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 cross_domain_models_structures_representations 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 in brain activity or structure via neuroimaging techniques such as fMRI, VBM, DTI or PET. It may also refer to a specific piece of software created by the SDM Project to carry out such meta-analyses. It is not uncommon in neuroimaging studies that some regions (e.g. a priori regions of interest) are more liberally thresholded than the rest of the brain.

Pre-processing of statistical parametric maps is straightforward, while pre-processing of reported peak coordinates requires recreating the clusters of difference by means of an un-normalized Gaussian Kernel, so that voxels closer to the peak coordinate have higher values. A low variability of the regressor is critical in meta-regressions, so they are recommended to be understood as exploratory and to be more conservatively thresholded. First, coordinates of cluster peaks (e.g. the voxels where the differences between patients and healthy controls were highest), and statistical maps if available, are selected according to SDM inclusion criteria.

For Seed-based d mapping, the abstraction is narrower than the article's general subject matter: a positive case must preserve 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. Retaining only the name, a familiar example, or a downstream effect is insufficient. The specialist roles and tests remain anchored in cross_domain_models_structures_representations, which is why this identity is domain-specific rather than prime.

Structural Signature¶

Sig role-phrases:

Defining carrier — Pre-processing of statistical parametric maps is straightforward, while pre-processing of reported peak coordinates requires recreating the clusters of difference by means of an un-normalized Gaussian Kernel, so that voxels closer to the peak coordinate have higher values.
Constitutive relation — 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.
Operating condition — Within a study, values obtained by close Gaussian kernels are summed, though values are combined by square-distance-weighted averaging.
Recognition evidence — The main statistical analysis is the mean analysis, which consists in calculating the mean of the voxel values in the different studies.
Admissible variation — This mean is weighted by the inverse of the variance and accounts for inter-study heterogeneity (QH maps).
Characteristic consequence — The statistical significance of the analyses is checked by standard randomization tests.
Failure boundary — Values in a Talairach label or coordinate can also be extracted for further processing or graphical presentation.

What It Is Not¶

Not the whole field of cross_domain_models_structures_representations. The node requires the specific identity stated by 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.
Not an over-broad reading. 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 in the same map, thus obtaining a signed differential map (SDM).
Not an over-broad reading. Finally, individual study maps are meta-analyzed using different tests to complement the main outcome with sensitivity and heterogeneity analyses.
Not an over-broad reading. It is not uncommon in neuroimaging studies that some regions (e.g. a priori regions of interest) are more liberally thresholded than the rest of the brain.
Not automatically Standard test image. Retrieval proximity does not establish equivalence; the two identities must be compared by carrier, operation, and failure boundary.

Scope of Application¶

Seed-based d mapping applies literally inside cross_domain_models_structures_representations wherever the source-defined carrier and relation can be established. Its documented habitats include:

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 in the same map, thus obtaining a signed differential map (SDM).
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, thus allowing more exhaustive and accurate meta-analyses.
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 the whole brain was used within each included study.
Pre-processing of studies. A rather large full-width at half-maximum (FWHM) of 20mm is used to account for different sources of spatial error, e.g. coregistration mismatch in the studies, the size of the cluster or the location of the peak within the cluster.

Outside cross_domain_models_structures_representations, the name should be retained only when these same operational conditions survive; otherwise the comparison belongs to the broader parent Pattern or should be marked as analogy.

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. The strongest recognition evidence in the frozen account is: The main statistical analysis is the mean analysis, which consists in calculating the mean of the voxel values in the different studies. A report should distinguish that evidence from a proxy, consequence, or common implementation. It should also state the qualification 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 in the same map, thus obtaining a signed differential map (SDM). so that a reader can reproduce the classification rather than infer it from topical resemblance.

Manages Complexity¶

Seed-based d mapping compresses multiple cross_domain_models_structures_representations 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 standard randomization tests. This compression makes cases comparable while leaving parameters, conventions, exceptions, and evidential quality explicit. It is lossy by design: local history and implementation details may be omitted only when they do not alter the defining relation.

Abstract Reasoning¶

Type the carrier. Identify the cross_domain_models_structures_representations entities to which the claim applies.
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.
Check operation and conditions. Within a study, values obtained by close Gaussian kernels are summed, though values are combined by square-distance-weighted averaging.
Demand recognition evidence. The main statistical analysis is the mean analysis, which consists in calculating the mean of the voxel values in the different studies.
Test variation. Change an implementation or setting while preserving this mean is weighted by the inverse of the variance and accounts for inter-study heterogeneity (QH maps).
Run the collapse test. Remove the defining operation; if the label still seems equally apt, only a topic or correlate was retained.
Reduce cautiously. When the specialist conditions cannot be carried, route the residual comparison to Pattern.

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 seen in previous methods, was the representation of both positive differences and negative differences in the same map, thus obtaining a signed differential map (SDM).

Beyond the home domain. No canonical parent is asserted for Seed-based d mapping. An outside case receives the specialist name only when the same typed roles and rejection conditions can be filled literally; otherwise the comparison remains an analogy pending later graph densification.

Examples¶

Canonical¶

SDM adopted and combined various positive features from previous methods, such as ALE or MKDA, and introduced a series of improvements and novel features. This case is canonical because it supplies a concrete carrier and lets the defining relation be checked rather than merely named.

Mapped back: carrier → the entities in the documented case; operation → 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; recognition evidence → The main statistical analysis is the mean analysis, which consists in calculating the mean of the voxel values in the different studies

Applied / In Practice¶

First, coordinates of cluster peaks (e.g. the voxels where the differences between patients and healthy controls were highest), and statistical maps if available, are selected according to SDM inclusion criteria. The applied case shows how the identity is used under a second setting or qualification while keeping the same operative relation.

Mapped back: changed setting → The seed-based d mapping approachOverview of the method; invariant → 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; boundary → the case exits the class when 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 in the same map, thus obtaining a signed differential map (SDM)

Structural Tensions¶

T1 — Stable identity versus admissible variation. 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 in the same map, thus obtaining a signed differential map (SDM). The tension matters because emphasizing only one side either dissolves the identity or overstates what the evidence and domain conventions warrant.

Diagnostic: Which changes preserve the defining relation, and which replace it?

T2 — Recognition versus proxy. Finally, individual study maps are meta-analyzed using different tests to complement the main outcome with sensitivity and heterogeneity analyses. The tension matters because emphasizing only one side either dissolves the identity or overstates what the evidence and domain conventions warrant.

Diagnostic: Does the cited evidence establish the identity or only a correlated sign?

T3 — Definition versus implementation. It is not uncommon in neuroimaging studies that some regions (e.g. a priori regions of interest) are more liberally thresholded than the rest of the brain. The tension matters because emphasizing only one side either dissolves the identity or overstates what the evidence and domain conventions warrant.

Diagnostic: Is the observed implementation constitutive, optional, or merely common?

T4 — Scope versus overextension. However, a meta-analysis of studies with such intra-study regional differences in thresholds would be biased towards these regions, as they are more likely to be reported just because authors apply more liberal thresholds in them. The tension matters because emphasizing only one side either dissolves the identity or overstates what the evidence and domain conventions warrant.

Diagnostic: Can every claimed application fill the same typed roles without metaphor?

T5 — Transfer versus domain accent. Pre-processing of statistical parametric maps is straightforward, while pre-processing of reported peak coordinates requires recreating the clusters of difference by means of an un-normalized Gaussian Kernel, so that voxels closer to the peak coordinate have higher values. The tension matters because emphasizing only one side either dissolves the identity or overstates what the evidence and domain conventions warrant.

Diagnostic: Does the receiving case instantiate Seed-based d mapping literally, co-instantiate Pattern, or only resemble it?

T6 — Autonomy versus reduction. 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. The tension matters because emphasizing only one side either dissolves the identity or overstates what the evidence and domain conventions warrant.

Diagnostic: What does Seed-based d mapping distinguish that the broader parent Pattern leaves together?

Structural–Framed Character¶

Seed-based d mapping is mixed or framed-leaning. Its structural side is the repeatable organization summarized by 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. Its framed side is the cross_domain_models_structures_representations vocabulary that fixes the carrier, evidence, exceptions, and admissible transformations.

Evaluative weight: the identity can be stated descriptively even when applications carry practical stakes. Human-practice dependence: the source-grounded carrier determines whether the relation exists independently or is constituted by a practice. Institutional origin: disciplinary conventions stabilize the name and test. Vocabulary portability: Within a study, values obtained by close Gaussian kernels are summed, though values are combined by square-distance-weighted averaging. Import versus recognition: literal transfer requires the same mechanism; shape alone is analogy.

Its portable skeleton is Pattern. Its character: a recurring specialist identity whose thin organization can be abstracted, while its operational meaning remains domain-bound.

Structural Core vs. Domain Accent¶

What is skeletal. 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. The stable skeleton is the typed relation expressed in that definition and the entry's recognition and collapse tests. The source identifies these operative conditions: Pre-processing of statistical parametric maps is straightforward, while pre-processing of reported peak coordinates requires recreating the clusters of difference by means of an un-normalized Gaussian Kernel, so that voxels closer to the peak coordinate have higher values. 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. It further constrains recognition and variation through: Within a study, values obtained by close Gaussian kernels are summed, though values are combined by square-distance-weighted averaging. The main statistical analysis is the mean analysis, which consists in calculating the mean of the voxel values in the different studies.

What is domain-bound. cross domain models structures representations supplies the operative entities, technical vocabulary, warrants, and exceptions that make Seed-based d mapping literal. Its documented scope includes the condition that SDM adopted and combined various positive features from previous methods, such as ALE or MKDA, and introduced a series of improvements and novel features. Another bounded application condition is that 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 in the same map, thus obtaining a signed differential map (SDM). These are not decorative examples; they determine which carrier and evidence can fill the abstraction's roles.

Why no parent is asserted. Removing those specialist details does not currently yield one live catalog node that is a necessary genus for every instance. The entry is therefore approved as unparented rather than attached by topical resemblance. Its collapse evidence remains specific—This mean is weighted by the inverse of the variance and accounts for inter-study heterogeneity (QH maps).—and future graph densification may discover a defensible relation only if it preserves that boundary.

Approved unparented node. No current live node supplies a defensible necessary genus or structural prerequisite for Seed-based d mapping. The reviewed identity 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. The accelerated suggestion was declined because topical or lexical similarity does not establish hierarchy; the node is admitted without a parent pending later graph densification.
Related reasoning operations. Evidence, representation, comparison, classification, transformation, or evaluation may participate in particular cases, but participation does not make any one of them a necessary parent of every instance.

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

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

Not to Be Confused With¶

Pattern. The parent omits the specialist differentia. Tell: Can the case establish 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?
Standard test image. A shared reference image used by multiple researchers or systems to compare image-processing, compression, reconstruction, transmission, calibration, and quality-assessment results under repeatable conditions. Tell: Which entry's carrier, operation, and failure condition are satisfied?
Concept Drift. A learned rule silently loses validity when the input–outcome relationship it was calibrated on changes underneath it. Tell: Which entry's carrier, operation, and failure condition are satisfied?
Random seed. The initial state value supplied to a pseudorandom generator so that it deterministically produces a reproducible sequence. Tell: Which entry's carrier, operation, and failure condition are satisfied?
A measurement, proxy, or consequence. Those may provide evidence without being the identity. Tell: Would Seed-based d mapping remain present if the detector or downstream effect changed?
A metaphorical analogue. A similar shape outside cross_domain_models_structures_representations lacks the specialist mechanism. Tell: Do the native roles transfer literally, or only the parent Pattern?

References¶

Frozen Wikipedia discovery revision: https://en.wikipedia.org/wiki/Seed-based_d_mapping (revision 1350092673).
Preserved source candidate: http://www.sdmproject.com/

The frozen Wikipedia revision is discovery provenance. The retained source set was reviewed for identity, formal or operational relation, and scope. The encyclopedia's structural synthesis is bounded to those claims; a thin authority surface is recorded as a nonblocking source-strengthening repair rather than concealed.