• MeSH
• Models, Neurological MeSH
• Neurosciences instrumentation   MeSH
• Computer Simulation MeSH
• Computing Methodologies MeSH

• Conspectus
• Patologie. Klinická medicína
• NML Fields
• neurologie
• neurovědy
• NML Publication type
• elektronické časopisy

• MeSH
• Models, Biological MeSH
• Drug Design MeSH

• Conspectus
• Farmacie. Farmakologie
• NML Fields
• farmacie a farmakologie
• NML Publication type
• elektronické časopisy

• MeSH
• Models, Neurological MeSH
• Computational Biology MeSH

• Conspectus
• Fyziologie člověka a srovnávací fyziologie
• NML Fields
• neurovědy
• NML Publication type
• elektronické časopisy

• MeSH
• Models, Chemical MeSH
• Chemistry methods   MeSH
• Computer Simulation MeSH

• Conspectus
• Chemie. Mineralogické vědy
• NML Fields
• chemie, klinická chemie
• NML Publication type
• elektronické časopisy

• MeSH
• Models, Biological MeSH
• Models, Theoretical MeSH

• Conspectus
• Biologické vědy
• NML Fields
• lékařství
• NML Publication type
• elektronické časopisy

Modern imaging methods allow a non-invasive assessment of both structural and functional brain connectivity. This has lead to the identification of disease-related alterations affecting functional connectivity. The mechanism of how such alterations in functional connectivity arise in a structured network of interacting neural populations is as yet poorly understood. Here we use a modeling approach to explore the way in which this can arise and to highlight the important role that local population dynamics can have in shaping emergent spatial functional connectivity patterns. The local dynamics for a neural population is taken to be of the Wilson-Cowan type, whilst the structural connectivity patterns used, describing long-range anatomical connections, cover both realistic scenarios (from the CoComac database) and idealized ones that allow for more detailed theoretical study. We have calculated graph-theoretic measures of functional network topology from numerical simulations of model networks. The effect of the form of local dynamics on the observed network state is quantified by examining the correlation between structural and functional connectivity. We document a profound and systematic dependence of the simulated functional connectivity patterns on the parameters controlling the dynamics. Importantly, we show that a weakly coupled oscillator theory explaining these correlations and their variation across parameter space can be developed. This theoretical development provides a novel way to characterize the mechanisms for the breakdown of functional connectivity in diseases through changes in local dynamics.

• MeSH
• Humans MeSH
• Models, Neurological MeSH
• Brain physiology   MeSH
• Nerve Net physiology   MeSH
• Computational Biology MeSH
• Check Tag
• Humans MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH

• MeSH
• Electrophysiology MeSH
• Computers utilization   MeSH
• Heart Rate MeSH
• Models, Theoretical MeSH

• MeSH
• Information Systems MeSH
• Medical Informatics Computing MeSH
• Molecular Biology MeSH
• Computer Simulation MeSH
• Computational Biology MeSH
• Publication type
• Periodical MeSH

• Conspectus
• Biologické vědy
• NML Fields
• biologie
• lékařská informatika

• MeSH
• Action Potentials MeSH
• Dendrites physiology   MeSH
• Ion Channels MeSH
• Humans MeSH
• Neurophysiology MeSH
• Neurons physiology   MeSH
• Computer Simulation MeSH
• Software MeSH
• Check Tag
• Humans MeSH
• Publication type
• Review MeSH

Co-milling is an effective technique for improving dissolution rate limited absorption characteristics of poorly water-soluble drugs. However, there is a scarcity of models available to forecast the magnitude of dissolution rate improvement caused by co-milling. Therefore, this study endeavoured to quantitatively predict the increase in dissolution by co-milling based on drug properties. Using a biorelevant dissolution setup, a series of 29 structurally diverse and crystalline drugs were screened in co-milled and physically blended mixtures with Polyvinylpyrrolidone K25. Co-Milling Dissolution Ratios after 15 min (COMDR15 min) and 60 min (COMDR60 min) drug release were predicted by variable selection in the framework of a partial least squares (PLS) regression. The model forecasts the COMDR15 min (R2 = 0.82 and Q2 = 0.77) and COMDR60 min (R2 = 0.87 and Q2 = 0.84) with small differences in root mean square errors of training and test sets by selecting four drug properties. Based on three of these selected variables, applicable multiple linear regression equations were developed with a high predictive power of R2 = 0.83 (COMDR15 min) and R2 = 0.84 (COMDR60 min). The most influential predictor variable was the median drug particle size before milling, followed by the calculated drug logD6.5 value, the calculated molecular descriptor Kappa 3 and the apparent solubility of drugs after 24 h dissolution. The study demonstrates the feasibility of forecasting the dissolution rate improvements of poorly water-solube drugs through co-milling. These models can be applied as computational tools to guide formulation in early stage development.

• MeSH
• Pharmaceutical Preparations chemistry   MeSH
• Least-Squares Analysis MeSH
• Computer Simulation MeSH
• Povidone chemistry   MeSH
• Drug Compounding * methods   MeSH
• Solubility * MeSH
• Drug Liberation * MeSH
• Publication type
• Journal Article MeSH