Enum: ConnectivityMethod¶

Method used to estimate connectivity. Open vocabulary: write the source's own wording in the field when nothing here fits.

URI: parse:ConnectivityMethod

Permissible Values¶

Value	Meaning	Description
ppi	None	Psychophysiological interaction: connectivity with a seed that changes with a...
gppi	None	Generalized PPI, which models every condition rather than a single contrast
seed_based	None	Correlation between a seed's time series and every other element, with no int...
dcm	None	Dynamic causal modelling, which estimates directed influence from a generativ...
granger	None	Granger causality, which infers direction from temporal precedence
coherence	None	Frequency-domain coupling between two signals
structural_covariance	None	Correlation of a structural measure between regions across participants, rath...
multivariate	None	Pattern-based connectivity: multivoxel or informational connectivity between ...

In Subsets¶

Identifier and Mapping Information¶

Schema Source¶

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

LinkML Source¶

name: ConnectivityMethod
description: 'Method used to estimate connectivity. 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:
  ppi:
    text: ppi
    description: 'Psychophysiological interaction: connectivity with a seed that changes
      with a psychological condition.'
    in_subset:
    - mvp
    - model_extracted
  gppi:
    text: gppi
    description: Generalized PPI, which models every condition rather than a single
      contrast.
    in_subset:
    - mvp
    - model_extracted
  seed_based:
    text: seed_based
    description: Correlation between a seed's time series and every other element,
      with no interaction term.
    in_subset:
    - mvp
    - model_extracted
  dcm:
    text: dcm
    description: Dynamic causal modelling, which estimates directed influence from
      a generative model.
    in_subset:
    - mvp
    - model_extracted
  granger:
    text: granger
    description: Granger causality, which infers direction from temporal precedence.
    in_subset:
    - mvp
    - model_extracted
  coherence:
    text: coherence
    description: Frequency-domain coupling between two signals.
    in_subset:
    - mvp
    - model_extracted
  structural_covariance:
    text: structural_covariance
    description: Correlation of a structural measure between regions across participants,
      rather than of a signal within one. The seed and target are regions as in any
      other method here, but the correlation runs over subjects, so there is no time
      series.
    in_subset:
    - mvp
    - model_extracted
  multivariate:
    text: multivariate
    description: 'Pattern-based connectivity: multivoxel or informational connectivity
      between ensembles of voxels rather than between averaged time series.'
    in_subset:
    - mvp
    - model_extracted