Terminologies: AnalysisTechnique library
Related schema specification: AnalysisTechnique
4PointsCongruentSetsAlignment
metadata sheet
- @context:
@vocab: <https://openminds.om-i.org/props/>
- @id:
https://openminds.om-i.org/instances/analysisTechnique/4PointsCongruentSetsAlignment
- @type:
- definition:
4-points congruent sets alignment is a fast and robust alignment technique for 3D point sets without pre-filtering or denoising the data, even if the data are noisy and/or contaminated with outliers ([Aiger et al., 2008](https://doi.org/10.1145/1360612.1360684)).
- name:
4-points congruent sets alignment
GrubbsTest
metadata sheet
- @context:
@vocab: <https://openminds.om-i.org/props/>
- @id:
https://openminds.om-i.org/instances/analysisTechnique/GrubbsTest
- @type:
- definition:
The ‘Grubbs test’ is a statistical test, first published by [Grubbs (1950)](https://doi.org/10.1214/aoms/1177729885), used to detect outliers in univariate data that are assumed to come from a normally distributed population. [adapted from [wikipedia](https://en.wikipedia.org/wiki/Grubbs%27s_test)]
- name:
Grubbs’ test
HilbertTransform
metadata sheet
- @context:
@vocab: <https://openminds.om-i.org/props/>
- @id:
https://openminds.om-i.org/instances/analysisTechnique/HilbertTransform
- @type:
- definition:
A convolution technique for a function u(t) of a real variable with the function h(t) = 1/πt, known as the Cauchy kernel, producing a function of a real variable H(u)(t). [adapted from [Wikipedia](https://en.wikipedia.org/wiki/Hilbert_transform)]
- name:
Hilbert transform
ICABasedDenoisingTechnique
metadata sheet
- @context:
@vocab: <https://openminds.om-i.org/props/>
- @id:
https://openminds.om-i.org/instances/analysisTechnique/ICABasedDenoisingTechnique
- @type:
- definition:
An ‘ICA based denoising technique’ removes independent components from input data to reduce noise while preserving the features of interest in the data.
- name:
ICA based denoising technique
Isomap
metadata sheet
- @context:
@vocab: <https://openminds.om-i.org/props/>
- @id:
https://openminds.om-i.org/instances/analysisTechnique/Isomap
- @type:
- definition:
A manifold learning algorithm used for computing a quasi-isometric, low-dimensional embedding of a set of high-dimensional data points to perform a nonlinear dimensionality reduction. [adapted from [Wikipedia](https://en.wikipedia.org/wiki/Isomap)]
- name:
Isomap
MannWhitneyUTest
metadata sheet
- @context:
@vocab: <https://openminds.om-i.org/props/>
- @id:
https://openminds.om-i.org/instances/analysisTechnique/MannWhitneyUTest
- @type:
- definition:
The ‘Mann–Whitney U test’ is a nonparametric test of the null hypothesis that, for randomly selected values X and Y from two populations, the probability of X being greater than Y is equal to the probability of Y being greater than X. [adapted from [wikipedia](https://en.wikipedia.org/wiki/Mann%E2%80%93Whitney_U_test)]
- name:
Mann–Whitney U test
ShapiroWilkTest
metadata sheet
- @context:
@vocab: <https://openminds.om-i.org/props/>
- @id:
https://openminds.om-i.org/instances/analysisTechnique/ShapiroWilkTest
- @type:
- definition:
The ‘Shapiro–Wilk test’ is a statistical test of normality of a complete sample, first described by [Shapiro and Wilk (1965)](https://doi.org/10.1093/biomet/52.3-4.591). [adapted from [wikipedia](https://en.wikipedia.org/wiki/Shapiro%E2%80%93Wilk_test)]
- name:
Shapiro-Wilk test
SpearmansRankOrderCorrelation
metadata sheet
- @context:
@vocab: <https://openminds.om-i.org/props/>
- @id:
https://openminds.om-i.org/instances/analysisTechnique/SpearmansRankOrderCorrelation
- @type:
- definition:
The ‘Spearman’s rank-order correlation’ is the nonparametric version of the Pearson product-moment correlation measuring the strength and direction of association between a set of two ranked variables. [adapted from [Laerd.com](https://statistics.laerd.com/statistical-guides/spearmans-rank-order-correlation-statistical-guide.php)]
- name:
Spearman’s rank-order correlation
WardClustering
metadata sheet
- @context:
@vocab: <https://openminds.om-i.org/props/>
- @id:
https://openminds.om-i.org/instances/analysisTechnique/WardClustering
- @type:
- definition:
‘Ward clustering’ is a general agglomerative hierarchical clustering procedure, where the criterion for choosing the pair of clusters to merge at each step is based on the optimal value of an objective function (typically aiming to minimize the total within-cluster variance). [adapted from [Wikipedia](https://en.wikipedia.org/wiki/Ward%27s_method)]
- name:
Ward clustering
activationLikelihoodEstimation
metadata sheet
- @context:
@vocab: <https://openminds.om-i.org/props/>
- @id:
https://openminds.om-i.org/instances/analysisTechnique/activationLikelihoodEstimation
- @type:
- definition:
An ‘activation likelihood estimation’ is a coordinate-based meta-analysis of neuroimaging data that determines the above-chance convergence of activation probabilities between experiments (i.e., not between foci). [adapted from [Eickhoff et al., 2011](https://dx.doi.org/10.1016%2Fj.neuroimage.2011.09.017)]
- name:
activation likelihood estimation
affineImageRegistration
metadata sheet
- @context:
@vocab: <https://openminds.om-i.org/props/>
- @id:
https://openminds.om-i.org/instances/analysisTechnique/affineImageRegistration
- @type:
- definition:
A ‘affine image registration’ is a process of bringing a set of images into the same coordinate system using affine transformation.
- name:
affine image registration
affineTransformation
metadata sheet
- @context:
@vocab: <https://openminds.om-i.org/props/>
- @id:
https://openminds.om-i.org/instances/analysisTechnique/affineTransformation
- @type:
- definition:
An ‘affine transformation’ is a specific linear transformation using combinations of rotations, translations, reflections, scaling and shearing to map coordinates between two coordinate spaces.
- name:
affine transformation
anatomicalDelineationTechnique
metadata sheet
- @context:
@vocab: <https://openminds.om-i.org/props/>
- @id:
https://openminds.om-i.org/instances/analysisTechnique/anatomicalDelineationTechnique
- @type:
- name:
anatomical delineation technique
averageLinkageClustering
metadata sheet
- @context:
@vocab: <https://openminds.om-i.org/props/>
- @id:
https://openminds.om-i.org/instances/analysisTechnique/averageLinkageClustering
- @type:
- name:
average linkage clustering
biasFieldCorrection
metadata sheet
- @context:
@vocab: <https://openminds.om-i.org/props/>
- @id:
https://openminds.om-i.org/instances/analysisTechnique/biasFieldCorrection
- @type:
- definition:
A ‘bias field correction’ is a mathematical technique to remove a corrupting, low frequency signal from magnetic resonance images. This bias field signal is typically caused by inhomogeneities in the magnetic fields of the magnetic resonance imaging machine.
- name:
bias field correction
bootstrapAggregating
metadata sheet
- @context:
@vocab: <https://openminds.om-i.org/props/>
- @id:
https://openminds.om-i.org/instances/analysisTechnique/bootstrapAggregating
- @type:
- definition:
A specialized machine learning ensemble meta-algorithm designed to improve the stability and accuracy of machine learning algorithms used in statistical classification and regression. [adapted from [Wikipedia](https://en.wikipedia.org/wiki/Bootstrap_aggregating)]
- name:
bootstrap aggregating
bootstrapping
metadata sheet
- @context:
@vocab: <https://openminds.om-i.org/props/>
- @id:
https://openminds.om-i.org/instances/analysisTechnique/bootstrapping
- @type:
- name:
bootstrapping
boundaryBasedRegistration
metadata sheet
- @context:
@vocab: <https://openminds.om-i.org/props/>
- @id:
https://openminds.om-i.org/instances/analysisTechnique/boundaryBasedRegistration
- @type:
- definition:
The term ‘boundary-based registration’ refers to feature based image registration methods which utilize a boundary which can be identified in the source and target image.
- name:
boundary-based registration
clusterAnalysis
metadata sheet
- @context:
@vocab: <https://openminds.om-i.org/props/>
- @id:
https://openminds.om-i.org/instances/analysisTechnique/clusterAnalysis
- @type:
- name:
cluster analysis
combinedVolumeSurfaceRegistration
metadata sheet
- @context:
@vocab: <https://openminds.om-i.org/props/>
- @id:
https://openminds.om-i.org/instances/analysisTechnique/combinedVolumeSurfaceRegistration
- @type:
- definition:
The term ‘combined volume-surface registration’ refers to an image registration framework which utilizes information from the brain surface and the brain volume to perform the registration (cf. [Postelnicu et al. (2009)](https://doi.org/10.1109/TMI.2008.2004426)).
- name:
combined volume–surface registration
communicationProfiling
metadata sheet
- @context:
@vocab: <https://openminds.om-i.org/props/>
- @id:
https://openminds.om-i.org/instances/analysisTechnique/communicationProfiling
- @type:
- name:
communication profiling
conjunctionAnalysis
metadata sheet
- @context:
@vocab: <https://openminds.om-i.org/props/>
- @id:
https://openminds.om-i.org/instances/analysisTechnique/conjunctionAnalysis
- @type:
- name:
conjunction analysis
connected-componentAnalysis
metadata sheet
- @context:
@vocab: <https://openminds.om-i.org/props/>
- @id:
https://openminds.om-i.org/instances/analysisTechnique/connected-componentAnalysis
- @type:
- definition:
‘connected-component analysis’ is an algorithmic application of graph theory, where subsets of connected components are uniquely labeled based on a given heuristic. [adapted from: [wikipedia](https://en.wikipedia.org/wiki/Connected-component_labeling)]
- name:
connected-component analysis
connectivityBasedParcellationTechnique
metadata sheet
- @context:
@vocab: <https://openminds.om-i.org/props/>
- @id:
https://openminds.om-i.org/instances/analysisTechnique/connectivityBasedParcellationTechnique
- @type:
- name:
connectivity based parcellation technique
convolution
metadata sheet
- @context:
@vocab: <https://openminds.om-i.org/props/>
- @id:
https://openminds.om-i.org/instances/analysisTechnique/convolution
- @type:
- definition:
In functional analysis, ‘convolution’ is a mathematical operation on two functions (f and g) producing a third function (f * g) that expresses how the shape of one is modified by the other. [adapted from [wikipedia](https://en.wikipedia.org/wiki/Convolution)]
- name:
convolution
correlationAnalysis
metadata sheet
- @context:
@vocab: <https://openminds.om-i.org/props/>
- @id:
https://openminds.om-i.org/instances/analysisTechnique/correlationAnalysis
- @type:
- name:
correlation analysis
covarianceAnalysis
metadata sheet
- @context:
@vocab: <https://openminds.om-i.org/props/>
- @id:
https://openminds.om-i.org/instances/analysisTechnique/covarianceAnalysis
- @type:
- name:
covariance analysis
currentSourceDensityAnalysis
metadata sheet
- @context:
@vocab: <https://openminds.om-i.org/props/>
- @id:
https://openminds.om-i.org/instances/analysisTechnique/currentSourceDensityAnalysis
- @type:
- name:
current source density analysis
cytoarchitectonicMapping
metadata sheet
- @context:
@vocab: <https://openminds.om-i.org/props/>
- @id:
https://openminds.om-i.org/instances/analysisTechnique/cytoarchitectonicMapping
- @type:
- definition:
‘Cytoarchitectonic mapping’ is a delineation technique that defines regional borders based on histological analysis of the cellular composition of the studied tissue.
- name:
cytoarchitectonic mapping
deepLearning
metadata sheet
- @context:
@vocab: <https://openminds.om-i.org/props/>
- @id:
https://openminds.om-i.org/instances/analysisTechnique/deepLearning
- @type:
- name:
deep learning
densityMeasurement
metadata sheet
- @context:
@vocab: <https://openminds.om-i.org/props/>
- @id:
https://openminds.om-i.org/instances/analysisTechnique/densityMeasurement
- @type:
- name:
density measurement
dictionaryLearning
metadata sheet
- @context:
@vocab: <https://openminds.om-i.org/props/>
- @id:
https://openminds.om-i.org/instances/analysisTechnique/dictionaryLearning
- @type:
- definition:
‘Dictionary learning’ is a branch of signal processing and machine learning that aims at finding a frame (called dictionary) in which some training data admits a sparse representation.
- name:
dictionary learning
diffeomorphicRegistration
metadata sheet
- @context:
@vocab: <https://openminds.om-i.org/props/>
- @id:
https://openminds.om-i.org/instances/analysisTechnique/diffeomorphicRegistration
- @type:
- definition:
‘Diffeomorphic registration’ refers to a suite of algorithms that register or build correspondences between dense coordinate systems in medical imaging by ensuring the solutions are diffeomorphic.
- name:
diffeomorphic registration
dynamicCausalModeling
metadata sheet
- @context:
@vocab: <https://openminds.om-i.org/props/>
- @id:
https://openminds.om-i.org/instances/analysisTechnique/dynamicCausalModeling
- @type:
- definition:
An analysis framework for specifying non-linear state-space models in continuous time using stochastic or ordinary differential equations, for fitting them to data and comparing their evidence using Bayesian model comparison.[adapted from [Wikipedia](https://en.wikipedia.org/wiki/Dynamic_causal_modeling)]
- name:
dynamic causal modeling
- preferredOntologyIdentifier:
eyeMovementAnalysis
metadata sheet
- @context:
@vocab: <https://openminds.om-i.org/props/>
- @id:
https://openminds.om-i.org/instances/analysisTechnique/eyeMovementAnalysis
- @type:
- definition:
‘Eye movement analysis’ refers to a group of techniques used to analyze eye movements from video or images.
- name:
eye movement analysis
generalLinearModeling
metadata sheet
- @context:
@vocab: <https://openminds.om-i.org/props/>
- @id:
https://openminds.om-i.org/instances/analysisTechnique/generalLinearModeling
- @type:
- name:
general linear modeling
geneticCorrelationAnalysis
metadata sheet
- @context:
@vocab: <https://openminds.om-i.org/props/>
- @id:
https://openminds.om-i.org/instances/analysisTechnique/geneticCorrelationAnalysis
- @type:
- name:
genetic correlation analysis
geneticRiskScore
metadata sheet
- @context:
@vocab: <https://openminds.om-i.org/props/>
- @id:
https://openminds.om-i.org/instances/analysisTechnique/geneticRiskScore
- @type:
- definition:
A genetic risk score is an estimate of the cumulative contribution of genetic factors to a specific outcome of interest in an individual (Igo et al, 2019).
- description:
[described in: Igo, R. P., Jr, Kinzy, T. G., & Cooke Bailey, J. N. (2019). Genetic Risk Scores. Current protocols in human genetics, 104(1), e95. https://doi.org/10.1002/cphg.95]
- name:
genetic risk score
globalSignalRegression
metadata sheet
- @context:
@vocab: <https://openminds.om-i.org/props/>
- @id:
https://openminds.om-i.org/instances/analysisTechnique/globalSignalRegression
- @type:
- definition:
A ‘global signal regression’ is a denoising technique where the global signal is removed from the time series of each voxel through linear regression. [adapted from: [Murphy & Fox, 2017](https://dx.doi.org/10.1016%2Fj.neuroimage.2016.11.052)]
- name:
global signal regression
hierarchicalAgglomerativeClustering
metadata sheet
- @context:
@vocab: <https://openminds.om-i.org/props/>
- @id:
https://openminds.om-i.org/instances/analysisTechnique/hierarchicalAgglomerativeClustering
- @type:
- name:
hierarchical agglomerative clustering
hierarchicalClustering
metadata sheet
- @context:
@vocab: <https://openminds.om-i.org/props/>
- @id:
https://openminds.om-i.org/instances/analysisTechnique/hierarchicalClustering
- @type:
- name:
hierarchical clustering
hierarchicalDivisiveClustering
metadata sheet
- @context:
@vocab: <https://openminds.om-i.org/props/>
- @id:
https://openminds.om-i.org/instances/analysisTechnique/hierarchicalDivisiveClustering
- @type:
- name:
hierarchical divisive clustering
imageDistortionCorrection
metadata sheet
- @context:
@vocab: <https://openminds.om-i.org/props/>
- @id:
https://openminds.om-i.org/instances/analysisTechnique/imageDistortionCorrection
- @type:
- definition:
‘Image distortion correction’ is the general term for any image processing technique correcting optical or perspective aberrations of an image.
- name:
image distortion correction
imageRegistration
metadata sheet
- @context:
@vocab: <https://openminds.om-i.org/props/>
- @id:
https://openminds.om-i.org/instances/analysisTechnique/imageRegistration
- @type:
- definition:
An ‘image registration’ is a process of bringing a set of images into the same coordinate system.
- name:
image registration
independentComponentAnalysis
metadata sheet
- @context:
@vocab: <https://openminds.om-i.org/props/>
- @id:
https://openminds.om-i.org/instances/analysisTechnique/independentComponentAnalysis
- @type:
- name:
independent component analysis
interSubjectAnalysis
metadata sheet
- @context:
@vocab: <https://openminds.om-i.org/props/>
- @id:
https://openminds.om-i.org/instances/analysisTechnique/interSubjectAnalysis
- @type:
- name:
inter-subject analysis
interpolation
metadata sheet
- @context:
@vocab: <https://openminds.om-i.org/props/>
- @id:
https://openminds.om-i.org/instances/analysisTechnique/interpolation
- @type:
- definition:
An ‘interpolation’ is an analysis technique that delivers estimates for new data points based on a range of a discrete set of known data points.
- name:
interpolation
intraSubjectAnalysis
metadata sheet
- @context:
@vocab: <https://openminds.om-i.org/props/>
- @id:
https://openminds.om-i.org/instances/analysisTechnique/intraSubjectAnalysis
- @type:
- name:
intra-subject analysis
isometricMapping
metadata sheet
- @context:
@vocab: <https://openminds.om-i.org/props/>
- @id:
https://openminds.om-i.org/instances/analysisTechnique/isometricMapping
- @type:
- definition:
A superclass of distance-preserving transformations between metric spaces, often used to reduce dimensionality of data by embedding one space into another. [adapted from [Wikipedia](https://en.wikipedia.org/wiki/Isometry)]
- name:
isometric mapping
k-meansClustering
metadata sheet
- @context:
@vocab: <https://openminds.om-i.org/props/>
- @id:
https://openminds.om-i.org/instances/analysisTechnique/k-meansClustering
- @type:
- definition:
‘k-means clustering’ is a centroid-based cluster analysis technique that aims to partition n observations into a pre-defined number of k clusters by assigning each observation to the cluster with the nearest mean (centroid).
- name:
k-means clustering
linearImageRegistration
metadata sheet
- @context:
@vocab: <https://openminds.om-i.org/props/>
- @id:
https://openminds.om-i.org/instances/analysisTechnique/linearImageRegistration
- @type:
- definition:
A ‘linear image registration’ is a process of bringing a set of images into the same coordinate system using linear transformation.
- name:
linear image registration
linearRegression
metadata sheet
- @context:
@vocab: <https://openminds.om-i.org/props/>
- @id:
https://openminds.om-i.org/instances/analysisTechnique/linearRegression
- @type:
- definition:
A ‘linear regression’ is an analysis approach for modelling the linear relationship between a scalar response and one or more explanatory variables.
- name:
linear regression
linearTransformation
metadata sheet
- @context:
@vocab: <https://openminds.om-i.org/props/>
- @id:
https://openminds.om-i.org/instances/analysisTechnique/linearTransformation
- @type:
- definition:
A ‘linear transformation’ is a linear mathematical function to map coordinates between two different coordinate systems while preserving straight lines.
- name:
linear transformation
literatureMining
metadata sheet
- @context:
@vocab: <https://openminds.om-i.org/props/>
- @id:
https://openminds.om-i.org/instances/analysisTechnique/literatureMining
- @type:
- name:
literature mining
macromolecularTissueVolumeImageProcessing
metadata sheet
- @context:
@vocab: <https://openminds.om-i.org/props/>
- @id:
https://openminds.om-i.org/instances/analysisTechnique/macromolecularTissueVolumeImageProcessing
- @type:
- definition:
Magnetic resonance imaging analysis technique that provides a quantitative estimate of the macromolecular tissue volume within the image. [adapted from [Mezer et al., (2013)](https://doi.org/10.1038/nm.3390)].
- name:
macromolecular tissue volume image processing
magnetizationTransferRatioImageProcessing
metadata sheet
- @context:
@vocab: <https://openminds.om-i.org/props/>
- @id:
https://openminds.om-i.org/instances/analysisTechnique/magnetizationTransferRatioImageProcessing
- @type:
- name:
magnetization transfer ratio image processing
magnetizationTransferSaturationImageProcessing
metadata sheet
- @context:
@vocab: <https://openminds.om-i.org/props/>
- @id:
- @type:
- definition:
Magnetization transfer estimation method that improves the contrast between white matter, gray matter, and cerebrospinal fluid, as well as the correlation with macromolecular content [adapted from [Longoni et al., (2023)](https://doi.org/10.1177/13524585221137500)].
- name:
magnetization transfer saturation image processing
manifoldLearning
metadata sheet
- @context:
@vocab: <https://openminds.om-i.org/props/>
- @id:
https://openminds.om-i.org/instances/analysisTechnique/manifoldLearning
- @type:
- definition:
‘manifold learning’ refers to a group of machine learning algorithms for non-linear dimensionality reduction of high-dimensionalty data.
- name:
manifold learning
massUnivariateAnalysis
metadata sheet
- @context:
@vocab: <https://openminds.om-i.org/props/>
- @id:
https://openminds.om-i.org/instances/analysisTechnique/massUnivariateAnalysis
- @type:
- definition:
A ‘mass univariate analysis’ is the statistical analysis of a massive number of simultaneously measured dependent variables via the performance of univariate hypothesis tests.
- name:
mass univariate analysis
maximumLikelihoodEstimation
metadata sheet
- @context:
@vocab: <https://openminds.om-i.org/props/>
- @id:
https://openminds.om-i.org/instances/analysisTechnique/maximumLikelihoodEstimation
- @type:
- definition:
‘Maximum likelihood estimation’ is a statistical analysis technique that estimates the parameters of an assumed probability distribution for some observed data by maximizing a likelihood function so that, under the assumed statistical model, the observed data is most probable. [adapted from [wikipedia](https://en.wikipedia.org/wiki/Maximum_likelihood_estimation)]
- name:
maximum likelihood estimation technique
maximumProbabilityProjection
metadata sheet
- @context:
@vocab: <https://openminds.om-i.org/props/>
- @id:
https://openminds.om-i.org/instances/analysisTechnique/maximumProbabilityProjection
- @type:
- name:
maximum probability projection
metaAnalysis
metadata sheet
- @context:
@vocab: <https://openminds.om-i.org/props/>
- @id:
https://openminds.om-i.org/instances/analysisTechnique/metaAnalysis
- @type:
- name:
meta-analysis
metaAnalyticConnectivityModeling
metadata sheet
- @context:
@vocab: <https://openminds.om-i.org/props/>
- @id:
https://openminds.om-i.org/instances/analysisTechnique/metaAnalyticConnectivityModeling
- @type:
- name:
meta-analytic connectivity modeling
metadataParsing
metadata sheet
- @context:
@vocab: <https://openminds.om-i.org/props/>
- @id:
https://openminds.om-i.org/instances/analysisTechnique/metadataParsing
- @type:
- name:
metadata parsing
modelBasedStimulationArtifactCorrection
metadata sheet
- @context:
@vocab: <https://openminds.om-i.org/props/>
- @id:
https://openminds.om-i.org/instances/analysisTechnique/modelBasedStimulationArtifactCorrection
- @type:
- definition:
The ‘model-based stimulation artifact correction’ is a model-based analysis technique for removing stimulation artifacts from intracranial electroencephalography signals to uncover the cortico-cortical evoked potentials caused by the stimulation (cf. [Trebaul et al. (2016)](https://doi.org/10.1016/j.jneumeth.2016.03.002)).
- name:
model-based stimulation artifact correction
morphometry
metadata sheet
- @context:
@vocab: <https://openminds.om-i.org/props/>
- @id:
https://openminds.om-i.org/instances/analysisTechnique/morphometry
- @type:
- name:
morphometry
motionAnalysis
metadata sheet
- @context:
@vocab: <https://openminds.om-i.org/props/>
- @id:
https://openminds.om-i.org/instances/analysisTechnique/motionAnalysis
- @type:
- definition:
‘Motion analysis’ refers to a group of analysis techniques used to measure from video/images the movement and/or position of an object, specimen, or anatomical parts of a specimen over a given period of time.
- name:
motion analysis
motionCorrection
metadata sheet
- @context:
@vocab: <https://openminds.om-i.org/props/>
- @id:
https://openminds.om-i.org/instances/analysisTechnique/motionCorrection
- @type:
- definition:
‘Motion correction’ is the general term for any preprocessing analysis technique used to correct for motion artifacts in imaging time-series.
- name:
motion correction
multi-scaleIndividualComponentClustering
metadata sheet
- @context:
@vocab: <https://openminds.om-i.org/props/>
- @id:
https://openminds.om-i.org/instances/analysisTechnique/multi-scaleIndividualComponentClustering
- @type:
- definition:
‘multi-scale individual component clustering’ is a multi-scale, unsupervised cluster analysis technique to group individual, independent components of a single-object/single-subject independent component analysis (ICA) from an object-pool/subject-pool (cf. [Naveau et al, 2012](https://doi.org/10.1007/s12021-012-9145-2)).
- name:
multi-scale individual component clustering
multiVoxelPatternAnalysis
metadata sheet
- @context:
@vocab: <https://openminds.om-i.org/props/>
- @id:
https://openminds.om-i.org/instances/analysisTechnique/multiVoxelPatternAnalysis
- @type:
- definition:
A ‘multi-voxel pattern analysis’ is considered as a supervised classification problem where a classifier attempts to capture the relationships between spatial patterns of functional magnetic resonance imaging activity and experimental conditions ([Mahmoudi et al., 2012](https://doi.org/10.1155/2012/961257), [Davatzikos et al., 2005](https://doi.org/10.1016/j.neuroimage.2005.08.009)).
- name:
multi-voxel pattern analysis
multipleLinearRegression
metadata sheet
- @context:
@vocab: <https://openminds.om-i.org/props/>
- @id:
https://openminds.om-i.org/instances/analysisTechnique/multipleLinearRegression
- @type:
- definition:
A ‘multiple linear regression’ is a linear approach for modelling the relationship between a scalar response and multiple explanatory variables. [adapted from [wikipedia](https://en.wikipedia.org/wiki/Linear_regression)]
- name:
multiple linear regression
multivariateAnalysis
metadata sheet
- @context:
@vocab: <https://openminds.om-i.org/props/>
- @id:
https://openminds.om-i.org/instances/analysisTechnique/multivariateAnalysis
- @type:
- definition:
Any statistical analysis of data where multiple measurements are made on each experimental unit and where the relationships among multivariate measurements and their structure are important. [adapted from [Olkin and Sampson, 2001](https://doi.org/10.1016/B0-08-043076-7/00472-1)]
- name:
multivariate analysis
myelinWaterFractionImageProcessing
metadata sheet
- @context:
@vocab: <https://openminds.om-i.org/props/>
- @id:
https://openminds.om-i.org/instances/analysisTechnique/myelinWaterFractionImageProcessing
- @type:
- name:
myelin water fraction image processing
nonlinearImageRegistration
metadata sheet
- @context:
@vocab: <https://openminds.om-i.org/props/>
- @id:
https://openminds.om-i.org/instances/analysisTechnique/nonlinearImageRegistration
- @type:
- definition:
A ‘nonlinear image registration’ is a process of bringing a set of images into the same coordinate system using nonlinear transformation.
- name:
nonlinear image registration
nonlinearTransformation
metadata sheet
- @context:
@vocab: <https://openminds.om-i.org/props/>
- @id:
https://openminds.om-i.org/instances/analysisTechnique/nonlinearTransformation
- @type:
- definition:
A ‘nonlinear transformation’ is a mathematical function to map coordinates between two different coordinate systems, not preserving straight lines.
- name:
nonlinear transformation
nonrigidImageRegistration
metadata sheet
- @context:
@vocab: <https://openminds.om-i.org/props/>
- @id:
https://openminds.om-i.org/instances/analysisTechnique/nonrigidImageRegistration
- @type:
- definition:
A ‘nonrigid image registration’ is a process of bringing a set of images into the same coordinate system using nonrigid transformation.
- name:
nonrigid image registration
nonrigidMotionCorrection
metadata sheet
- @context:
@vocab: <https://openminds.om-i.org/props/>
- @id:
https://openminds.om-i.org/instances/analysisTechnique/nonrigidMotionCorrection
- @type:
- name:
nonrigid motion correction
nonrigidTransformation
metadata sheet
- @context:
@vocab: <https://openminds.om-i.org/props/>
- @id:
https://openminds.om-i.org/instances/analysisTechnique/nonrigidTransformation
- @type:
- definition:
A ‘nonrigid transformation’ is a specific linear transformation using combinations of rotations, translations, reflections, scaling, shearing, and perspective projections to map coordinates between two coordinate spaces.
- name:
nonrigid transformation
nuisanceRegression
metadata sheet
- @context:
@vocab: <https://openminds.om-i.org/props/>
- @id:
https://openminds.om-i.org/instances/analysisTechnique/nuisanceRegression
- @type:
- definition:
‘Nuisance regression’ is an image processing technique which seeks to attenuate non-neural BOLD fluctuations from measurable noise sources such as scanner drift and head motion, as well as periodic physiological signals. [adapted from [Hallquist et al. 2013](https://doi.org/10.1016%2Fj.neuroimage.2013.05.116)]
- name:
nuisance regression
pathwayAnalysis
metadata sheet
- @context:
@vocab: <https://openminds.om-i.org/props/>
- @id:
https://openminds.om-i.org/instances/analysisTechnique/pathwayAnalysis
- @type:
- definition:
A ‘pathway analysis’ refers to a group of techniques that aim to discover what biological themes, and which biomolecules, are crucial to understand biological pathways of (typically) high-throughput biological data (adapted from [García-Campos et al., 2015](https://doi.org/10.3389/fphys.2015.00383)).
- name:
pathway analysis
- preferredOntologyIdentifier:
performanceProfiling
metadata sheet
- @context:
@vocab: <https://openminds.om-i.org/props/>
- @id:
https://openminds.om-i.org/instances/analysisTechnique/performanceProfiling
- @type:
- name:
performance profiling
phaseSynchronizationAnalysis
metadata sheet
- @context:
@vocab: <https://openminds.om-i.org/props/>
- @id:
https://openminds.om-i.org/instances/analysisTechnique/phaseSynchronizationAnalysis
- @type:
- definition:
A ‘phase synchronization analysis’ detects and quantifies synchronization between two time series.
- name:
phase synchronization analysis
principalComponentAnalysis
metadata sheet
- @context:
@vocab: <https://openminds.om-i.org/props/>
- @id:
https://openminds.om-i.org/instances/analysisTechnique/principalComponentAnalysis
- @type:
- definition:
A ‘principal component analysis’ is a statistical technique for reducing the dimensionality of a dataset by linearly transforming the data into a new coordinate system where (most of) the variation in the data can be described with fewer dimensions than the initial data. [adapted from [wikipedia](https://en.wikipedia.org/wiki/Principal_component_analysis)]
- name:
principal component analysis
probabilisticAnatomicalParcellationTechnique
metadata sheet
- @context:
@vocab: <https://openminds.om-i.org/props/>
- @id:
https://openminds.om-i.org/instances/analysisTechnique/probabilisticAnatomicalParcellationTechnique
- @type:
- name:
probabilistic anatomical parcellation technique
probabilisticDiffusionTractography
metadata sheet
- @context:
@vocab: <https://openminds.om-i.org/props/>
- @id:
https://openminds.om-i.org/instances/analysisTechnique/probabilisticDiffusionTractography
- @type:
- name:
probabilistic diffusion tractography
qualitativeAnalysis
metadata sheet
- @context:
@vocab: <https://openminds.om-i.org/props/>
- @id:
https://openminds.om-i.org/instances/analysisTechnique/qualitativeAnalysis
- @type:
- definition:
‘Qualitative analysis’ uses subjective judgment to analyze data based on non-quantifiable information. The resulting data are typically nonnumerical.
- name:
qualitative analysis
quantitativeAnalysis
metadata sheet
- @context:
@vocab: <https://openminds.om-i.org/props/>
- @id:
https://openminds.om-i.org/instances/analysisTechnique/quantitativeAnalysis
- @type:
- name:
quantitative analysis
ratiometry
metadata sheet
- @context:
@vocab: <https://openminds.om-i.org/props/>
- @id:
https://openminds.om-i.org/instances/analysisTechnique/ratiometry
- @type:
- definition:
Quantitative analysis technique utilizing the ratio of two signals or responses obtained from a sample.
- name:
ratiometry
reconstructionTechnique
metadata sheet
- @context:
@vocab: <https://openminds.om-i.org/props/>
- @id:
https://openminds.om-i.org/instances/analysisTechnique/reconstructionTechnique
- @type:
- definition:
A ‘reconstruction technique’ is able to re-build, re-assemble, re-create, or re-imagine something by applying (often mathematical) principles to physical evidence.
- name:
reconstruction technique
rigidImageRegistration
metadata sheet
- @context:
@vocab: <https://openminds.om-i.org/props/>
- @id:
https://openminds.om-i.org/instances/analysisTechnique/rigidImageRegistration
- @type:
- definition:
A ‘rigid image registration’ is a process of bringing a set of images into the same coordinate system using rigid transformation.
- name:
rigid image registration
rigidMotionCorrection
metadata sheet
- @context:
@vocab: <https://openminds.om-i.org/props/>
- @id:
https://openminds.om-i.org/instances/analysisTechnique/rigidMotionCorrection
- @type:
- name:
rigid motion correction
rigidTransformation
metadata sheet
- @context:
@vocab: <https://openminds.om-i.org/props/>
- @id:
https://openminds.om-i.org/instances/analysisTechnique/rigidTransformation
- @type:
- definition:
A ‘rigid transformation’ is a specific linear transformation using combinations of rotations, translations, and reflections to map coordinates between two coordinate spaces, leaving the object congruent.
- name:
rigid transformation
seed-basedCorrelationAnalysis
metadata sheet
- @context:
@vocab: <https://openminds.om-i.org/props/>
- @id:
https://openminds.om-i.org/instances/analysisTechnique/seed-basedCorrelationAnalysis
- @type:
- name:
seed-based correlation analysis
semanticAnchoring
metadata sheet
- @context:
@vocab: <https://openminds.om-i.org/props/>
- @id:
https://openminds.om-i.org/instances/analysisTechnique/semanticAnchoring
- @type:
- name:
semantic anchoring
semiquantitativeAnalysis
metadata sheet
- @context:
@vocab: <https://openminds.om-i.org/props/>
- @id:
https://openminds.om-i.org/instances/analysisTechnique/semiquantitativeAnalysis
- @type:
- definition:
An analysis technique which constitutes or involves less than quantitative precision.
- name:
semiquantitative analysis
signalFilteringTechnique
metadata sheet
- @context:
@vocab: <https://openminds.om-i.org/props/>
- @id:
https://openminds.om-i.org/instances/analysisTechnique/signalFilteringTechnique
- @type:
- definition:
‘Signal filtering’ is a signal processing technique used to remove or suppress unwanted components or features (e.g., certain frequencies) from a measured signal. [adapted from [wikipedia](https://en.wikipedia.org/wiki/Filter_(signal_processing))]
- name:
signal filtering technique
- preferredOntologyIdentifier:
signalProcessingTechnique
metadata sheet
- @context:
@vocab: <https://openminds.om-i.org/props/>
- @id:
https://openminds.om-i.org/instances/analysisTechnique/signalProcessingTechnique
- @type:
- definition:
‘Signal processing’ refers to a class of analysis techniques used to improve transmission, storage efficiency and subjective quality as well as to emphasize or detect components of interest in a measured signal. [adapted from [wikipedia](https://en.wikipedia.org/wiki/Signal_processing)]
- name:
signal processing technique
- preferredOntologyIdentifier:
sliceTimingCorrection
metadata sheet
- @context:
@vocab: <https://openminds.om-i.org/props/>
- @id:
https://openminds.om-i.org/instances/analysisTechnique/sliceTimingCorrection
- @type:
- definition:
‘Slice timing correction’ is a preprocessing technique applied to functional magnetic resonance image data in order to correct for temporal offsets between 2D image slices during the data acquisition. [adapted from [Parker and Razlighi, 2019](https://doi.org/10.3389/fnins.2019.00821)]
- name:
slice timing correction
spectralPowerAutoSegmentationTechnique
metadata sheet
- @context:
@vocab: <https://openminds.om-i.org/props/>
- @id:
https://openminds.om-i.org/instances/analysisTechnique/spectralPowerAutoSegmentationTechnique
- @type:
- definition:
A ‘spectral power auto-segmentation technique’ makes use of the power spectrum along the time axis of individual pixels or voxels in an image to automatically generate a segmentation.
- name:
spectral power auto-segmentation technique
spikeSorting
metadata sheet
- @context:
@vocab: <https://openminds.om-i.org/props/>
- @id:
https://openminds.om-i.org/instances/analysisTechnique/spikeSorting
- @type:
- definition:
‘Spike sorting’ is a class of techniques used in the analysis of extracellular electrophysiological data to extract the activity of one or more neurons from the background electrical noise by making use of the typical waveforms action potentials (spikes) create in the recorded neuronal signal.
- name:
spike sorting
- preferredOntologyIdentifier:
stochasticOnlineMatrixFactorization
metadata sheet
- @context:
@vocab: <https://openminds.om-i.org/props/>
- @id:
https://openminds.om-i.org/instances/analysisTechnique/stochasticOnlineMatrixFactorization
- @type:
- definition:
‘Stochastic online matrix factorization’ is a matrix-factorization algorithm that scales to input matrices with both huge number of rows and columns [(Mensch et al., 2018)](https://doi.org/10.1109/TSP.2017.2752697).
- name:
stochastic online matrix factorization
structuralCovarianceAnalysis
metadata sheet
- @context:
@vocab: <https://openminds.om-i.org/props/>
- @id:
https://openminds.om-i.org/instances/analysisTechnique/structuralCovarianceAnalysis
- @type:
- name:
structural covariance analysis
supportVectorMachineClassifier
metadata sheet
- @context:
@vocab: <https://openminds.om-i.org/props/>
- @id:
https://openminds.om-i.org/instances/analysisTechnique/supportVectorMachineClassifier
- @type:
- definition:
A ‘support-vector machine classifier’ is a supervised machine learning technique that analyzes data for classification.
- name:
support-vector machine classifier
supportVectorMachineRegression
metadata sheet
- @context:
@vocab: <https://openminds.om-i.org/props/>
- @id:
https://openminds.om-i.org/instances/analysisTechnique/supportVectorMachineRegression
- @type:
- definition:
A ‘Support-Vector Regression Algorithm’ is a supervised machine learning technique used to estimate the relationship between a dependent and a number of independent variables.
- name:
support-vector regression algorithm
surfaceProjection
metadata sheet
- @context:
@vocab: <https://openminds.om-i.org/props/>
- @id:
https://openminds.om-i.org/instances/analysisTechnique/surfaceProjection
- @type:
- name:
surface projection
temporalFiltering
metadata sheet
- @context:
@vocab: <https://openminds.om-i.org/props/>
- @id:
https://openminds.om-i.org/instances/analysisTechnique/temporalFiltering
- @type:
- definition:
‘Temporal filtering’ is a functional image signal processing technique that aims to remove or attenuate frequencies that vary along the time axis of the raw signal. [adapted from [Wikibooks](https://en.wikibooks.org/wiki/Neuroimaging_Data_Processing/Processing/Steps/Temporal_Filtering)]
- name:
temporal filtering
tractography
metadata sheet
- @context:
@vocab: <https://openminds.om-i.org/props/>
- @id:
https://openminds.om-i.org/instances/analysisTechnique/tractography
- @type:
- name:
tractography
transformation
metadata sheet
- @context:
@vocab: <https://openminds.om-i.org/props/>
- @id:
https://openminds.om-i.org/instances/analysisTechnique/transformation
- @type:
- definition:
A ‘transformation’ is a mathematical function to map coordinates between two different coordinate systems.
- name:
transformation
univariateAnalysis
metadata sheet
- @context:
@vocab: <https://openminds.om-i.org/props/>
- @id:
https://openminds.om-i.org/instances/analysisTechnique/univariateAnalysis
- @type:
- definition:
Any statistical analysis that is carried out on only one (dependent) variable of the data to summarize or describe that variable. [adapted from [Dandilands, 2014](https://doi.org/10.1007/978-94-007-0753-5_3108)]
- name:
univariate analysis
videoAnnotation
metadata sheet
- @context:
@vocab: <https://openminds.om-i.org/props/>
- @id:
https://openminds.om-i.org/instances/analysisTechnique/videoAnnotation
- @type:
- name:
video annotation
voxel-basedMorphometry
metadata sheet
- @context:
@vocab: <https://openminds.om-i.org/props/>
- @id:
https://openminds.om-i.org/instances/analysisTechnique/voxel-basedMorphometry
- @type:
- name:
voxel-based morphometry
zScoreAnalysis
metadata sheet
- @context:
@vocab: <https://openminds.om-i.org/props/>
- @id:
https://openminds.om-i.org/instances/analysisTechnique/zScoreAnalysis
- @type:
- definition:
The ‘z-score analysis’ is a statistical normalization technique where the z-score is calculated by subtracting the population mean from an individual raw score (observed data point) and dividing the difference by the population standard deviation. [adapted from [Wikipedia](https://en.wikipedia.org/wiki/Standard_score)]
- name:
z-score analysis