptive Cortical Parcellations for Source Reconstructed EEG/MEG Connectomes

2017 ◽  
Author(s):  
Seyedeh-Rezvan Farahibozorg ◽  
Richard N Henson ◽  
Olaf Hauk
Keyword(s):  
Image Segmentation ◽  
Cross Talk ◽  
Real Data ◽  
Network Reconstruction ◽  
Brain Network ◽  
Reconstruction Method ◽  
List Type ◽  
Resolution Limit ◽  
Starting Point ◽  
Sensor Configuration

AbstractThere is growing interest in the rich temporal and spectral properties of the brain's functional connectome that are provided by Electro- and Magnetoencephalography (EEG/MEG). However, the problem of leakage between brain sources that arises when reconstructing brain activity from EEG/MEG recordings outside the head makes it difficult to distinguish true connections from spurious connections, even when connections are based on measures that ignore zero-lag dependencies. In particular, standard anatomical parcellations for potential cortical sources tend to over- or under-sample the real spatial resolution of EEG/MEG. By using information from cross-talk functions (CTFs) that objectively describe leakage for a given sensor configuration and distributed source reconstruction method, we introduce methods for optimising the number of parcels while simultaneously minimising the leakage between them. More specifically, we compare two image segmentation algorithms: 1) a split-and-merge (SaM) algorithm based on standard anatomical parcellations and 2) a region growing (RG) algorithm based on all the brain vertices with no prior parcellation. Interestingly, when applied to minimum-norm reconstructions for EEG/MEG configurations from real data, both algorithms yielded approximately 70 parcels despite their different starting points, suggesting that this reflects the resolution limit of this particular sensor configuration and reconstruction method. Importantly, when compared against standard anatomical parcellations, resolution matrices of adaptive parcellations showed notably higher sensitivity and distinguishability of parcels. Furthermore, extensive simulations of realistic networks under various circumstances revealed significant improvements in network reconstruction accuracies, particularly in reducing false leakage-induced connections. Adaptive parcellations therefore allow a more accurate reconstruction of functional EEG/MEG connectomes.HighlightsWe introduce adaptive cortical parcellation algorithms for E/MEG source estimation.Algorithms are based on cross-talk functions and image segmentation methods.The resulting parcellations yielded ~70 parcels regardless of starting point.Sensitivity and distinguishability improved compared to anatomical parcellations.Accuracy of realistic whole-brain network reconstruction improved significantly.

scholarly journals Diffuse optical reconstructions of fNIRS data using Maximum Entropy on the Mean

2021 ◽  
Author(s):  
Zhengchen Cai ◽  
Alexis Machado ◽  
Rasheda Arman Chowdhury ◽  
Amanda Spilkin ◽  
Thomas Vincent ◽  
...  
Keyword(s):  
Maximum Entropy ◽  
Near Infrared ◽  
Optical Tomography ◽  
Real Data ◽  
Reconstruction Method ◽  
List Type ◽  
Functional Near Infrared Spectroscopy ◽  
Concentration Changes ◽  
Weighting Strategy ◽  
The Mean

AbstractFunctional near-infrared spectroscopy (fNIRS) measures the hemoglobin concentration changes associated with neuronal activity. Diffuse optical tomography (DOT) consists of reconstructing the optical density changes measured from scalp channels to the oxy-/deoxy-hemoglobin (i.e., HbO/HbR) concentration changes within the cortical regions. In the present study, we adapted a nonlinear source localization method developed and validated in the context of Electro- and Magneto-Encephalography (EEG/MEG): the Maximum Entropy on the Mean (MEM), to solve the inverse problem of DOT reconstruction. We first introduced depth weighting strategy within the MEM framework for DOT reconstruction to avoid biasing the reconstruction results of DOT towards superficial regions. We also proposed a new initialization of the MEM model improving the temporal accuracy of the original MEM framework. To evaluate MEM performance and compare with widely used depth weighted Minimum Norm Estimate (MNE) inverse solution, we applied a realistic simulation scheme which contained 4000 simulations generated by 250 different seeds at different locations and 4 spatial extents ranging from 3 to 40cm2 along the cortical surface. Our results showed that overall MEM provided more accurate DOT reconstructions than MNE. Moreover, we found that MEM was remained particularly robust in low signal-to-noise ratio (SNR) conditions. The proposed method was further illustrated by comparing to functional Magnetic Resonance Imaging (fMRI) activation maps, on real data involving finger tapping tasks with two different montages. The results showed that MEM provided more accurate HbO and HbR reconstructions in spatial agreement with the main fMRI cluster, when compared to MNE.HighlightsWe introduced a new fNIRS reconstruction method - Maximum Entropy on the Mean.We implemented depth weighting strategy within the MEM framework.We improved the temporal accuracy of the original MEM reconstruction.Performances of MEM and MNE were evaluated with realistic simulations and real data.MEM provided more accurate and robust reconstructions than MNE.

Rising from the ashes: Satyam's story

2014 ◽  
Vol 4 (1) ◽  
pp. 1-8
Author(s):  
Tanvi Gautam
Keyword(s):  
Change Management ◽  
Subject Area ◽  
Executive Education ◽  
List Type ◽  
Corporate Communications ◽  
It Industry ◽  
Market Reactions ◽  
Content Type ◽  
Employee Trust ◽  
Starting Point

Subject area Leadership, human resource management, crisis management, change management and communication. Study level/applicability Executive education; postgraduate; undergraduate. Case overview This case study describes the collapse of Satyam, a leading IT industry service provider from India. Satyam went into a crisis mode after revelation of financial fraud by its Chairman. This resulted in a crisis not just for the company, its clients and employees – but it also had the potential to shake up the entire Indian IT industry the world over, by shattering investor and client confidence in the Indian IT sector. The case provides the students with an inside view of the unfolding of events at Satyam and the people challenges that emerge in a crisis scenario. The case outlines reactions from the industry, government, clients and employees as they tried to make sense of a very chaotic situation, and its multi-level ramifications both within India and outside. The case ends with Thallapalli Hari, the Global Head of Marketing and Communication and ex-head of HR, trying to visualise and prioritise a course of action to propose to other members of the leadership team. Expected learning outcomes The key aim of this case is to provide a backdrop to the crisis, and also help students put themselves in the role of an HR crisis manager as well as portray the decision making and communication challenges that emerge in chaotic situations. The importance of an immediate and yet strategic response is emphasised and the case is a great starting point to have a discussion on the competencies and skills required in HR to lead under unusual circumstances. This case allows participants to get an in-depth understanding of the collapse of Satyam. The case also illustrates principles of leadership, change management and communication, in particular: Leadership: The Satyam story is an HR and leadership crisis nightmare come true. What should an HR leader do when you wake up to find your company with a ruined reputation, minimal financial capital, 53,000 employees on the payroll and more than 500 clients with pending deliverables worldwide. Where do you begin? The case illustrates a situation where immediate action is required to stop the tailspin into which the company was heading. Change management: The situation demanded that change be managed from a chaotic system to a stable system. The big issue though remains as to how one can get a system into a state of stability when everything is changing at the same time. Most change management plans have some stable variables, however in the case of Satyam there were multiple changes taking place simultaneously. A combination of change in leadership, client relationships, employee trust and confidence, market reactions together make for a perfect storm. Dealing with even one of these changes is a challenge for a company. In the case of Satyam, its entire existence was at stake. Communication: The demands for communicating effectively in a crisis situation are different than communicating under stable systems. The choice of medium, the speed of response, the content all need careful monitoring. Whereas most companies have teams that separately deal with internal and external communication, Satyam provides a unique situation where managing both effectively at the same time was critical to the future of the firm. The stakes for effective communication are much higher under the circumstances. This case can be used in organizational behaviour, human resources and corporate communications modules being taught to under-graduates, post-graduates and for executive education. Supplementary materials Teaching notes are available for educators only. Please contact your library to gain login details or email [email protected] to request teaching notes.

scholarly journals Refractive Two-View Reconstruction for Underwater 3D Vision

2019 ◽  
Vol 128 (5) ◽  
pp. 1101-1117 ◽  
Author(s):  
François Chadebecq ◽  
Francisco Vasconcelos ◽  
René Lacher ◽  
Efthymios Maneas ◽  
Adrien Desjardins ◽  
...  
Keyword(s):  
Structure From Motion ◽  
Real Data ◽  
Reconstruction Method ◽  
Underwater Imaging ◽  
Shape Estimation ◽  
Camera Model ◽  
Projection Model ◽  
3D Vision ◽  
Object Distance ◽  
View Reconstruction

AbstractRecovering 3D geometry from cameras in underwater applications involves the Refractive Structure-from-Motion problem where the non-linear distortion of light induced by a change of medium density invalidates the single viewpoint assumption. The pinhole-plus-distortion camera projection model suffers from a systematic geometric bias since refractive distortion depends on object distance. This leads to inaccurate camera pose and 3D shape estimation. To account for refraction, it is possible to use the axial camera model or to explicitly consider one or multiple parallel refractive interfaces whose orientations and positions with respect to the camera can be calibrated. Although it has been demonstrated that the refractive camera model is well-suited for underwater imaging, Refractive Structure-from-Motion remains particularly difficult to use in practice when considering the seldom studied case of a camera with a flat refractive interface. Our method applies to the case of underwater imaging systems whose entrance lens is in direct contact with the external medium. By adopting the refractive camera model, we provide a succinct derivation and expression for the refractive fundamental matrix and use this as the basis for a novel two-view reconstruction method for underwater imaging. For validation we use synthetic data to show the numerical properties of our method and we provide results on real data to demonstrate its practical application within laboratory settings and for medical applications in fluid-immersed endoscopy. We demonstrate our approach outperforms classic two-view Structure-from-Motion method relying on the pinhole-plus-distortion camera model.

scholarly journals Evolution Model for Epidemic Diseases Based on the Kaplan-Meier Curve Determination

Mathematics ◽  
2020 ◽  
Vol 8 (8) ◽  
pp. 1260
Author(s):  
Jose M. Calabuig ◽  
Luis M. García-Raffi ◽  
Albert García-Valiente ◽  
Enrique A. Sánchez-Pérez
Keyword(s):  
Real Data ◽  
Local Minima ◽  
Global Minima ◽  
Huge Number ◽  
Quadratic Error ◽  
Kaplan Meier ◽  
Starting Point ◽  
Curve Determination ◽  
Epidemic Diseases

We show a simple model of the dynamics of a viral process based, on the determination of the Kaplan-Meier curve P of the virus. Together with the function of the newly infected individuals I, this model allows us to predict the evolution of the resulting epidemic process in terms of the number E of the death patients plus individuals who have overcome the disease. Our model has as a starting point the representation of E as the convolution of I and P. It allows introducing information about latent patients—patients who have already been cured but are still potentially infectious, and re-infected individuals. We also provide three methods for the estimation of P using real data, all of them based on the minimization of the quadratic error: the exact solution using the associated Lagrangian function and Karush-Kuhn-Tucker conditions, a Monte Carlo computational scheme acting on the total set of local minima, and a genetic algorithm for the approximation of the global minima. Although the calculation of the exact solutions of all the linear systems provided by the use of the Lagrangian naturally gives the best optimization result, the huge number of such systems that appear when the time variable increases makes it necessary to use numerical methods. We have chosen the genetic algorithms. Indeed, we show that the results obtained in this way provide good solutions for the model.

scholarly journals Analytical Fuzzy Predictive Control Applied to Wastewater Treatment Biological Processes

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-29 ◽  
Author(s):  
Pedro M. Vallejo LLamas ◽  
Pastora Vega
Keyword(s):  
Wastewater Treatment ◽  
Predictive Control ◽  
Treatment Process ◽  
Linear Time ◽  
Real Data ◽  
Control Law ◽  
Time Varying ◽  
Wastewater Treatment Process ◽  
Starting Point ◽  
Fuzzy Predictive Control

A novel control fuzzy predictive control law is proposed and successfully applied to a wastewater treatment process in this paper. The proposed control law allows us to evaluate the control signal in an analytical way, each sampling time being a nonlinear and fuzzy alternative to other classic predictive controllers. The control law is based on the formalization of the internal fuzzy predictive model of the process as linear time-varying state space equations that are updated every discrete time instant to take into account the nonlinearity effects due to disturbance action and changes in the operating point with time. The model is then used to evaluate the predictions, and, taking them as a starting point and considering them as a paradigm of the predictive functional control strategy, a control law, it is derived in an analytical and explicit way by imposing on the outputs of the follow-up of certain reference trajectories previously established. The work presented here addresses the application of this particular strategy of intelligent predictive control to the case of an activated sludge wastewater treatment process successfully in a simulation environment of a real plant taking into account real data for the disturbance records. Such a process is multivariable, nonlinear, time varying, and difficult to control due to its biological nature. The proposed control law can be straightforwardly used within a dual-mode MPC scheme to handle constraints, as a nonlinear and fuzzy alternative to the classic state feedback control law.

Ambiguities in AVO inversion of reflections from a gas‐sand

Geophysics ◽  
1995 ◽  
Vol 60 (1) ◽  
pp. 134-141 ◽  
Author(s):  
Giuseppe Drufuca ◽  
Alfredo Mazzotti
Keyword(s):  
Unique Solution ◽  
Incidence Angle ◽  
Synthetic Data ◽  
Real Data ◽  
Shear Velocity ◽  
Sand Layer ◽  
Borehole Data ◽  
Starting Point ◽  
Wide Range ◽  
Compressional Velocity

We examine the reflections from a thick sand layer embedded in shales deposited in an open marine environment of Miocene age. Borehole data indicate that the sand bed is gas saturated. Making the assumptions of single interface reflections, plane‐wave propagation in elastic and isotropic media, and correct amplitude recovery of the actual seismic data, we try to invert the amplitude variation with offset (AVO) response for the compressional velocity [Formula: see text], shear velocity [Formula: see text], and density [Formula: see text] of the gas‐sand layer, knowing the parameters of the upper layer and the calibration constant. The actual reflections reach incidence angles up to 54 degrees at the farthest offset. Notwithstanding the large range of incidence angles, the outcomes of the inversion are ambiguous for we find many solutions that fit equally well, in a least‐squares sense, the observed AVO response. We present the locus of the solutions as curves in compressional velocity [Formula: see text], shear velocity [Formula: see text], and density [Formula: see text] space. To gain a better understanding of the results, we also perform the same inversion experiment on synthetic AVO data derived from the borehole information. We find that when inverting the AVO response in the same range of incidence angles as in the real data case, the exact solution is found whichever starting point we choose; that is, we have no ambiguity. However, if we limit the incidence angle range, e.g., to 15 degrees, the invention is no longer able to find a unique solution and the set of admissible solutions defines regular curves in [Formula: see text], [Formula: see text], [Formula: see text] space. We infer that residual noise in the recorded data is responsible for the ambiguities of the solutions, and that because of numerical noise, a wide range of incidence angle is required to obtain a unique solution even in noise‐free synthetic data.

scholarly journals Modelling local autonomy and dependence through cooperative relations

2018 ◽  
Vol 31 (4) ◽  
pp. 410-425 ◽  
Author(s):  
Lluís Medir Tejado ◽  
Esther Pano
Keyword(s):  
Local Governments ◽  
Intergovernmental Relations ◽  
Empirical Studies ◽  
Open Data ◽  
Real Data ◽  
Local Autonomy ◽  
Content Type ◽  
Starting Point ◽  
Governance Tools ◽  
Multi Level

Purpose Given the spread of multi-level governance tools, interaction between local and regional governments has become an important mechanism for service delivery and the implementation of public policies. The purpose of this paper is to empirically test a model of cooperative relations affecting local governments and thus having impact on local autonomy and dependence. Design/methodology/approach This paper takes previous typologies as a starting point to theoretically build and empirically test a model of cooperative relations based on two selected indicators: the degree of autonomy-dependence and the degree of rigidity-flexibility of the cooperative system. In a second step, the authors stimulate the model numerically to match real data on it coming from South European local governments to assess the functioning of the theory in a concrete space and time. Findings The combination of the aforementioned concepts, creates a four-option theoretical model that describes four possible situations where cooperative intergovernmental relations can be empirically placed. Originality/value This paper points out the need to incorporate empirical studies to trace the characteristics and evolution of the cooperative relations between local governments and upper tiers. This is particularly relevant if the authors are referring to mechanisms that can vary through time. In the current big and open data era, this empirical process will become easier and more affordable. In this context, local government studies benefit from particular features that improve the operation of this kind of analysis: large “N” configuration (a large number of units to be included) and a reasonable equivalence in concepts and bodies that allows comparability.

scholarly journals Impact of anaesthesia on static and dynamic functional connectivity in mice

2021 ◽  
Author(s):  
Tomokazu Tsurugizawa ◽  
Daisuke Yoshimaru
Keyword(s):  
Functional Connectivity ◽  
Functional Mri ◽  
Resting State ◽  
Low Frequency ◽  
Resting State Fmri ◽  
Brain Network ◽  
List Type ◽  
Thalamic Nuclei ◽  
Dynamic Functional Connectivity ◽  
Subcortical Regions

AbstractA few studies have compared the static functional connectivity between awake and anaesthetized states in rodents by resting-state fMRI. However, impact of anaesthesia on static and dynamic fluctuations in functional connectivity has not been fully understood. Here, we developed a resting-state fMRI protocol to perform awake and anaesthetized functional MRI in the same mice. Static functional connectivity showed a widespread decrease under anaesthesia, such as when under isoflurane or a mixture of isoflurane and medetomidine. Several interhemispheric connections were key connections for anaesthetized condition from awake. Dynamic functional connectivity demonstrates the shift from frequent broad connections across the cortex, the hypothalamus, and the auditory-visual cortex to frequent local connections within the cortex only. Fractional amplitude of low frequency fluctuation in the thalamic nuclei decreased under both anaesthesia. These results indicate that typical anaesthetics for functional MRI alters the spatiotemporal profile of the dynamic brain network in subcortical regions, including the thalamic nuclei and limbic system.HighlightsResting-state fMRI was compared between awake and anaesthetized in the same mice.Anaesthesia induced a widespread decrease of static functional connectivity.Anaesthesia strengthened local connections within the cortex.fALFF in the thalamus was decreased by anaesthesia.

scholarly journals Maternal effects and environmental filtering shape seed fungal communities in oak trees

2019 ◽  
Author(s):  
Tania Fort ◽  
Charlie Pauvert ◽  
Amy E. Zanne ◽  
Otso Ovaskainen ◽  
Thomas Caignard ◽  
...  
Keyword(s):  
Microbial Communities ◽  
Maternal Effects ◽  
Environmental Filtering ◽  
Fungal Communities ◽  
Future Research ◽  
Phenotypic Traits ◽  
List Type ◽  
Elevation Gradients ◽  
Sessile Oak ◽  
Starting Point

SummaryTrees, as foundation species, play a pivotal role in the species interaction networks that constitute forest ecosystems. From the seed stage, they interact with microbial communities that affect their growth, health and fitness. Despite their eco-evolutionary importance, the processes shaping seed microbial communities in natural forests have received little attention.To unravel these processes, we analyzed the microbial communities of seeds collected in populations of sessile oak (Quercus petraea) growing along elevation gradients. We focused on the fungal communities as this group includes seed pathogens. Ecological processes shaping the communities were quantified using joint species distribution models.Fungi were present in all seed tissues, including the embryo. Fungal communities differed significantly among oak populations along the elevation gradients, and among mother trees within the same population. These maternal effects remained significant after seed fall, despite colonization by fungal species on the ground. Associations between tree pathogens and their antagonists were detected in the seeds.Our results demonstrate that both maternal effects and environmental filtering shape seed microbial communities of sessile oak. They provide a starting point for future research aimed at identifying the seed extended phenotypic traits that influence seed dispersal and germination, and seedling survival and growth across environments.

scholarly journals 3D Cell Image Segmentation by Modified Subjective Surface Method

2020 ◽  
Vol 75 (1) ◽  
pp. 147-162
Author(s):  
MarKjoe Olunna Uba ◽  
Karol Mikula ◽  
Zuzana Krivá ◽  
Hanh Nguyen ◽  
Thierry Savy ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Real Data ◽  
Finite Volume Scheme ◽  
3D Image ◽  
Cell Nuclei ◽  
Surface Evolution ◽  
Surface Method ◽  
Segmentation Approach ◽  
Level Set Formulation ◽  
Microscopy Images

AbstractIn this work, we focused on 3D image segmentation where the segmented surface is reconstructed by the use of 3D digital image information and information from thresholded 3D image in a local neighbourhood. To this end, we applied a mathematical model based on the level set formulation and numerical method which is based on the so-called reduced diamond cell approach. The segmentation approach is based on surface evolution governed by a nonlinear PDE, the modified subjective surface equation. This is done by defining the input to the edge detector function as the weighted sum of norm of presmoothed 3D image and norm of presmoothed thresholded 3D image in a local neighbourhood. For the numerical discretization, we used a semi-implicit finite volume scheme. The method was applied to real data representing 3D microscopy images of cell nuclei within the zebrafish pectoral fin.

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