Oct 31, 2011 - Statistics are needed to infer information from the data. However ... data analysis is done for the magnitude data and not for the phase data.
R (http://www.r-project.org/) is one of the most exciting free data mining ... Its
popularity is completely justified (see Kdnuggets Polls – Data Mining/ Analytic
Tools.
Jul 20, 2010 - Statistical Analysis Programs in R for FMRI Data. Gang Chen, Ziad S. Saad, and Robert W. Cox. Scientific and Statistical Computing Core.
Mar 20, 2015 - This study was supported by UNITAID; a PROSPER fellowship to O. Keiser and a PRODOC PhD grant for N. Blaser from the Swiss National ...
to generalized spherical laws, giving new classes of probability distributions on .... It is relatively easy to add new types of terms to this list if other contours are of ...
Apr 29, 2013 - Abstract stpp is an R package for analyzing, simulating and displaying ... Generic methods for the analysis of spatio-temporal point processes ...
mortality data and the fitting and analysis of the given log-linear modelling ... ilc is a publically available R package for the analysis of age-period mortality data ...
The R Package fechner. Examples. Morse Code Data. Regular Minimality/Maximality. Fechnerian Scaling Analysis. Plotting and Summarizing. Conclusion ...
Nov 4, 2012 - The R package MixSim is a new tool that allows simulating mixtures of .... We refer the reader to their paper for the detailed analysis of the.
An R package for analyzing truncated data. Carla Moreira1â, Jacobo de UËna-Ãlvarez1 and Rosa M. Crujeiras2. 1 Department of Statistics and OR, University of ...
traction of data from a graph whose source is not available. The package digitize, that I present here, allows a ... gra
and select mixed causal-noncausal autoregressive models, possibly including exogenous regressors. ... The existing autoregressive moving average (ARMA).
Oct 27, 2012 - incorporate the two main references of functional data analysis: The R package fda and the functions implemented by Ferraty and Vieu (2006).
Andreou, Ghysels, and Kourtellos (2011) who review more extensively some of the material summarized in this ... (2013) s
Jul 29, 2011 - used for mapping land cover, tracking land use change, and even .... The conversion coefficients are available in the metadata accom- panying ...
The software comes in the form of an R package. R (R Core .... warnings: a structured list where are specified information the user should take into accounts.
We introduce the R package npmv that performs nonparametric inference for the ... analysis of variance), itself available through the standard R package stats ( ...
Nov 22, 2016 - The R package (R Core Team 2013) genMOSS is specifically ... MOSS. Suppose that V is a set of classification criteria. Let Y = Xγ,γ â V be a ..... Wu MC, Kraft P, Epstein MP, Taylor DM, Chanock SJ, Hunter DJ, Lin X (2010).
calculated for overall tests (permutation tests and F approximations), and, using .... each condition, the ni subjects (experimental units), on which the p-variate ...
the same household type. Weights are rounded to the nearest integer value. ... The number of households in the synthetic population should then match the ...
Oct 27, 2012 - fda.usc: Functional Data Analysis in R has been evaluated in ... Functional data: Definition and descript
The R package sdcMicro serves as an easy-to-handle, object-oriented S4 class ... increasing since economic analysis or empirical analysis of the society is often ...
Jul 29, 2011 - 6 landsat: Analyzing Remote Sensing Data in R. 5. Radiometric .... around the cloud edge (buffer) were adequate for the test data once ...
Summary. Simulating fMRI data: the R package. neuRosim ... Department of Data Analysis .... in-house developed software routines, often not available for the.
neuRosim
Outline Motivation The need for simulation The need for validated simulation The need for software
Simulating fMRI data: the R package neuRosim
Features Goals What can you do with neuRosim?
Marijke Welvaert
How is neuRosim organized?
Example Setting up the design
Department of Data Analysis Ghent University
Simulating the data Exporting and analyzing the data
Summary
Berlin Workshop on Statistics and Neuroimaging 2011
Outline neuRosim
1 Motivation Outline Motivation The need for simulation
The need for simulation The need for validated simulation The need for software
The need for validated simulation The need for software
Features Goals What can you do with neuRosim? How is neuRosim organized?
Example Setting up the design Simulating the data Exporting and analyzing the data
Summary
2 Features
Goals What can you do with neuRosim? How is neuRosim organized? 3 Example
Setting up the design Simulating the data Exporting and analyzing the data 4 Summary
Knowing the ground truth in MRI neuRosim
Outline Motivation The need for simulation The need for validated simulation The need for software
Features Goals What can you do with neuRosim? How is neuRosim organized?
Example Setting up the design Simulating the data Exporting and analyzing the data
Summary
Knowing the ground truth in fMRI neuRosim
Outline Motivation The need for simulation The need for validated simulation The need for software
Features Goals What can you do with neuRosim? How is neuRosim organized?
Example Setting up the design Simulating the data Exporting and analyzing the data
Summary
Reflection in the literature neuRosim
Web of Science publications/year Outline
3000
Motivation The need for simulation
How is neuRosim organized?
Example Setting up the design
1500
What can you do with neuRosim?
Number of publications
Goals
1000
The need for software
Features
2000
2500
The need for validated simulation
Exporting and analyzing the data
500
Simulating the data
0
Summary
1995
2000 Year of publication
2005
2010
fMRI data components neuRosim
Activation Outline
experimentally induced
Motivation
spontaneous
The need for simulation The need for validated simulation The need for software
Features Goals What can you do with neuRosim? How is neuRosim organized?
Example Setting up the design
Known artefacts B0 inhomogeneities low-frequency drift Noise system movement
Simulating the data Exporting and analyzing the data
Summary
physiological task-related ... Spatial and temporal correlations
Typical fMRI simulation studies neuRosim
Outline
1
Motivation
known activation combined with real noise e.g. Bianciardi et al. (2004), Lange (1999), Weibull et al. (2008)
The need for simulation The need for validated simulation The need for software
2
white time series known activation combined with white noise i.i.d or AR(1) Gaussian distribution e.g. Lei et al. (2010), Lin et al. (2010), Purdon & Weisskoff (1998), Smith et al. (2011)
Features Goals What can you do with neuRosim? How is neuRosim organized?
Example
hybrid simulation
3
other
Setting up the design Simulating the data Exporting and analyzing the data
Summary
model-based simulation, Bloch equations, noise based on residuals of real data e.g. Drobnjak et al. (2006), Havlicek et al. (2010), Logan & Rowe (2004), Ramsey et al. (2010)
Problems - Discrepancies - Shortcomings neuRosim
Outline Motivation The need for simulation
real noise may contain undesired activity
The need for validated simulation
simulated noise = system noise
The need for software
Features Goals What can you do with neuRosim? How is neuRosim organized?
Example Setting up the design Simulating the data Exporting and analyzing the data
Summary
beware of the phrase: “. . . simulations under realistic noise conditions. . . ” total ignorance of spatial context no stand-alone simulations often missing (crucial) information while reporting simulation studies
The choice of simulation model matters! neuRosim
Outline Low CNR
0.95
0.30 0.25
0.90
The need for software
Features
Power
0.20
0.75 0.70
Setting up the design
0.15
How is neuRosim organized?
Example
0.10
What can you do with neuRosim?
Power
Goals
0.85
The need for validated simulation
0.80
The need for simulation
1.00
High CNR
0.35
Motivation
White noise AR(1) noise Phys. noise Real noise
0.65
Exporting and analyzing the data
0.05
Simulating the data
0.02
Summary
0.04
0.06 Contrast−to−noise ratio
0.08
0.10
0.20
0.25
0.30
Contrast−to−noise ratio
0.35
Towards a convergence of simulation methods neuRosim
Outline Motivation The need for simulation
in-house developed software routines, often not available for the community
The need for validated simulation
language barrier
The need for software
no widespread software packages
Features Goals What can you do with neuRosim? How is neuRosim organized?
Example Setting up the design Simulating the data Exporting and analyzing the data
Outline Motivation The need for simulation The need for validated simulation The need for software
Features
to provide a tool for simulating fMRI data to be a base for more validated simulation studies
Goals What can you do with neuRosim?
to make simulation available for less technical researchers
How is neuRosim organized?
to allow maximum flexibility for the useRs
Example Setting up the design Simulating the data Exporting and analyzing the data
Summary
What can you do with neuRosim? neuRosim
Outline Motivation The need for simulation The need for validated simulation
specify your experimental design based on stimulus onsets and durations
The need for software
Features Goals What can you do with neuRosim? How is neuRosim organized?
Example Setting up the design Simulating the data Exporting and analyzing the data
Summary
specify activated regions using an xyz-coordinate system simulate BOLD activation with the choice of different models simulate resting state activation (still under development) simulate fMRI noise originating from different noise sources generate fMRI data from 1D time series to 4D volume data
Low-level functions neuRosim
Outline Motivation The need for simulation
Building blocks for advanced useRs who want in-depth control over their simulation data
The need for validated simulation The need for software
Features Goals What can you do with neuRosim? How is neuRosim organized?
Example Setting up the design Simulating the data Exporting and analyzing the data
Summary
canonicalHRF() gammaHRF() balloon()
High-level functions neuRosim
Outline Motivation The need for simulation The need for validated simulation
Direct simulation of fMRI data
The need for software
Features
Preparation functions
Simulation functions
simprepTemporal() simprepSpatial()
simTSfmri() simVOLfmri() simRestingStatefmri()
Goals What can you do with neuRosim? How is neuRosim organized?
Example Setting up the design Simulating the data Exporting and analyzing the data
Summary
Real dataset neuRosim
Consider the data from a repetition priming experiment performed using event-related fMRI (Henson et al., 2002). Outline Motivation The need for simulation The need for validated simulation The need for software
Features Goals What can you do with neuRosim? How is neuRosim organized?
Example Setting up the design Simulating the data Exporting and analyzing the data
Summary
2 × 2 factorial design famous vs non-famous faces effect of repetition
Setting up the design (1) neuRosim
Temporal Parameters Outline Motivation The need for simulation The need for validated simulation The need for software
Features Goals What can you do with neuRosim? How is neuRosim organized?
Example Setting up the design Simulating the data Exporting and analyzing the data
