The Experts below are selected from a list of 27 Experts worldwide ranked by ideXlab platform
Nora D Volkow - One of the best experts on this subject based on the ideXlab platform.
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temporal changes in local functional connectivity density reflect the temporal variability of the amplitude of low frequency fluctuations in gray matter
PLOS ONE, 2016Co-Authors: Dardo Tomasi, Ehsan Shokrikojori, Nora D VolkowAbstract:Data-driven functional connectivity density (FCD) mapping is being increasingly utilized to assess brain connectomics at rest in the healthy brain and its disruption in neuropsychiatric diseases with the underlying assumption that the spatiotemporal hub distribution is stationary. However, recent studies show that functional connectivity is highly dynamic. Here we study the temporal variability of the local FCD (lFCD) at high spatiotemporal resolution (2-mm isotropic; 0.72s) using a sliding-window approach and ‘resting-state’ datasets from 40 healthy subjects collected under the Human Connectome Project. Prominent functional connectivity hubs in visual and posterior parietal cortices had pronounced temporal changes in local FCD. These dynamic patterns in the strength of the lFCD hubs occurred in cortical gray matter with high sensitivity (up to 85%) and specificity (> 85%) and showed high reproducibility (up to 72%) across sessions and high test-retest reliability (ICC(3,1) > 0.5). The temporal changes in lFCD predominantly occurred in medial occipitoparietal regions and were proportional to the strength of the connectivity hubs. The temporal variability of the lFCD was associated with the amplitude of the low frequency fluctuations (ALFF). Pure Randomness did not account for the probability distribution of lFCD. Shannon entropy increased in proportion to the strength of the lFCD hubs suggesting high average flow of information per unit of time in the lFCD hubs, particularly in medial occipitoparietal regions. Thus, the higher dynamic range of the lFCD hubs is consistent with their role in the complex orchestration of interacting brain networks.
Mohammad Bagher Ahmadi - One of the best experts on this subject based on the ideXlab platform.
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opposition versus Randomness in binary spaces
Applied Soft Computing, 2015Co-Authors: Z Seif, Mohammad Bagher AhmadiAbstract:Evolutionary algorithms start with an initial population vector, which is randomly generated when no preliminary knowledge about the solution is available. Recently, it has been claimed that in solving continuous domain optimization problems, the simultaneous consideration of Randomness and opposition is more effective than Pure Randomness. Here it is mathematically proven that this scheme, called opposition-based learning, also does well in binary spaces. The proposed binary opposition-based scheme can be embedded inside many binary population-based algorithms. We applied it to accelerate the convergence rate of Binary Gravitational Search Algorithm (BGSA) as an application. The experimental results and mathematical proofs confirm each other. We introduce the concept of opposition-based learning in binary spaces.It is proven that utilizing random numbers and their opposite is beneficial in evolutionary algorithms.Opposite numbers are applied to accelerate the convergence rate of Binary Gravitational Search Algorithm (BGSA).The results show that OBGSA possesses superior performance in accuracy as compared to the BGSA. Evolutionary algorithms start with an initial population vector, which is randomly generated when no preliminary knowledge about the solution is available. Recently, it has been claimed that in solving continuous domain optimization problems, the simultaneous consideration of Randomness and opposition is more effective than Pure Randomness. In this paper it is mathematically proven that this scheme, called opposition-based learning, also does well in binary spaces. The proposed binary opposition-based scheme can be embedded inside many binary population-based algorithms. We applied it to accelerate the convergence rate of binary gravitational search algorithm (BGSA) as an application. The experimental results and mathematical proofs confirm each other.
Don Monroe - One of the best experts on this subject based on the ideXlab platform.
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Pure Randomness extracted from two poor sources
Communications of The ACM, 2016Co-Authors: Don MonroeAbstract:Developments from several disparate areas of computer science provide "a huge jump, both technically and also quantitatively."
Z Seif - One of the best experts on this subject based on the ideXlab platform.
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opposition versus Randomness in binary spaces
Applied Soft Computing, 2015Co-Authors: Z Seif, Mohammad Bagher AhmadiAbstract:Evolutionary algorithms start with an initial population vector, which is randomly generated when no preliminary knowledge about the solution is available. Recently, it has been claimed that in solving continuous domain optimization problems, the simultaneous consideration of Randomness and opposition is more effective than Pure Randomness. Here it is mathematically proven that this scheme, called opposition-based learning, also does well in binary spaces. The proposed binary opposition-based scheme can be embedded inside many binary population-based algorithms. We applied it to accelerate the convergence rate of Binary Gravitational Search Algorithm (BGSA) as an application. The experimental results and mathematical proofs confirm each other. We introduce the concept of opposition-based learning in binary spaces.It is proven that utilizing random numbers and their opposite is beneficial in evolutionary algorithms.Opposite numbers are applied to accelerate the convergence rate of Binary Gravitational Search Algorithm (BGSA).The results show that OBGSA possesses superior performance in accuracy as compared to the BGSA. Evolutionary algorithms start with an initial population vector, which is randomly generated when no preliminary knowledge about the solution is available. Recently, it has been claimed that in solving continuous domain optimization problems, the simultaneous consideration of Randomness and opposition is more effective than Pure Randomness. In this paper it is mathematically proven that this scheme, called opposition-based learning, also does well in binary spaces. The proposed binary opposition-based scheme can be embedded inside many binary population-based algorithms. We applied it to accelerate the convergence rate of binary gravitational search algorithm (BGSA) as an application. The experimental results and mathematical proofs confirm each other.
J M Behnke - One of the best experts on this subject based on the ideXlab platform.
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structure in parasite component communities in wild rodents predictability stability associations and interactions or Pure Randomness
Parasitology, 2008Co-Authors: J M BehnkeAbstract:Experimental data establish that interactions exist between species of intestinal helminths during concurrent infections in rodents, the strongest effects being mediated through the host’s immune responses. Detecting immune-mediated relationships in wild rodent populations has been fraught with problems and published data do not support a major role for interactions in structuring helminth communities. Helminths in wild rodents show predictable patterns of seasonal, host age-dependent and spatial variation in species richness and in abundance of core species. When these are controlled for, patterns of co-infection compatible with synergistic interactions can be demonstrated. At least one of these, the positive relationship between Heligmosomoides polygyrus and species richness of other helminths has been demonstrated in three totally independent data-sets. Collectively, they explain only a small percentage of the variance/deviance in abundance data and at this level are unlikely to play a major role in structuring helminth communities, although they may be important in the more heavily infected wood mice. Current worm burdens underestimate the possibility that earlier interactions through the immune system have taken place, and therefore interactions may have a greater role to play than is immediately evident from current worm burdens. Longitudinal studies are proposed to resolve this issue.
