Enum: AnalysisType

The method that produced the statistical map, independent of what was tested about it. A between-group comparison of connectivity, decoding accuracy, or component loadings keeps the method here and records the comparison in Effect. Bound only by ConjunctionComponent.analysis_type; an Analysis states its method by which AnalysisDetails subclass it fills. Open vocabulary: write the source's own wording in the field when nothing here fits.

URI: parse:AnalysisType

Permissible Values

Value	Meaning	Description
mass_univariate	None	Voxelwise, vertexwise, or ROI-wise GLM, mixed-effects model, ANOVA or ANCOVA,...
multivariate_decoding	None	Classification or pattern discriminability
representational_similarity	None	Association at the level of representational dissimilarity matrices
connectivity	None	PPI, gPPI, seed-based connectivity, DCM, covariance, or Granger causality
conjunction	None	Logical combination of independently defined effects
latent_decomposition	None	One component or latent variable of a decomposition -- ICA, PCA, NMF, PLS, CC...
not_structurable	None	The effect is grounded in the source but has no stable structured decompositi...

In Subsets

• mvp
• model_extracted

Identifier and Mapping Information

Schema Source

• from schema: https://neurostuff.org/schemas/neuroimaging-paper-parse

LinkML Source

name: AnalysisType
description: 'The method that produced the statistical map, independent of what was
  tested about it. A between-group comparison of connectivity, decoding accuracy,
  or component loadings keeps the method here and records the comparison in Effect.
  Bound only by ConjunctionComponent.analysis_type; an Analysis states its method
  by which AnalysisDetails subclass it fills. Open vocabulary: write the source''s
  own wording in the field when nothing here fits.'
in_subset:
- mvp
- model_extracted
from_schema: https://neurostuff.org/schemas/neuroimaging-paper-parse
rank: 1000
permissible_values:
  mass_univariate:
    text: mass_univariate
    description: Voxelwise, vertexwise, or ROI-wise GLM, mixed-effects model, ANOVA
      or ANCOVA, or robust regression. This is the default family and takes no details
      payload.
    in_subset:
    - mvp
    - model_extracted
  multivariate_decoding:
    text: multivariate_decoding
    description: Classification or pattern discriminability.
    in_subset:
    - mvp
    - model_extracted
  representational_similarity:
    text: representational_similarity
    description: Association at the level of representational dissimilarity matrices.
      A trial-wise similarity or model-fit regressor entered into a univariate GLM
      is mass_univariate with a parametric_modulation effect, not this.
    in_subset:
    - mvp
    - model_extracted
  connectivity:
    text: connectivity
    description: PPI, gPPI, seed-based connectivity, DCM, covariance, or Granger causality.
      PPI stays here even when the software estimates it with a GLM.
    in_subset:
    - mvp
    - model_extracted
  conjunction:
    text: conjunction
    description: Logical combination of independently defined effects.
    in_subset:
    - mvp
    - model_extracted
  latent_decomposition:
    text: latent_decomposition
    description: 'One component or latent variable of a decomposition -- ICA, PCA,
      NMF, PLS, CCA, or a multimodal fusion. How many blocks were decomposed is not
      a method family: it is `LatentDecompositionDetails.second_block`. A conjunction
      component needing the distinction states it in this field''s free-text escape
      hatch.'
    in_subset:
    - mvp
    - model_extracted
  not_structurable:
    text: not_structurable
    description: The effect is grounded in the source but has no stable structured
      decomposition.
    in_subset:
    - mvp
    - model_extracted