In neuroimaging the typical analysis approach is a “mass univariate”, where a univariate model is independently fit to each voxel (volume element) in parallel. This approach can be applied to a large range of datasets including: functional MRI, anatomical MRI (with Voxel-Based Morphometry), PET, EEG and MEG data.

The three major software analysis package for neuroimaging: SPM (Statistical Parametric Mapping) and FSL (FMRIB Software Library) and AFNI (AFNI) provide implementations of mass univariate analyses. While there are many commonalities across software packages, there are also software-specific outputs that can be of interest for the end-user.

This document describes the encoding of results of a mass univariate neuroimaging analysis using the NIDM data model ([[!KEATORNI13]]). The goal of this specification is to provide a unified representation of neuroimaging results across analysis software. When a piece of information is only available in a specific software, software-specific extensions are provided.

NIDM-Results is a part of NIDM as depicted in .

NIDM-Results as part of NIDM
NIDM-Results as part of NIDM.

NIDM Family of Documents

This document is part of the NIDM family of documents.

Please Send Comments

This document was published by the NIDASH NIDM Working Group as a Candidate Release. If you wish to make comments regarding this document, please report using the NIDM issue tracker or send them to nidm-users@googlegroups.com. All comments are welcome.

Introduction

NIDM-Results is a NIDM compliant structured representation of the results of a massively univariate neuroimaging study.

NIDM-Results specification formally describes the encoding of the massively univariate neuroimaging results into a NIDM representation. Extensions are provided to describe software-specific objects for SPM (Statistical Parametric Mapping) and FSL (FMRIB Software Library) analysis software.

Structure of this Document

Section 2 provides an overview of NIDM-Results, distinguishing a core set of types and relations, commonly found in provenance, from extended structures catering for more specific uses. It also introduces a modular organization of the data model in components.

Section 3 illustrates how NIDM-Results can be used to express the results of a neuroimaging study in SPM and FSL.

Section 4 provides the definitions (and examples) of NIDM-Results concepts, structured according to three components.

Notational Conventions

The key word "OPTIONAL" in this document is to be interpreted as described in [RFC2119].

Examples throughout this document use the PROV-N Provenance Notation, specified in a separate document [[prov-n]].

Namespaces

The following namespaces prefixes are used throughout this document.

Table 1: Prefix and Namespaces used in this specification
prefixnamespace IRI definition
nidmhttp://purl.org/nidash/nidm#The NIDM namespace
provhttp://www.w3.org/ns/prov#The PROV namespace [[prov-dm]]
xsdhttp://www.w3.org/2000/10/XMLSchema#XML Schema Namespace [[XMLSCHEMA11-2]]
(others)(various)All other namespace prefixes are used in examples only.
In particular, IRIs starting with "http://example.com" represent
some application-dependent IRI [[RFC3987]]

Overview

This section introduces neuroimaging results concepts with informal explanations and illustrative examples (e.g. see SPM results). NIDM-Results distinguishes software-agnostic structures, forming the essence of the results, from software-specific structures catering for more specific uses of results by different analysis software. Core and extended structures are respectively presented in Section 2.1 and Section 2.2.

Domain covered by NIDM-Results

NIDM-Results is concerned with the modelling of model fitting and inference in the context of massively univariate analyses. A typical example is the analysis of functional MRI data but studies involving other modalities (such as PET) and sequences (e.g. anatomical MRI through VBM) can also be modelled. The domain covered by NIDM-Results data model is represented in .

Domain overview

NIDM-Results Core Structures

The concepts found in the core of NIDM-Results are introduced in the rest of this section.

Overview

The core NIDM-Results structures are presented in . The color coding corresponds to the prov:type (blue: prov:entity, red: prov:activity). NIDM-Results is divided in two elements: "Model fitting" and "Inference", the structures and relations belonging to each element are presented in details in and .
Core structures overview

Re-use of PROV-DM Structures

PROV relations used in NIDM-Results are summarized in Table 2.

Table 2: PROV relations in use in NIDM-Results
NIDM-Results ConceptsTypes or Relation (PROV concepts)NameOverview
Generation PROV Relations
 WasGeneratedBy PROV-DM wasGeneratedBy
UsageUsedPROV-DM used
DerivationWasDerivedFromPROV-DM wasDerivedFrom

NIDM-Results: Types and relations

General

Table 3: Mapping of NIDM-Results General Core Concepts to types and relations and PROV core concepts
NIDM-Results Concepts Types or Relation (PROV concepts) Name
nidm:'Map' NIDM-Results Types
(PROV 'Entity')		 nidm:'Map'

nidm:'Map'

nidm:'Map': Ordered set of values corresponding to the discrete sampling of some process (e.g. brain MRI data measured on a regular 3D lattice; or brain cortical surface data measured irregularly over the cortex) nidm:'Map' is a prov:'Entity'.

A nidm:'Map' has attributes:

Parameters estimation

Table 4: Mapping of NIDM-Results Parameters estimation Core Concepts to types and relations and PROV core concepts
NIDM-Results Concepts Types or Relation (PROV concepts) Name
nidm:'Model Parameters Estimation' NIDM-Results Types
(PROV 'Activity')		 nidm:'Model Parameters Estimation'
nidm:'Data Scaling' NIDM-Results Types
(PROV 'Entity')		 nidm:'Data Scaling'
nidm:'Design Matrix' nidm:'Design Matrix'
nidm:'Error Model' nidm:'Error Model'
nidm:'Grand Mean Map' nidm:'Grand Mean Map'
nidm:'Mask Map' nidm:'Mask Map'
nidm:'Parameter Estimate Map' nidm:'Parameter Estimate Map'
nidm:'Residual Mean Squares Map' nidm:'Residual Mean Squares Map'
nidm:'Resels Per Voxel Map' nidm:'Resels Per Voxel Map'

nidm:'Model Parameters Estimation'

nidm:'Model Parameters Estimation': The process of performing a obo:model parameter estimation of a map nidm:'Model Parameters Estimation' is a prov:'Activity' that uses nidm:'Data Scaling', nidm:'Design Matrix', nidm:'Error Model', nidm:'Mask Map' entities. This activity generates nidm:'Mask Map', nidm:'Parameter Estimate Map', nidm:'Resels Per Voxel Map', nidm:'Residual Mean Squares Map' entities. 

@prefix nidm_ModelParametersEstimation: <http://purl.org/nidash/nidm#NIDM_0000056> .
@prefix nidm_withEstimationMethod: <http://purl.org/nidash/nidm#NIDM_0000134> .
@prefix obo_ordinaryleastsquaresestimation: <http://purl.obolibrary.org/obo/STATO_0000370> .


niiri:model_pe_id prov:used niiri:error_model_id ;
	a prov:Activity , nidm_ModelParametersEstimation: ;
	rdfs:label "Model parameters estimation" ;
	nidm_withEstimationMethod: obo_ordinaryleastsquaresestimation: ; # obo:'ordinary least squares estimation'
	prov:used niiri:design_matrix_id ;
    prov:used niiri:data_id ;
    prov:used niiri:error_model_id ;
    prov:wasAssociatedWith niiri:software_id .

nidm:'Data Scaling'

nidm:'Data Scaling': Scaling applied to the data before parameter estimation, including specification of the target intensity nidm:'Data Scaling' is a prov:'Entity' used by nidm:'Model Parameters Estimation'.

A nidm:'Data Scaling' has attributes:
  • rdfs:label: (OPTIONAL) Human readable description of the nidm:'Data Scaling'.
  • nidm:'grand Mean Scaling': (OPTIONAL) Binary flag defining whether the data was scaled (true for scaled). Specifically, "grand mean scaling" refers to multiplication of every voxel in every scan by a common (or session-specific) value (range xsd:boolean).
  • nidm:'target Intensity': (OPTIONAL) Value to which the grand mean of the Data was scaled (applies only if grand mean scaling is true) (range xsd:float).
@prefix nidm_DataScaling: <http://purl.org/nidash/nidm#NIDM_0000018> .
@prefix nidm_grandMeanScaling: <http://purl.org/nidash/nidm#NIDM_0000096> .
@prefix nidm_targetIntensity: <http://purl.org/nidash/nidm#NIDM_0000124> .


niiri:data_id a prov:Entity , nidm_DataScaling: , prov:Collection ;
    rdfs:label "Data" ;
    nidm_grandMeanScaling: "true"^^xsd:boolean ;
    nidm_targetIntensity: "100"^^xsd:float .

nidm:'Design Matrix'

nidm:'Design Matrix': A stato:design matrix, with additional neuroimaging attributes, including HRF and drift for first level fMRI models nidm:'Design Matrix' is a prov:'Entity' used by nidm:'Contrast Estimation', nidm:'Model Parameters Estimation'.

A nidm:'Design Matrix' has attributes: 
@prefix nidm_DesignMatrix: <http://purl.org/nidash/nidm#NIDM_0000019> .


niiri:design_matrix_id a prov:Entity , nidm_DesignMatrix: ;
	rdfs:label "Design Matrix" ;
	prov:atLocation "file:///path/to/DesignMatrix.csv"^^xsd:anyURI ;
	dct:format "text/csv"^^xsd:string ;
	nfo:fileName "DesignMatrix.csv"^^xsd:string ;
	dc:description niiri:design_matrix_png_id .

nidm:'fMRI Design Type'

nidm:'fMRI Design Type': The type of stimulus presentation used in the data acquisition, one of block-based design event-related design or mixed design nidm:'fMRI Design Type' is a prov:'Entity'.

A nidm:'fMRI Design Type' has attributes:

nidm:'Drift Model'

nidm:'Drift Model': A model used to compensate for low frequency baseline drifts when analyzing functional MRI data at the subject level nidm:'Drift Model' is a prov:'Entity'.

A nidm:'Drift Model' has attributes: 
@prefix fsl_GaussianRunningLineDriftModel: <http://purl.org/nidash/fsl#FSL_0000002> .
@prefix fsl_driftCutoffPeriod: <http://purl.org/nidash/fsl#FSL_0000004> .


niiri:drift_model_id a prov:Entity , fsl_GaussianRunningLineDriftModel: ;
	rdfs:label "FSL's Gaussian Running Line Drift Model" ;
	fsl_driftCutoffPeriod: "2"^^xsd:float .
@prefix spm_DCTDriftModel: <http://purl.org/nidash/spm#SPM_0000002> .
@prefix spm_SPMsDriftCutoffPeriod: <http://purl.org/nidash/spm#SPM_0000001> .


niiri:drift_model_id a prov:Entity , spm_DCTDriftModel: ;
	rdfs:label "SPM's DCT Drift Model" ;
	spm_SPMsDriftCutoffPeriod: "128"^^xsd:float .

fsl:'Gaussian Running Line Drift Model'

fsl:'Gaussian Running Line Drift Model': A drift model in which the drifts are modeled with a Gaussian-weighted running line smoother, fit to and subtracted from the data and each column of the design matrix fsl:'Gaussian Running Line Drift Model' is a prov:'Entity'.

A fsl:'Gaussian Running Line Drift Model' has attributes:

spm:'DCT Drift Model'

spm:'DCT Drift Model': A drift model in which the drifts are modeled by a Discrete Cosine Transform basis added to regression model spm:'DCT Drift Model' is a prov:'Entity'.

A spm:'DCT Drift Model' has attributes:

nidm:'Finite Impulse Response Basis Set'

nidm:'Finite Impulse Response Basis Set': Set of Finite impulse response (FIR) filters, with FIR the convolution kernel is represented as a set of discrete fixed-width "impulses" (definition adapted from FSL wiki nidm:'Finite Impulse Response Basis Set' is a prov:'Entity'.

A nidm:'Finite Impulse Response Basis Set' has attributes:

nidm:'Gamma Difference HRF'

nidm:'Gamma Difference HRF': Hemodynamic response function which is a difference of two gamma probability density functions nidm:'Gamma Difference HRF' is a prov:'Entity'.

A nidm:'Gamma Difference HRF' has attributes:
  • rdfs:label: (OPTIONAL) Human readable description of the nidm:'Gamma Difference HRF'.
    • Examples of nidm:'Gamma Difference HRF' includes
    • fsl:'FSL's Gamma Difference HRF': Hemodynamic response function which is a fixed difference of two gamma probability density functions - a standard positive function at normal lag, and a small, delayed, negated gamma probability density function, which attempts to model the late undershoot (definition adapted FSL wiki. This is the default in FSL
    • spm:'SPM's Canonical HRF': Hemodynamic response function which is a fixed difference of two gamma probability density functions and is denoted by "canonical HRF" in SPM. This is the default in SPM

nidm:'Gamma Basis Set'

nidm:'Gamma Basis Set': Set of gamma probability density functions nidm:'Gamma Basis Set' is a prov:'Entity'.

A nidm:'Gamma Basis Set' has attributes:

nidm:'Gamma HRF'

nidm:'Gamma HRF': Hemodynamic response function which is a gamma probability density function nidm:'Gamma HRF' is a prov:'Entity'.

A nidm:'Gamma HRF' has attributes:

nidm:'Hemodynamic Response Function'

nidm:'Hemodynamic Response Function': Hemodynamic response function basis that can on its own be used to represent the idealised hemodynamic response function nidm:'Hemodynamic Response Function' is a prov:'Entity'.

A nidm:'Hemodynamic Response Function' has attributes:

nidm:'Convolution Basis Set'

nidm:'Convolution Basis Set': nidm:'Convolution Basis Set' is a prov:'Entity'.

A nidm:'Convolution Basis Set' has attributes:

nidm:'Error Model'

nidm:'Error Model': Model used to describe the random variation of the error term as part of parameter estimation, including specification of the error probability distribution, its variance and dependence both spatially and across observations nidm:'Error Model' is a prov:'Entity' used by nidm:'Model Parameters Estimation'.

A nidm:'Error Model' has attributes: 
@prefix nidm_ErrorModel: <http://purl.org/nidash/nidm#NIDM_0000023> .
@prefix nidm_hasErrorDistribution: <http://purl.org/nidash/nidm#NIDM_0000101> .
@prefix nidm_GaussianDistribution: <http://purl.org/nidash/nidm#NIDM_0000032> .
@prefix nidm_errorVarianceHomogeneous: <http://purl.org/nidash/nidm#NIDM_0000094> .
@prefix nidm_varianceMapWiseDependence: <http://purl.org/nidash/nidm#NIDM_0000126> .
@prefix nidm_IndependentParameter: <http://purl.org/nidash/nidm#NIDM_0000073> .
@prefix nidm_hasErrorDependence: <http://purl.org/nidash/nidm#NIDM_0000100> .
@prefix nidm_IndependentError: <http://purl.org/nidash/nidm#NIDM_0000048> .
@prefix nidm_dependenceMapWiseDependence: <http://purl.org/nidash/nidm#NIDM_0000089> .


niiri:error_model_id a prov:Entity , nidm_ErrorModel: ;
    nidm_hasErrorDistribution: nidm_GaussianDistribution: ;
    nidm_errorVarianceHomogeneous: "true"^^xsd:boolean ;
    nidm_varianceMapWiseDependence: nidm_IndependentParameter: ;
    nidm_hasErrorDependence: nidm_IndependentError: ;
    nidm_dependenceMapWiseDependence: nidm_IndependentParameter: .

nidm:'Error Parameter Map-Wise Dependence'

nidm:'Error Parameter Map-Wise Dependence': Map-wise dependence structure of a parameter in the error model (i.e. variance or dependence parameter). For example, whether a temporal autocorrelation parameter is estimated at each element separately, estimated using data in a local neighbourhood, or estimated using all elements in the map nidm:'Error Parameter Map-Wise Dependence' is a prov:'Entity'.

A nidm:'Error Parameter Map-Wise Dependence' has attributes:

nidm:'Arbitrarily Correlated Error'

nidm:'Arbitrarily Correlated Error': nidm:'Arbitrarily Correlated Error' is a prov:'Entity'.

A nidm:'Arbitrarily Correlated Error' has attributes:

nidm:'Exchangeable Error'

nidm:'Exchangeable Error': nidm:'Exchangeable Error' is a prov:'Entity'.

A nidm:'Exchangeable Error' has attributes:

nidm:'Independent Error'

nidm:'Independent Error': nidm:'Independent Error' is a prov:'Entity'.

A nidm:'Independent Error' has attributes:

nidm:'Binomial Distribution'

nidm:'Binomial Distribution': The binomial distribution is a discrete probability distribution which describes the probability of k successes in n draws with replacement from a finite population of size N. The binomial distribution is frequently used to model the number of successes in a sample of size n drawn with replacement from a population of size N. The binomial distribution gives the discrete probability distribution of obtaining exactly n successes out of N Bernoulli trials (where the result of each Bernoulli trial is true with probability p and false with probability q=1-p ) notation: B(n,p) The mean is N*p The variance is N*p*q. (Definition from STATO) nidm:'Binomial Distribution' is a prov:'Entity'.

A nidm:'Binomial Distribution' has attributes:

nidm:'Error Distribution'

nidm:'Error Distribution': Probability distribution used to model the error nidm:'Error Distribution' is a prov:'Entity'.

A nidm:'Error Distribution' has attributes:

nidm:'Grand Mean Map'

nidm:'Grand Mean Map': A map whose value at each element (e.g., pixel, voxel, vertex, or face) is the mean over all observations of that element in the input maps (after any scaling of those input maps) nidm:'Grand Mean Map' is a prov:'Entity'.

A nidm:'Grand Mean Map' has attributes: 
@prefix nidm_GrandMeanMap: <http://purl.org/nidash/nidm#NIDM_0000033> .
@prefix nidm_maskedMedian: <http://purl.org/nidash/nidm#NIDM_0000107> .
@prefix nidm_inCoordinateSpace: <http://purl.org/nidash/nidm#NIDM_0000104> .


niiri:grand_mean_map_id a prov:Entity , nidm_GrandMeanMap: ;
	rdfs:label "Grand Mean Map" ;
	prov:atLocation "file:///path/to/GrandMean.nii.gz"^^xsd:anyURI ;
	nfo:fileName "GrandMean.nii.gz"^^xsd:string ;
	dct:format "image/nifti"^^xsd:string ;
	nidm_maskedMedian: "115"^^xsd:float ;
	nidm_inCoordinateSpace: niiri:coordinate_space_id_1 ;
	crypto:sha512 "e43b6e01b0463fe7d40782137867a..."^^xsd:string ;
    prov:wasGeneratedBy niiri:model_pe_id .

nidm:'Mask Map'

nidm:'Mask Map': A binary map representing the exact set of elements (e.g., pixels, voxels, vertices, and faces) in which an activity was performed (e.g. the mask map generated by the model parameter estimation activity represents the exact set of voxels in which the mass univariate model was estimated) and/or restraining the space in which an activity was performed (e.g. the mask map used by inference) nidm:'Mask Map' is a prov:'Entity' used by nidm:'Contrast Estimation', nidm:'Model Parameters Estimation' and generated by nidm:'Model Parameters Estimation'.

A nidm:'Mask Map' has attributes: 
@prefix nidm_MaskMap: <http://purl.org/nidash/nidm#NIDM_0000054> .
@prefix nidm_isUserDefined: <http://purl.org/nidash/nidm#NIDM_0000106> .
@prefix nidm_inCoordinateSpace: <http://purl.org/nidash/nidm#NIDM_0000104> .


niiri:model_pe_id prov:used niiri:mask_id_2 .
niiri:mask_id_2 a prov:Entity , nidm_MaskMap: ;
	rdfs:label "Mask" ;
    nidm_isUserDefined: "false"^^xsd:boolean ;
	prov:atLocation "file:///path/to/Mask.nii.gz"^^xsd:anyURI ;
	nfo:fileName "Mask.nii.gz"^^xsd:string ;
	dct:format "image/nifti"^^xsd:string ;
	nidm_inCoordinateSpace: niiri:coordinate_space_id_1 ;
	crypto:sha512 "e43b6e01b0463fe7d40782137867a..."^^xsd:string .

nidm:'Parameter Estimate Map'

nidm:'Parameter Estimate Map': A map whose value at each element (e.g., pixel, voxel, vertex, or face) is a model parameter estimate nidm:'Parameter Estimate Map' is a prov:'Entity' used by nidm:'Contrast Estimation' and generated by nidm:'Model Parameters Estimation'.

A nidm:'Parameter Estimate Map' has attributes: 
@prefix nidm_ParameterEstimateMap: <http://purl.org/nidash/nidm#NIDM_0000061> .
@prefix nidm_inCoordinateSpace: <http://purl.org/nidash/nidm#NIDM_0000104> .


niiri:beta_map_id_1 a prov:Entity , nidm_ParameterEstimateMap: ;
    rdfs:label "Beta Map 1" ;
    nidm_inCoordinateSpace: niiri:coordinate_space_id_1 ;
    prov:wasGeneratedBy niiri:model_pe_id .

nidm:'Residual Mean Squares Map'

nidm:'Residual Mean Squares Map': A map whose value at each element (e.g., pixel, voxel, vertex, or face) is a residual mean square nidm:'Residual Mean Squares Map' is a prov:'Entity' used by nidm:'Contrast Estimation' and generated by nidm:'Model Parameters Estimation'.

A nidm:'Residual Mean Squares Map' has attributes: 
@prefix nidm_ResidualMeanSquaresMap: <http://purl.org/nidash/nidm#NIDM_0000066> .
@prefix nidm_inCoordinateSpace: <http://purl.org/nidash/nidm#NIDM_0000104> .


niiri:residual_mean_squares_map_id a prov:Entity , nidm_ResidualMeanSquaresMap: ;
	rdfs:label "Residual Mean Squares Map" ;
	prov:atLocation "file:///path/to/ResidualMeanSquares.nii.gz"^^xsd:anyURI ;
	nfo:fileName "ResidualMeanSquares.nii.gz"^^xsd:string ;
	dct:format "image/nifti"^^xsd:string ;
	nidm_inCoordinateSpace: niiri:coordinate_space_id_1 ;
	crypto:sha512 "e43b6e01b0463fe7d40782137867a..."^^xsd:string ;
    prov:wasGeneratedBy niiri:model_pe_id .

nidm:'Resels Per Voxel Map'

nidm:'Resels Per Voxel Map': A map whose value at each element (e.g., pixel, voxel, vertex, or face) location is the number of resels per voxel nidm:'Resels Per Voxel Map' is a prov:'Entity' used by nidm:'Inference' and generated by nidm:'Model Parameters Estimation'.

A nidm:'Resels Per Voxel Map' has attributes: 
@prefix nidm_ReselsPerVoxelMap: <http://purl.org/nidash/nidm#NIDM_0000144> .
@prefix nidm_inCoordinateSpace: <http://purl.org/nidash/nidm#NIDM_0000104> .


niiri:resels_per_voxel_map_id a prov:Entity , nidm_ReselsPerVoxelMap: ;
	rdfs:label "Resels per Voxel Map" ;
	prov:atLocation "file:///path/to/ReselsPerVoxel.nii.gz"^^xsd:anyURI ;
	nfo:fileName "ReselsPerVoxel.nii.gz"^^xsd:string ;
	dct:format "image/nifti"^^xsd:string ;
	nidm_inCoordinateSpace: niiri:coordinate_space_id_1 ;
	crypto:sha512 "e43b6e01b0463fe7d40782137867a..."^^xsd:string ;
    prov:wasGeneratedBy niiri:model_pe_id.

Contrast estimation

Table 5: Mapping of NIDM-Results Contrast estimation Core Concepts to types and relations and PROV core concepts
NIDM-Results Concepts Types or Relation (PROV concepts) Name
nidm:'Contrast Estimation' NIDM-Results Types
(PROV 'Activity')		 nidm:'Contrast Estimation'
obo:'contrast weight matrix' NIDM-Results Types
(PROV 'Entity')		 obo:'contrast weight matrix'
nidm:'Contrast Map' nidm:'Contrast Map'
nidm:'Contrast Standard Error Map' nidm:'Contrast Standard Error Map'
nidm:'Statistic Map' nidm:'Statistic Map'

nidm:'Contrast Estimation'

nidm:'Contrast Estimation': The process of performing a obo:contrast estimation of a map nidm:'Contrast Estimation' is a prov:'Activity' that uses nidm:'Design Matrix', nidm:'Mask Map', nidm:'Parameter Estimate Map', nidm:'Residual Mean Squares Map', obo:'contrast weight matrix' entities. This activity generates nidm:'Contrast Map', nidm:'Contrast Standard Error Map', nidm:'Statistic Map' entities.

A nidm:'Contrast Estimation' has attributes: 
@prefix nidm_ContrastEstimation: <http://purl.org/nidash/nidm#NIDM_0000001> .


niiri:contrast_estimation_id a prov:Activity , nidm_ContrastEstimation: ;
	rdfs:label "Contrast estimation" ;
	prov:used niiri:mask_id_2 , niiri:residual_mean_squares_map_id , niiri:design_matrix_id , niiri:contrast_id, niiri:beta_map_id_1 ;
    prov:wasAssociatedWith niiri:software_id .

obo:'contrast weight matrix'

obo:'contrast weight matrix': A contrast weight matrix is a information content entity which holds a set of contrast weight, coefficient used in a weighting sum of means defining a contrast obo:'contrast weight matrix' is a prov:'Entity' used by nidm:'Contrast Estimation'.

A obo:'contrast weight matrix' has attributes: 
[1,0,0]
@prefix nidm_statisticType: <http://purl.org/nidash/nidm#NIDM_0000123> .
@prefix nidm_contrastName: <http://purl.org/nidash/nidm#NIDM_0000085> .
@prefix obo_contrastweightmatrix: <http://purl.obolibrary.org/obo/STATO_0000323> .
@prefix obo_tstatistic: <http://purl.obolibrary.org/obo/STATO_0000176> .


niiri:contrast_id a prov:Entity , obo_contrastweightmatrix: ;
	rdfs:label "Contrast: Listening > Rest" ;
	prov:value "[ 1, 0, 0 ]"^^xsd:string ;
	nidm_statisticType: obo_tstatistic: ; # obo:'t-statistic'
	nidm_contrastName: "listening > rest"^^xsd:string .

nidm:'Contrast Map'

nidm:'Contrast Map': A map whose value at each element (e.g., pixel, voxel, vertex, or face) is a contrast estimate nidm:'Contrast Map' is a prov:'Entity' used by nidm:'Inference' and generated by nidm:'Contrast Estimation'.

A nidm:'Contrast Map' has attributes: 
@prefix nidm_ContrastMap: <http://purl.org/nidash/nidm#NIDM_0000002> .
@prefix nidm_contrastName: <http://purl.org/nidash/nidm#NIDM_0000085> .
@prefix nidm_inCoordinateSpace: <http://purl.org/nidash/nidm#NIDM_0000104> .


niiri:contrast_map_id a prov:Entity , nidm_ContrastMap: ;
	rdfs:label "Contrast Map: listening > rest" ;
	prov:atLocation "file:///path/to/Contrast.nii.gz"^^xsd:anyURI ;
	dct:format "image/nifti"^^xsd:string ;
	nfo:fileName "Contrast.nii.gz"^^xsd:string ;
	nidm_contrastName: "listening > rest"^^xsd:string ;
	nidm_inCoordinateSpace: niiri:coordinate_space_id_1 ;
	crypto:sha512 "e43b6e01b0463fe7d40782137867a..."^^xsd:string ;
    prov:wasGeneratedBy niiri:contrast_estimation_id .

nidm:'Contrast Standard Error Map'

nidm:'Contrast Standard Error Map': A map whose value at each element (e.g., pixel, voxel, vertex, or face) is a standard error of a contrast estimate nidm:'Contrast Standard Error Map' is a prov:'Entity' generated by nidm:'Contrast Estimation'.

A nidm:'Contrast Standard Error Map' has attributes: 
@prefix nidm_ContrastStandardErrorMap: <http://purl.org/nidash/nidm#NIDM_0000013> .
@prefix nidm_inCoordinateSpace: <http://purl.org/nidash/nidm#NIDM_0000104> .


niiri:contrast_standard_error_map_id a prov:Entity , nidm_ContrastStandardErrorMap: ;
	rdfs:label "Contrast Standard Error Map" ;
	prov:atLocation "file:///path/to/ContrastStandardError.nii.gz"^^xsd:anyURI ;
	nfo:fileName "ContrastStandardError.nii.gz"^^xsd:string ;
	dct:format "image/nifti"^^xsd:string ;
	nidm_inCoordinateSpace: niiri:coordinate_space_id_1 ;
	crypto:sha512 "e43b6e01b0463fe7d40782137867a..."^^xsd:string ;
    prov:wasGeneratedBy niiri:contrast_estimation_id .

nidm:'Statistic Map'

nidm:'Statistic Map': A map whose value at each element (e.g., pixel, voxel, vertex, or face) is a statistic nidm:'Statistic Map' is a prov:'Entity' used by nidm:'Inference' and generated by nidm:'Contrast Estimation'.

A nidm:'Statistic Map' has attributes: 
@prefix nidm_StatisticMap: <http://purl.org/nidash/nidm#NIDM_0000076> .
@prefix nidm_statisticType: <http://purl.org/nidash/nidm#NIDM_0000123> .
@prefix nidm_contrastName: <http://purl.org/nidash/nidm#NIDM_0000085> .
@prefix nidm_effectDegreesOfFreedom: <http://purl.org/nidash/nidm#NIDM_0000091> .
@prefix nidm_inCoordinateSpace: <http://purl.org/nidash/nidm#NIDM_0000104> .
@prefix nidm_errorDegreesOfFreedom: <http://purl.org/nidash/nidm#NIDM_0000093> .
@prefix obo_tstatistic: <http://purl.obolibrary.org/obo/STATO_0000176> .


niiri:statistic_map_id a prov:Entity , nidm_StatisticMap: ;
	rdfs:label "Statistic Map: listening > rest" ;
	prov:atLocation "file:///path/to/TStatistic.nii.gz"^^xsd:anyURI ;
	nidm_statisticType: obo_tstatistic: ; # obo:'t-statistic'
	nfo:fileName "TStatistic.nii.gz"^^xsd:string ;
	dct:format "image/nifti"^^xsd:string ;
	nidm_contrastName: "listening > rest"^^xsd:string ;
	nidm_effectDegreesOfFreedom: "1"^^xsd:float ;
	nidm_inCoordinateSpace: niiri:coordinate_space_id_1 ;
	crypto:sha512 "e43b6e01b0463fe7d40782137867a..."^^xsd:string ;
	prov:wasGeneratedBy niiri:contrast_estimation_id;
    nidm_errorDegreesOfFreedom: "72.9999999990787"^^xsd:float .

Inference

Table 6: Mapping of NIDM-Results Inference Core Concepts to types and relations and PROV core concepts
NIDM-Results Concepts Types or Relation (PROV concepts) Name
nidm:'Conjunction Inference' NIDM-Results Types
(PROV 'Activity')		 nidm:'Conjunction Inference'
nidm:'Inference' nidm:'Inference'
nidm:'Cluster Definition Criteria' NIDM-Results Types
(PROV 'Entity')		 nidm:'Cluster Definition Criteria'
nidm:'Cluster Labels Map' nidm:'Cluster Labels Map'
nidm:'Coordinate' nidm:'Coordinate'
nidm:'Display Mask Map' nidm:'Display Mask Map'
nidm:'Excursion Set Map' nidm:'Excursion Set Map'
nidm:'Extent Threshold' nidm:'Extent Threshold'
nidm:'Height Threshold' nidm:'Height Threshold'
nidm:'Peak' nidm:'Peak'
nidm:'Peak Definition Criteria' nidm:'Peak Definition Criteria'
nidm:'Search Space Mask Map' nidm:'Search Space Mask Map'
nidm:'Significant Cluster' nidm:'Significant Cluster'

nidm:'Conjunction Inference'

nidm:'Conjunction Inference': Statistically testing for the joint significance of multiple effects, with emphasis on rejecting all (instead of one or more) of the respective null hypotheses nidm:'Conjunction Inference' is a prov:'Activity'.

A nidm:'Conjunction Inference' has attributes:

nidm:'Inference'

nidm:'Inference': Statistical inference is a process of drawing conclusions following data analysis using statistical methods (statistical tests) and evaluating whether to reject or accept null hypothesis. (definition from STATO) nidm:'Inference' is a prov:'Activity' that uses nidm:'Cluster Definition Criteria', nidm:'Contrast Map', nidm:'Display Mask Map', nidm:'Peak Definition Criteria', nidm:'Resels Per Voxel Map', nidm:'Statistic Map' entities.

A nidm:'Inference' has attributes: 
@prefix nidm_Inference: <http://purl.org/nidash/nidm#NIDM_0000049> .
@prefix nidm_hasAlternativeHypothesis: <http://purl.org/nidash/nidm#NIDM_0000097> .
@prefix nidm_OneTailedTest: <http://purl.org/nidash/nidm#NIDM_0000060> .


niiri:inference_id a prov:Activity , nidm_Inference: ;
	rdfs:label "Inference" ;
	nidm_hasAlternativeHypothesis: nidm_OneTailedTest: ;
    prov:used niiri:statistic_map_id, niiri:height_threshold_id, niiri:extent_threshold_id, niiri:peak_definition_criteria_id, niiri:cluster_definition_criteria_id, niiri:mask_id ;
    prov:wasAssociatedWith niiri:software_id .

nidm:'Cluster Definition Criteria'

nidm:'Cluster Definition Criteria': Set of criterion specified a priori to define the clusters reported after inference (e.g. pixel or voxel connectivity criterion) nidm:'Cluster Definition Criteria' is a prov:'Entity' used by nidm:'Inference'.

A nidm:'Cluster Definition Criteria' has attributes: 
@prefix nidm_ClusterDefinitionCriteria: <http://purl.org/nidash/nidm#NIDM_0000007> .
@prefix nidm_hasConnectivityCriterion: <http://purl.org/nidash/nidm#NIDM_0000099> .
@prefix nidm_voxel18connected: <http://purl.org/nidash/nidm#NIDM_0000128> .


niiri:cluster_definition_criteria_id a prov:Entity , nidm_ClusterDefinitionCriteria: ;
	rdfs:label "Cluster Connectivity Criterion: 18" ;
	nidm_hasConnectivityCriterion: nidm_voxel18connected: .

nidm:'Connectivity Criterion'

nidm:'Connectivity Criterion': The criterion used to characterize two voxels, pixels or vertices as 'connected' nidm:'Connectivity Criterion' is a prov:'Entity'.

A nidm:'Connectivity Criterion' has attributes:

nidm:'Cluster Labels Map'

nidm:'Cluster Labels Map': A map whose value at each element (e.g., pixel, voxel, vertex, or face) denotes cluster membership within the excursion set. Each cluster is denoted by a different integer and all members of the same cluster have the same value nidm:'Cluster Labels Map' is a prov:'Entity'.

A nidm:'Cluster Labels Map' has attributes: 
@prefix nidm_ClusterLabelsMap: <http://purl.org/nidash/nidm#NIDM_0000008> .


niiri:cluster_label_map_id a prov:Entity , nidm_ClusterLabelsMap: ;
	prov:atLocation "file:///path/to/ClusterLabels.nii.gz"^^xsd:anyURI ;
	nfo:fileName "ClusterLabels.nii.gz"^^xsd:string ;
	dct:format "image/nifti"^^xsd:string .

nidm:'Coordinate'

nidm:'Coordinate': nidm:'Coordinate' is a prov:'Entity'.

A nidm:'Coordinate' has attributes:
  • rdfs:label: (OPTIONAL) Human readable description of the nidm:'Coordinate'.
  • nidm:'coordinate Vector': (OPTIONAL) A vector with one number per dimension. The first element corresponds to the coordinate along the first dimension measured in map elements (e.g., pixels, voxels, vertices, or faces), the second element to the coordinate along the second dimension etc (range xsd:string).
  • nidm:'coordinate Vector In Voxels': (OPTIONAL) Coordinate along the first dimension in voxels. (This definition needs to be re-worked as this term was renamed from coordinate1 to coordinate in https://github.com/incf-nidash/nidm/issues/270) (range xsd:string).
@prefix nidm_Coordinate: <http://purl.org/nidash/nidm#NIDM_0000015> .
@prefix nidm_coordinateVector: <http://purl.org/nidash/nidm#NIDM_0000086> .


niiri:coordinate_0001 a prov:Entity , prov:Location , nidm_Coordinate: ;
	rdfs:label "Coordinate: 0001" ;
	nidm_coordinateVector: "[ -60, -28, 13 ]"^^xsd:string .

nidm:'Display Mask Map'

nidm:'Display Mask Map': A binary map used by an activity that changed the voxels displayed, but did not alter the space in which the activity was performed (e.g. at the level of inference, this mask is called "contrast masking" in both FSL & SPM and does not alter voxel-wise corrected p-values) nidm:'Display Mask Map' is a prov:'Entity' used by nidm:'Inference'.

A nidm:'Display Mask Map' has attributes: 
@prefix nidm_DisplayMaskMap: <http://purl.org/nidash/nidm#NIDM_0000020> .
@prefix nidm_inCoordinateSpace: <http://purl.org/nidash/nidm#NIDM_0000104> .


niiri:display_map_id a prov:Entity , nidm_DisplayMaskMap: ;
	rdfs:label "Display Mask Map" ;
	prov:atLocation "file:///path/to/DisplayMask.nii.gz"^^xsd:anyURI ;
	dct:format "image/nifti"^^xsd:string ;
    nfo:fileName "DisplayMask.nii.gz"^^xsd:string ;
	nidm_inCoordinateSpace: niiri:coordinate_space_id_2 ;
	crypto:sha512 "e43b6e01b0463fe7d40782137867a..."^^xsd:string .

nidm:'Excursion Set Map'

nidm:'Excursion Set Map': A map in which the set of elements (e.g., pixels, voxels, vertices, or faces) not selected by the inference activity is set to zero or NaN nidm:'Excursion Set Map' is a prov:'Entity'.

A nidm:'Excursion Set Map' has attributes: 
@prefix nidm_ExcursionSetMap: <http://purl.org/nidash/nidm#NIDM_0000025> .
@prefix nidm_hasClusterLabelsMap: <http://purl.org/nidash/nidm#NIDM_0000098> .
@prefix nidm_hasMaximumIntensityProjection: <http://purl.org/nidash/nidm#NIDM_0000138> .
@prefix nidm_inCoordinateSpace: <http://purl.org/nidash/nidm#NIDM_0000104> .
@prefix nidm_numberOfSignificantClusters: <http://purl.org/nidash/nidm#NIDM_0000111> .
@prefix nidm_pValue: <http://purl.org/nidash/nidm#NIDM_0000114> .


niiri:excursion_set_map_id a prov:Entity , nidm_ExcursionSetMap: ;
	rdfs:label "Excursion Set Map" ;
	prov:atLocation "file:///path/to/ExcursionSet.nii.gz"^^xsd:anyURI ;
	dct:format "image/nifti"^^xsd:string ;
	nfo:fileName "ExcursionSet.nii.gz"^^xsd:string ;
	nidm_hasClusterLabelsMap: niiri:cluster_label_map_id ;
	nidm_hasMaximumIntensityProjection: niiri:maximum_intensity_projection_id ;
	nidm_inCoordinateSpace: niiri:coordinate_space_id_1 ;
	crypto:sha512 "e43b6e01b0463fe7d40782137867a..."^^xsd:string ;
	nidm_numberOfSignificantClusters: "8"^^xsd:int ;
	nidm_pValue: "8.95949980872501e-14"^^xsd:float ;
	prov:wasGeneratedBy niiri:inference_id .

nidm:'Extent Threshold'

nidm:'Extent Threshold': A numerical value that establishes a lower bound on cluster-sizes and can be specified by the user in terms of FWER-corrected p-value, uncorrected p-value or minimum cluster size in voxels nidm:'Extent Threshold' is a prov:'Entity'.

A nidm:'Extent Threshold' has attributes: 
@prefix nidm_ExtentThreshold: <http://purl.org/nidash/nidm#NIDM_0000026> .
@prefix nidm_clusterSizeInVoxels: <http://purl.org/nidash/nidm#NIDM_0000084> .
@prefix nidm_clusterSizeInResels: <http://purl.org/nidash/nidm#NIDM_0000156> .
@prefix nidm_pValueUncorrected: <http://purl.org/nidash/nidm#NIDM_0000116> .
@prefix nidm_pValueFWER: <http://purl.org/nidash/nidm#NIDM_0000115> .


niiri:extent_threshold_id a prov:Entity , nidm_ExtentThreshold: ;
	rdfs:label "Extent Threshold: k>=0" ;
	nidm_clusterSizeInVoxels: "0"^^xsd:int ;
	nidm_clusterSizeInResels: "0"^^xsd:float ;
	nidm_pValueUncorrected: "1"^^xsd:float ;
	nidm_pValueFWER: "1"^^xsd:float .

nidm:'Height Threshold'

nidm:'Height Threshold': A numerical value that establishes a lower bound on statistic values and can be specified by the user in terms of FWER-corrected p-value, uncorrected p-value or minimum statistic value nidm:'Height Threshold' is a prov:'Entity'.

A nidm:'Height Threshold' has attributes:
  • rdfs:label: (OPTIONAL) Human readable description of the nidm:'Height Threshold'.
  • nidm:'p Value FWER': (OPTIONAL) "A quantitative confidence value resulting from a multiple testing error correction method which adjusts the p-value used as input to control for Type I error in the context of multiple pairwise tests" (range xsd:float).
  • nidm:'p Value Uncorrected': (OPTIONAL) A p-value reported without correction for multiple testing. (range xsd:float).
  • nidm:'q Value FDR': (OPTIONAL) A quantitative confidence value that measures the minimum false discovery rate that is incurred when calling that test significant. To compute q-values, it is necessary to know the p-value produced by a test and possibly set a false discovery rate level (same as OBI_0001442) (range xsd:float).
  • nidm:'user Specified Threshold Type': (OPTIONAL) Type of method used to define a threshold (e.g. statistic value, uncorrected P-value or corrected P-value) (range xsd:string).
@prefix nidm_HeightThreshold: <http://purl.org/nidash/nidm#NIDM_0000034> .
@prefix nidm_userSpecifiedThresholdType: <http://purl.org/nidash/nidm#NIDM_0000125> .
@prefix nidm_pValueUncorrected: <http://purl.org/nidash/nidm#NIDM_0000116> .
@prefix nidm_pValueFWER: <http://purl.org/nidash/nidm#NIDM_0000115> .


niiri:height_threshold_id a prov:Entity , nidm_HeightThreshold: ;
	rdfs:label "Height Threshold: p<0.05 (FWE)" ;
	nidm_userSpecifiedThresholdType: "p-value FWE"^^xsd:string ;
	prov:value "5.23529984739211"^^xsd:float ;
    nidm_pValueUncorrected: "7.62276079258051e-07"^^xsd:float ;
	nidm_pValueFWER: "0.05"^^xsd:float .

nidm:'Peak'

nidm:'Peak': A map element (e.g., pixel, voxel, vertex, or face) which is a local maximum in the significant cluster and complies with the peak definition criteria nidm:'Peak' is a prov:'Entity' derived from nidm:'Significant Cluster'.

A nidm:'Peak' has attributes:
  • rdfs:label: (OPTIONAL) Human readable description of the nidm:'Peak'.
  • nidm:'equivalent ZStatistic': (OPTIONAL) Statistic value transformed into Z units; the output of a process which takes a non-normal statistic and transforms it to an equivalent z score (range xsd:float).
  • nidm:'p Value FWER': (OPTIONAL) "A quantitative confidence value resulting from a multiple testing error correction method which adjusts the p-value used as input to control for Type I error in the context of multiple pairwise tests" (range xsd:float).
  • nidm:'p Value Uncorrected': (OPTIONAL) A p-value reported without correction for multiple testing. (range xsd:float).
  • nidm:'q Value FDR': (OPTIONAL) A quantitative confidence value that measures the minimum false discovery rate that is incurred when calling that test significant. To compute q-values, it is necessary to know the p-value produced by a test and possibly set a false discovery rate level (same as OBI_0001442) (range xsd:float).
@prefix nidm_Peak: <http://purl.org/nidash/nidm#NIDM_0000062> .
@prefix nidm_pValueUncorrected: <http://purl.org/nidash/nidm#NIDM_0000116> .
@prefix nidm_equivalentZStatistic: <http://purl.org/nidash/nidm#NIDM_0000092> .
@prefix nidm_pValueFWER: <http://purl.org/nidash/nidm#NIDM_0000115> .
@prefix nidm_qValueFDR: <http://purl.org/nidash/nidm#NIDM_0000119> .


niiri:peak_0001 a prov:Entity , nidm_Peak: ;
    rdfs:label "Peak 0001" ;
    prov:atLocation niiri:coordinate_0001 ;
    nidm_pValueUncorrected: "4.44089209850063e-16"^^xsd:float ;
    nidm_equivalentZStatistic: "INF"^^xsd:float ;
    prov:wasDerivedFrom niiri:significant_cluster_0001 ;
    prov:value "13.9346199035645"^^xsd:float ;
	nidm_pValueFWER: "0"^^xsd:float ;
	nidm_qValueFDR: "6.3705194444993e-11"^^xsd:float .

nidm:'Peak Definition Criteria'

nidm:'Peak Definition Criteria': Set of criterion specified a priori to define the peaks reported after inference (e.g. maximum number of peaks within a cluster, minimum distance between peaks) nidm:'Peak Definition Criteria' is a prov:'Entity' used by nidm:'Inference'.

A nidm:'Peak Definition Criteria' has attributes: 
@prefix nidm_PeakDefinitionCriteria: <http://purl.org/nidash/nidm#NIDM_0000063> .
@prefix nidm_minDistanceBetweenPeaks: <http://purl.org/nidash/nidm#NIDM_0000109> .
@prefix nidm_maxNumberOfPeaksPerCluster: <http://purl.org/nidash/nidm#NIDM_0000108> .


niiri:peak_definition_criteria_id a prov:Entity , nidm_PeakDefinitionCriteria: ;
	rdfs:label "Peak Definition Criteria" ;
	nidm_minDistanceBetweenPeaks: "8.0"^^xsd:float ;
    nidm_maxNumberOfPeaksPerCluster: "3"^^xsd:int .

nidm:'Search Space Mask Map'

nidm:'Search Space Mask Map': A mask map representing the set of elements (e.g., pixels, voxels, vertices, or faces) in which the inference was performed nidm:'Search Space Mask Map' is a prov:'Entity'.

A nidm:'Search Space Mask Map' has attributes: 
@prefix nidm_SearchSpaceMaskMap: <http://purl.org/nidash/nidm#NIDM_0000068> .
@prefix nidm_inCoordinateSpace: <http://purl.org/nidash/nidm#NIDM_0000104> .
@prefix nidm_expectedNumberOfVoxelsPerCluster: <http://purl.org/nidash/nidm#NIDM_0000143> .
@prefix nidm_expectedNumberOfClusters: <http://purl.org/nidash/nidm#NIDM_0000141> .
@prefix nidm_heightCriticalThresholdFWE05: <http://purl.org/nidash/nidm#NIDM_0000147> .
@prefix nidm_heightCriticalThresholdFDR05: <http://purl.org/nidash/nidm#NIDM_0000146> .
@prefix nidm_searchVolumeInVoxels: <http://purl.org/nidash/nidm#NIDM_0000121> .
@prefix nidm_searchVolumeInUnits: <http://purl.org/nidash/nidm#NIDM_0000136> .
@prefix nidm_reselSizeInVoxels: <http://purl.org/nidash/nidm#NIDM_0000148> .
@prefix nidm_searchVolumeInResels: <http://purl.org/nidash/nidm#NIDM_0000149> .
@prefix nidm_randomFieldStationarity: <http://purl.org/nidash/nidm#NIDM_0000120> .
@prefix spm_smallestSignificantClusterSizeInVoxelsFWE05: <http://purl.org/nidash/spm#SPM_0000014> .
@prefix spm_smallestSignificantClusterSizeInVoxelsFDR05: <http://purl.org/nidash/spm#SPM_0000013> .
@prefix spm_searchVolumeReselsGeometry: <http://purl.org/nidash/spm#SPM_0000010> .
@prefix spm_noiseFWHMInVoxels: <http://purl.org/nidash/spm#SPM_0000009> .
@prefix spm_noiseFWHMInUnits: <http://purl.org/nidash/spm#SPM_0000007> .


niiri:search_space_mask_id a prov:Entity , nidm_SearchSpaceMaskMap: ;
	rdfs:label "Search Space Mask Map" ;
	prov:atLocation "file:///path/to/SearchSpaceMask.nii.gz"^^xsd:anyURI ;
	nfo:fileName "SearchSpaceMask.nii.gz"^^xsd:string ;
	dct:format "image/nifti"^^xsd:string ;
	nidm_inCoordinateSpace: niiri:coordinate_space_id_2 ;
	nidm_expectedNumberOfVoxelsPerCluster: "0.553331387916112"^^xsd:float ;
	nidm_expectedNumberOfClusters: "0.0889172687960151"^^xsd:float ;
	nidm_heightCriticalThresholdFWE05: "5.23529984739211"^^xsd:float ;
	nidm_heightCriticalThresholdFDR05: "6.22537899017334"^^xsd:float ;
	spm_smallestSignificantClusterSizeInVoxelsFWE05: "1"^^xsd:int ;
	spm_smallestSignificantClusterSizeInVoxelsFDR05: "3"^^xsd:int ;
	nidm_searchVolumeInVoxels: "65593"^^xsd:int ;
	nidm_searchVolumeInUnits: "1771011"^^xsd:float ;
	nidm_reselSizeInVoxels: "22.9229643140043"^^xsd:float ;
	nidm_searchVolumeInResels: "2552.68032521656"^^xsd:float ;
	spm_searchVolumeReselsGeometry: "[3, 72.3216126440484, 850.716735116472, 2552.68032521656]"^^xsd:string ;
	spm_noiseFWHMInVoxels: "[ 2.95881189165801, 2.96628446669584, 2.61180425626264 ]"^^xsd:string ;
	spm_noiseFWHMInUnits: "[ 8.87643567497404, 8.89885340008753, 7.83541276878791 ]"^^xsd:string ;
	nidm_randomFieldStationarity: "false"^^xsd:boolean ;
	crypto:sha512 "e43b6e01b0463fe7d40782137867a..."^^xsd:string ;
	prov:wasGeneratedBy niiri:inference_id .

nidm:'Significant Cluster'

nidm:'Significant Cluster': A cluster of map elements (e.g., pixels, voxels, vertices, and faces) that were selected by the inference activity and are contiguous according to the cluster connectivity criteria nidm:'Significant Cluster' is a prov:'Entity' derived from nidm:'Excursion Set Map'.

A nidm:'Significant Cluster' has attributes: 
@prefix nidm_SignificantCluster: <http://purl.org/nidash/nidm#NIDM_0000070> .
@prefix nidm_clusterSizeInVoxels: <http://purl.org/nidash/nidm#NIDM_0000084> .
@prefix nidm_clusterLabelId: <http://purl.org/nidash/nidm#NIDM_0000082> .
@prefix nidm_clusterSizeInResels: <http://purl.org/nidash/nidm#NIDM_0000156> .
@prefix nidm_pValueUncorrected: <http://purl.org/nidash/nidm#NIDM_0000116> .
@prefix nidm_pValueFWER: <http://purl.org/nidash/nidm#NIDM_0000115> .
@prefix nidm_qValueFDR: <http://purl.org/nidash/nidm#NIDM_0000119> .


niiri:significant_cluster_0001 a prov:Entity , nidm_SignificantCluster: ;
	rdfs:label "Significant Cluster 0001" ;
	nidm_clusterSizeInVoxels: "530"^^xsd:int ;
	nidm_clusterLabelId: "1"^^xsd:int ;
	nidm_clusterSizeInResels: "23.1209189500945"^^xsd:float ;
	nidm_pValueUncorrected: "9.56276736481136e-52"^^xsd:float ;
	nidm_pValueFWER: "0"^^xsd:float ;
	nidm_qValueFDR: "7.65021389184909e-51"^^xsd:float ;
	prov:wasDerivedFrom niiri:excursion_set_map_id .

Release notes: updates since NIDM-Results 0.2.0

Updated namespaces

  • nidm namespace (http://purl.org/nidash/nidm#) replaced by http://purl.org/nidash/nidm# PR 237
  • spm namespace (http://www.incf.org/ns/nidash/spm#) replaced by http://purl.org/nidash/spm# PR 237
  • fsl namespace (http://www.incf.org/ns/nidash/fsl#) replaced by http://purl.org/nidash/fsl# PR 237

Updated terms

Alphanumeric identifiers:

  • Semantic identifiers replaced by alphanumeric identifiers. For example, nidm:ContrastMap replaced by nidm:'Contrast Map' (cf. Constants.py for a complete list).

Re-use of Dublin Core and NFO terms (PR 247)

Re-use STATO estimation methods (PR 252):

Re-use Neurolex/RRID SPM and FSL terms (PR 263):

Re-use STATO statistic (PR 262):

Re-use error covariance structures from STATO (PR 306):

Re-use "contrast weight matrix" from STATO (PR 305):

Noise spatial model terms (PR 194):

Mask terms (PR 258):

  • nidm:CustomMask replaced by nidm:MaskMap with attribute nidm:isUserDefined = true.
  • nidm:SearchSpaceMask replaced by nidm:SearchSpaceMaskMap
  • nidm:DisplayMask replaced by nidm:DisplayMaskMap

coordinate1, coordinate2, coordinate3 -> coordinate Vector (PR 270):

Change in namespace:

New terms

Hemodynamic Response Function (HRF)(PR 248)

  • New attribute nidm:hasHRFBasis in nidm:DesignMatrix (only for first-level analyses) to represent which hemodynamic response function that was used.

Drift models (PR 261)

  • New attribute nidm:hasDriftModel in nidm:DesignMatrix (only for first-level analyses) to represent which drift model was used.

Type of fMRI design and regressor names (PR 299)

  • New attribute nidm:'has fMRI Design' in nidm:DesignMatrix (only for first-level analyses) specifying the type of fMRI design (block-based, event-related, mixed).
  • New attribute nidm:'regressor Names' in nidm:DesignMatrix (only for first-level analyses) providing list of abstract names for the design matrix regressors.

Updated labels

Deprecated terms

  • spm:softwareVersion deprecated PR 291
  • fsl:ClusterMaximumStatistic deprecated PR 286
  • nidm:objectModel deprecated PR 137