Seed-based d mapping

Seed-based d mapping (formerly Signed differential mapping) or SDM is a statistical technique created by Joaquim Radua for meta-analysis studies assessing differences in brain activity or structure via neuroimaging techniques such as fMRI, VBM, DTI or PET.

Version

v1 · 2026-09-28 · History

Domain-specific #

11937

Domain group

Natural Sciences

Origin domain

Neuroscience

Subdomains

Neuroimaging Meta Analysis, Neuroimaging → Neuroscience

Core Idea

Seed-based d mapping is treated here as the recurring crossdomainmodelsstructuresrepresentations identity summarized by this source-grounded definition: Seed-based d mapping (formerly Signed differential mapping) or SDM is a statistical technique created by Joaquim Radua for meta-analysis studies assessing differences in brain activity or structure via neuroimaging techniques such as fMRI, VBM, DTI or PET. Seed-based d mapping (formerly Signed differential mapping) or SDM is a statistical technique created by Joaquim Radua for meta-analysis studies assessing differences.

Scope of Application

• The seed-based d mapping approachOverview of the method. SDM adopted and combined various positive features from previous methods, such as ALE or MKDA, and introduced a series of improvements and novel features.

• The seed-based d mapping approachOverview of the method. One of the new features, introduced to avoid positive and negative findings in the same voxel as seen in previous methods, was the representation of both positive differences and negative differences.

• The seed-based d mapping approachOverview of the method. Another relevant feature, introduced in version 2.11, was the use of effect sizes (leading to effect-size SDM or 'ES-SDM'), which allows combination of reported peak coordinates with statistical parametric maps.

• The seed-based d mapping approachOverview of the method. Second, coordinates are used to recreate statistical maps, and effect-sizes maps and their variances are derived from t-statistics (or equivalently from p-values or z-scores).

• Inclusion criteria. In order to overcome this issue SDM introduced a criterion in the selection of the coordinates: while different studies may employ different thresholds, you should ensure that the same threshold throughout.

Clarity

A clear use of Seed-based d mapping names the carrier, the operative relation, and the conditions under which the source treats the identity as present. The minimal definition is Seed-based d mapping (formerly Signed differential mapping) or SDM is a statistical technique created by Joaquim Radua for meta-analysis studies assessing differences in brain activity or structure via neuroimaging techniques such as fMRI, VBM, DTI or PET.

Manages Complexity

Seed-based d mapping compresses multiple crossdomainmodelsstructuresrepresentations details into a stable diagnostic relation. The source shows both the central mechanism—another relevant feature, introduced in version 2.11, was the use of effect sizes (leading to effect-size SDM or 'ES-SDM'), which allows combination of reported peak coordinates with statistical parametric maps, thus allowing more exhaustive and accurate meta-analyses.—and the practical consequence—the statistical significance of the analyses is checked by.

Abstract Reasoning

1. Type the carrier. Identify the crossdomainmodelsstructuresrepresentations entities to which the claim applies.
2. State the relation. Use the source-grounded identity: Seed-based d mapping (formerly Signed differential mapping) or SDM is a statistical technique created by Joaquim Radua for meta-analysis studies assessing differences in brain activity or structure via neuroimaging techniques such as fMRI, VBM, DTI or PET.
3. Check operation and conditions. Within a study, values obtained by close Gaussian kernels.

Knowledge Transfer

Within the home domain. Knowledge about Seed-based d mapping transfers literally when a new case preserves the same carrier type, relation, and recognition test. SDM adopted and combined various positive features from previous methods, such as ALE or MKDA, and introduced a series of improvements and novel features. One of the new features, introduced to avoid positive and negative findings in the same voxel as.

Neighborhood in Abstraction Space

Seed-based d mapping sits in a sparse region of the domain-specific corpus (87^th percentile for distinctiveness): few abstractions share its structure, so a faithful description tends to retrieve it precisely.

Family — Unclustered & Miscellaneous (2551 abstractions)

Nearest neighbors

• Combinatorial Meta-Analysis — 0.82
• MAP estimator — 0.81
• Jeffreys prior — 0.80
• Bootstrapping populations — 0.80
• Funnel Plot Asymmetry — 0.80

Computed from structural-signature embeddings · 2026-10-08