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Brain Connectivity Estimators
Brain connectivity estimators represent patterns of links in the brain. Connectivity can be considered at different levels of the brain's organisation: from neurons, to neural assemblies and brain structures. Brain connectivity involves different concepts such as: neuroanatomical or structural connectivity (pattern of anatomical links), functional connectivity (usually understood as statistical dependencies) and effective connectivity (referring to causal interactions). Neuroanatomical connectivity is inherently difficult to define given the fact that at the microscopic scale of neurons, new synaptic connections or elimination of existing ones are formed dynamically and are largely dependent on the function executed, but may be considered as pathways extending over regions of the brain, which are in accordance with general anatomical knowledge. Diffusion Weighted Imaging (DWI) can be used to provide such information. The distinction between functional and effective connectivit ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Brain
A brain is an organ that serves as the center of the nervous system in all vertebrate and most invertebrate animals. It is located in the head, usually close to the sensory organs for senses such as vision. It is the most complex organ in a vertebrate's body. In a human, the cerebral cortex contains approximately 14–16 billion neurons, and the estimated number of neurons in the cerebellum is 55–70 billion. Each neuron is connected by synapses to several thousand other neurons. These neurons typically communicate with one another by means of long fibers called axons, which carry trains of signal pulses called action potentials to distant parts of the brain or body targeting specific recipient cells. Physiologically, brains exert centralized control over a body's other organs. They act on the rest of the body both by generating patterns of muscle activity and by driving the secretion of chemicals called hormones. This centralized control allows rapid and coordinated responses ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Histogram
A histogram is an approximate representation of the frequency distribution, distribution of numerical data. The term was first introduced by Karl Pearson. To construct a histogram, the first step is to "Data binning, bin" (or "Data binning, bucket") the range of values—that is, divide the entire range of values into a series of intervals—and then count how many values fall into each interval. The bins are usually specified as consecutive, non-overlapping interval (mathematics), intervals of a variable. The bins (intervals) must be adjacent and are often (but not required to be) of equal size. If the bins are of equal size, a bar is drawn over the bin with height proportional to the Frequency (statistics), frequency—the number of cases in each bin. A histogram may also be normalization (statistics), normalized to display "relative" frequencies showing the proportion of cases that fall into each of several Categorization, categories, with the sum of the heights equaling 1. ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Sleep Stage
Sleep is a sedentary state of mind and body. It is characterized by altered consciousness, relatively inhibited Perception, sensory activity, reduced muscle activity and reduced interactions with surroundings. It is distinguished from wakefulness by a decreased ability to react to stimulus (physiology), stimuli, but more reactive than a coma or disorders of consciousness, with sleep displaying different, active brain patterns. Sleep occurs in sleep cycle, repeating periods, in which the body alternates between two distinct modes: REM sleep and non-rapid eye movement sleep, non-REM sleep. Although REM stands for "rapid eye movement", this mode of sleep has many other aspects, including virtual paralysis of the body. dream, Dreams are a succession of images, ideas, emotions, and sensations that usually occur involuntarily in the mind during certain stages of sleep. During sleep, most of the human body, body's systems are in an anabolic state, helping to restore the Immunity (medi ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Epilepsy
Epilepsy is a group of non-communicable neurological disorders characterized by recurrent epileptic seizures. Epileptic seizures can vary from brief and nearly undetectable periods to long periods of vigorous shaking due to abnormal electrical activity in the brain. These episodes can result in physical injuries, either directly such as broken bones or through causing accidents. In epilepsy, seizures tend to recur and may have no immediate underlying cause. Isolated seizures that are provoked by a specific cause such as poisoning are not deemed to represent epilepsy. People with epilepsy may be treated differently in various areas of the world and experience varying degrees of social stigma due to the alarming nature of their symptoms. The underlying mechanism of epileptic seizures is excessive and abnormal neuronal activity in the cortex of the brain which can be observed in the electroencephalogram (EEG) of an individual. The reason this occurs in most cases of epilepsy ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Brain Atlas
A brain atlas is composed of serial sections along different anatomical planes of the healthy or diseased developing or adult animal or human brain where each relevant brain structure is assigned a number of coordinates to define its outline or volume. Brain atlases are contiguous, comprehensive results of visual brain mapping and may include anatomical, genetic or functional features. A functional brain atlas is made up of N regions of interest, where these regions are typically defined as spatially contiguous and functionally coherent patches of gray matter. In most atlases, the three dimensions are: latero-lateral (x), dorso-ventral (y) and rostro-caudal (z). The possible sections are * coronal * sagittal * transverse Surface maps are sometimes used in addition to the 3D serial section maps Besides the human brain, brain atlases exist for the brains of the mouse, rhesus macaques, ''Drosophila'', pig and others. Notable examples include the Allen Brain Atlas, BrainMaps, ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Bootstrapping (statistics)
Bootstrapping is any test or metric that uses random sampling with replacement (e.g. mimicking the sampling process), and falls under the broader class of resampling methods. Bootstrapping assigns measures of accuracy (bias, variance, confidence intervals, prediction error, etc.) to sample estimates.software This technique allows estimation of the sampling distribution of almost any statistic using random sampling methods. Bootstrapping estimates the properties of an estimand (such as its variance) by measuring those properties when sampling from an approximating distribution. One standard choice for an approximating distribution is the [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Explained Variation
In statistics, explained variation measures the proportion to which a mathematical model accounts for the variation (dispersion) of a given data set. Often, variation is quantified as variance; then, the more specific term explained variance can be used. The complementary part of the total variation is called unexplained or residual variation. Definition in terms of information gain Information gain by better modelling Following Kent (1983), we use the Fraser information (Fraser 1965) :F(\theta) = \int \textrmr\,g(r)\,\ln f(r;\theta) where g(r) is the probability density of a random variable R\,, and f(r;\theta)\, with \theta\in\Theta_i (i=0,1\,) are two families of parametric models. Model family 0 is the simpler one, with a restricted parameter space \Theta_0\subset\Theta_1. Parameters are determined by maximum likelihood estimation, :\theta_i = \operatorname_ F(\theta). The information gain of model 1 over model 0 is written as :\Gamma(\theta_1:\theta_0) = 2 F(\theta_ ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Akaike Information Criterion
The Akaike information criterion (AIC) is an estimator of prediction error and thereby relative quality of statistical models for a given set of data. Given a collection of models for the data, AIC estimates the quality of each model, relative to each of the other models. Thus, AIC provides a means for model selection. AIC is founded on information theory. When a statistical model is used to represent the process that generated the data, the representation will almost never be exact; so some information will be lost by using the model to represent the process. AIC estimates the relative amount of information lost by a given model: the less information a model loses, the higher the quality of that model. In estimating the amount of information lost by a model, AIC deals with the trade-off between the goodness of fit of the model and the simplicity of the model. In other words, AIC deals with both the risk of overfitting and the risk of underfitting. The Akaike information criterion ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Autoregressive Model
In statistics, econometrics and signal processing, an autoregressive (AR) model is a representation of a type of random process; as such, it is used to describe certain time-varying processes in nature, economics, etc. The autoregressive model specifies that the output variable depends linearly on its own previous values and on a stochastic term (an imperfectly predictable term); thus the model is in the form of a stochastic difference equation (or recurrence relation which should not be confused with differential equation). Together with the moving-average (MA) model, it is a special case and key component of the more general autoregressive–moving-average (ARMA) and autoregressive integrated moving average (ARIMA) models of time series, which have a more complicated stochastic structure; it is also a special case of the vector autoregressive model (VAR), which consists of a system of more than one interlocking stochastic difference equation in more than one evolving random vari ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Multivariate Analysis
Multivariate statistics is a subdivision of statistics encompassing the simultaneous observation and analysis of more than one outcome variable. Multivariate statistics concerns understanding the different aims and background of each of the different forms of multivariate analysis, and how they relate to each other. The practical application of multivariate statistics to a particular problem may involve several types of univariate and multivariate analyses in order to understand the relationships between variables and their relevance to the problem being studied. In addition, multivariate statistics is concerned with multivariate probability distributions, in terms of both :*how these can be used to represent the distributions of observed data; :*how they can be used as part of statistical inference, particularly where several different quantities are of interest to the same analysis. Certain types of problems involving multivariate data, for example simple linear regression an ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Granger Causality
The Granger causality test is a statistical hypothesis test for determining whether one time series is useful in forecasting another, first proposed in 1969. Ordinarily, regressions reflect "mere" correlations, but Clive Granger argued that causality in economics could be tested for by measuring the ability to predict the future values of a time series using prior values of another time series. Since the question of "true causality" is deeply philosophical, and because of the post hoc ergo propter hoc fallacy of assuming that one thing preceding another can be used as a proof of causation, econometricians assert that the Granger test finds only "predictive causality". Using the term "causality" alone is a misnomer, as Granger-causality is better described as "precedence", or, as Granger himself later claimed in 1977, "temporally related". Rather than testing whether ''X'' ''causes'' Y, the Granger causality tests whether X ''forecasts'' ''Y.'' A time series ''X'' is said to Gra ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Clive Granger
Sir Clive William John Granger (; 4 September 1934 – 27 May 2009) was a British econometrician known for his contributions to nonlinear time series analysis. He taught in Britain, at the University of Nottingham and in the United States, at the University of California, San Diego. Granger was awarded the Nobel Memorial Prize in Economic Sciences in 2003 in recognition of the contributions that he and his co-winner, Robert F. Engle, had made to the analysis of time series data. This work fundamentally changed the way in which economists analyse financial and macroeconomic data. Biography Early life Clive Granger was born in 1934 in Swansea, south Wales, United Kingdom, to Edward John Granger and Evelyn Granger. The next year his parents moved to Lincoln. During World War II Granger and his mother moved to Cambridge because Edward joined the Royal Air Force and deployed to North Africa. Here they stayed first with Evelyn's mother, then later Edward's parents, while Clive bega ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
