The shortest map
The words on this site can be roughly divided into four levels. These are to observe, arrange, estimate, and confirm. Even if the words seem difficult, confusion will be reduced if you first explain what stage the story is in.
The older beginner route on this site grouped ESI, DCM, and SCM together too loosely as "modeling words." That was too weak. On this site, ESI is read through a validation ladder, DCM through candidate-model disclosure and model recovery, and SCM through intervention conditions and equivalence-class narrowing.
A second beginner overread also remained: more sensors, same-brain linkage, or a connectome prior can sound as if the candidate set were almost closed. On this site, that is still too strong. Same-session multimodal work needs a Fusion Card, sequential same-brain or cross-day claims need a State-Continuity Bridge Card, and connectome-constrained predictors still need an Identifiability Card. The detailed rule lives in Wiki: From observation to estimation.
One more beginner overread still remained: inverse-problem progress could still sound like one continuous bar. The primary literature does not support that shortcut. Ahlfors et al. (2010), Goldenholz et al. (2009), and Piastra et al. (2021) show that field formation and head-model detail already limit what reaches the sensors. Vorwerk et al. (2024) and Vorwerk et al. (2026) show that conductivity uncertainty and estimation still materially move the result. Luria et al. (2024), Tong et al. (2025), and Feng et al. (2025) improve how candidate sets and uncertainty are exposed inside a stated inverse family. Mikulan et al. (2020), Pascarella et al. (2023), Unnwongse et al. (2023), and Hao et al. (2025) then validate different source regimes and error questions. Therefore, on this site, inverse papers are no longer read as one ladder.
One more technical overread still remained: after naming an ambiguity, readers could still think the next step is simply to add more data or one more modality. The primary literature does not support that shortcut. Raue et al. (2011) showed that non-identifiability is resolved by experimental design under suitable conditions or by model reduction matched to the information content of the data. Chis et al. (2016) then showed that sloppiness is not identifiability, so design should optimize identifiability criteria rather than only compress one proxy uncertainty score. White et al. (2016) showed that apparently complementary experiments can instead make omitted mechanisms relevant and increase model discrepancy. Gevertz & Kareva (2024) and Liu et al. (2025) then showed that identifiability analysis and active learning can derive a minimally sufficient schedule rather than an open-ended collection plan. In neuroscience, Beiran & Litwin-Kumar (2025) showed that a small targeted recording set can remove degeneracy in connectome-constrained networks, and Langdon & Engel (2025) showed that preserving causal interactions among task variables can recover behaviorally relevant computation that correlation-only reductions miss. Therefore, on this site, the safer beginner rule is not "collect more" but "name the surviving ambiguity, state which identifiability objective chose the next condition, test whether the new condition exposed omitted-mechanism error, and say what minimum-sufficiency stop rule would end collection." The longer rule lives in Wiki: From observation to estimation and Verification: experiment-design leverage.
The remaining beginner weakness was subtler: this page already stopped readers from treating DCM as generic causal wording, but it still left room to read recent DCM papers as if they formed one monotonic ladder of causal strength. The primary literature does not support that shortcut. Penny et al. (2004) and Rosa et al. (2012) strengthen candidate-model comparison and family search. Frässle et al. (2021) and Wu et al. (2024) strengthen tractability and scaling. But Frässle et al. (2016), Almgren et al. (2020), Zhang et al. (2024), and Ma et al. (2024) show that reliability, priors, processing policy, scan duration, and sample size still materially move the result. Therefore, on this site, DCM advances are read by axis, not as one progress bar.
The next weakness was not inside one route, but between routes. This page already taught that observation, estimation, and verification are different stages, yet it still left one high-cost ambiguity: readers could move from same-session multimodal, same-subject proxy-rich, or same-brain sequential wording to the vague idea that measurement itself became stronger. The current primary literature does not support that compression. Kothe et al. (2025), Vafaii et al. (2024), Chen et al. (2025), Bolt et al. (2025), and Epp et al. (2025) show why synchronized or coupled modalities still do not define one temporal object or one biological quantity by default. Li et al. (2025), Bøgh et al. (2024), Morgan et al. (2024), Amiri et al. (2023), and Manasova et al. (2026) show why several living-human rows still differ in quantity type, operating point, complete-case availability, and disagreement topology. Bosch et al. (2022), MICrONS Consortium et al. (2025), Gallego et al. (2020), Van De Ville et al. (2021), Karpowicz et al. (2025), Wilson et al. (2025), and Wairagkar et al. (2025) show why specimen identity or stable use across time still does not tell you which carried object remained the same. Therefore, this page now separates route cards from companion cards instead of letting them blur together under one generic verification label.
View in 4 levels
| stage | Words that are easy to appear here | What are you doing |
|---|---|---|
| 1. Observation | EEG, MEG, fMRI, ECoG | We first measure the signals coming out from the brain and body. |
| 2. Organize | QC, pretreatment, BIDS | Check for noise and defects and arrange it into a shape that others can follow. |
| 3. Estimation | Inverse problem, ESI, DCM, SCM | Think about how far you can estimate the state and causal structure in the brain from observations, and which route card fixes the ceiling. |
| 4. Verification | Benchmark, baseline, pre-registration, model card | Check whether the estimation or model really holds true in a comparable manner. |
Three companion cards that still had to be split
This page now uses a stricter distinction. A route card describes one measurement or estimation route and its ceiling. A companion card describes the relation among several routes or stages when one route no longer explains the claim by itself. The scientific reason is simple: current primary literature does not support treating multimodal, proxy-rich, and same-brain as if they were one generic upgrade in evidence strength.
| Claim pattern that still gets overread | Why the shortcut is scientifically unsafe | Primary-literature stop rule | Companion card required on this site |
|---|---|---|---|
| Same-session multimodal / atlas-informed claim | Shared timestamps, a shared factor, and one externally grounded biological quantity are different achievements. Same-session or atlas-informed wording does not by itself prove one temporal object or one quantity bridge. | Kothe et al. (2025), Vafaii et al. (2024), Chen et al. (2025), Bolt et al. (2025), and Epp et al. (2025) show why temporal-kernel mismatch, shared-vs-specific structure, autonomic coupling, and quantity-bridge failure remain separate burdens. | Fusion Card |
| Several living-human proxy rows in one argument | Several real human routes can still differ in quantity type, evidence role, operating point, complete-case geometry, and disagreement topology. Listing them together does not yet make one same-subject state sample. | Li et al. (2025), Bøgh et al. (2024), Morgan et al. (2024), Amiri et al. (2023), and Manasova et al. (2026) show why route-local repeatability, method-family non-equivalence, restricted complete-case slices, and disagreement in hard groups remain separate audits. | Human Proxy Composition Card |
| Same-subject / same-brain sequential bridge | Specimen identity, local structure-function linkage, or stable interface use do not by themselves tell you which object stayed the same across time, regime, or tissue transformation. | Bosch et al. (2022), MICrONS Consortium et al. (2025), Gallego et al. (2020), Van De Ville et al. (2021), Karpowicz et al. (2025), Wilson et al. (2025), and Wairagkar et al. (2025) show why stable use can still depend on alignment, recalibration, local witness objects, or short-horizon support. | State-Continuity Bridge Card |
If the claim fails because one route hid its own assumptions, the missing object is a route card. If the claim fails because several routes or stages were silently fused into one argument, the missing object is a companion card. This page now keeps those failure modes separate on purpose.
1. Observation: First get the signal
EEG and MEG do not directly look inside the brain, but rather measure signals that can be observed from outside. The important point here is thatwhat you observe is not the same as what is really happening inthe brain.
| Term | In one word |
|---|---|
| EEG | This is a method to quickly measure the potential difference on the scalp. While it is resistant to changes over time, it is easily blurred spatially. |
| MEG | This is a method of measuring magnetic fields. Although it is complementary to EEG, it is expensive and has significant equipment limitations. |
| fMRI | This is a method to measure changes in blood flow. It is strong in position, but slow in time resolution. |
| ECoG | This is an invasive measurement that measures near the brain surface. Although it is highly accurate, there are strong restrictions on the applicable range. |
2. Organize: Don't believe the signal as it is
The observed signals include blinks, myoelectricity, body movements, equipment noise, etc. Therefore, the next step is QC and pre-treatment. This is not a matter of improving the appearance, but of recording what information has been kept and what has been removed.
Words used here
- QC:Leave missing, noise, artifact, and exclusion reasons in numerical form.
- Preprocessing: Set up reference methods, filters, artifact removal, etc.
- BIDS:A standard for aligning data and metadata in a way that others can track them.
If you skip this step, even if a high-performance model comes out later, it will not provide comparable evidence.
3. Estimation: How much can we tell from observations
We want to estimate brain activity and causal structure based on the organized signals. This is where inverse problems, ESI, DCM, and SCM come into play. However, it must be remembered at this stage thatthe estimate is an estimate, and uncertainty and candidate model dependence remain.
| Term | What it adds | What still has to be disclosed |
|---|---|---|
| Inverse problem | This is the general family of routes that estimate hidden causes from externally observed signals. | The solution is not unique by default, so field visibility, forward-model or conductivity burden, solver uncertainty, and validation class remain part of the result. |
| ESI | A concrete inverse workflow that combines a head model, source prior, and estimation rule to produce candidate source configurations. | One polished map is not enough; disclose field visibility, forward-model burden, cross-solver or posterior spread, and the validation class or source regime that was actually tested. |
| DCM | A framework for comparing candidate generative circuit models and asking which one better explains the observation. | The result still depends on the candidate model space, priors, family comparison, recovery, and external validation. |
| SCM | A language for making interventions and counterfactuals explicit. | With observational data alone, equivalence classes often remain, so intervention design still determines how strong the causal claim can become. |
| Inverse-problem gate | What question it answers | Representative primary literature | What it still does not close |
|---|---|---|---|
| Gate 1: Field-formation visibility | Does the target source class generate a usable scalp field under the actual orientation, extent, anatomy, and head-model detail? | Ahlfors et al. (2010); Goldenholz et al. (2009); Piastra et al. (2021) | A visible source class can still remain poorly localized, poorly identified, or weakly validated. |
| Gate 2: Forward-model / conductivity burden | How much do skull or tissue conductivity and geometry assumptions move localization, depth, magnitude, or orientation? | Vorwerk et al. (2024); Vorwerk et al. (2026) | Reducing conductivity-driven spread does not by itself collapse solver degeneracy or prove source recovery in every regime. |
| Gate 3: Solver-family / posterior uncertainty | Does the method expose alternative source configurations, intervals, or extended-source uncertainty instead of one polished point map? | Luria et al. (2024); Tong et al. (2025); Feng et al. (2025) | Better uncertainty exposure does not repair missing observability, wrong head models, or unmatched validation classes. |
| Gate 4: Validation class / source regime | Which error question was actually tested: known stimulation site, focal-source board, simultaneous invasive concordance, or clinical ictal localization? | Mikulan et al. (2020); Pascarella et al. (2023); Unnwongse et al. (2023); Hao et al. (2025) | Validation success in one regime is not a universal winner for focal, extended, spontaneous, and deep-source recovery together. |
Observing an EEG is not the same as uniquely reconstructing brain states. Furthermore, being correct in a correlational prediction is not the same as knowing the causal structure.
DCM is a comparison of candidate generative models, and SCM is a language that facilitates describing interventions and counterfactuals. Causal equivalence classes often remain from observational data alone, so it is necessary to read the candidate model space, family comparison, external validation, and the presence or absence of intervention data separately. For more information, see Wiki: From observation to estimation.
Michel & Brunet (2019) summarize ESI as a multi-step pipeline rather than a one-word method. On top of that, Ahlfors et al. (2010), Goldenholz et al. (2009), and Piastra et al. (2021) show that field formation is already selective before inversion begins, Vorwerk et al. (2024) and Vorwerk et al. (2026) show that conductivity assumptions still move the result, and Luria et al. (2024), Tong et al. (2025), and Feng et al. (2025) show why uncertainty has to be exposed rather than hidden. Finally, Mikulan et al. (2020), Pascarella et al. (2023), Unnwongse et al. (2023), and Hao et al. (2025) validate different source regimes. On this site, a claim that says only "we used ESI" still does not say enough.
Penny et al. (2004) fixed that DCM inference is relative to the compared model set, Rosa et al. (2012) showed how post-hoc model-space search can be expanded, and Frässle et al. (2021) plus Wu et al. (2024) pushed whole-brain and faster estimation. Those are advances in tractability, not automatic solutions to identifiability. On this site, DCM therefore remains a model-conditioned causal hypothesis unless candidate space, observed-subsystem closure / latent-confound audit, node-definition policy, sampling / transformation sensitivity, recovery, reliability, and validation are disclosed.
| DCM axis | What it actually strengthens | What it still does not close |
|---|---|---|
| Candidate-model comparison / family search | Penny et al. (2004); Rosa et al. (2012). Stronger comparison among explicitly declared competitors. | It does not prove that omitted nodes, edges, priors, or model families were absent or irrelevant. |
| Scaling / tractability | Frässle et al. (2021); Wu et al. (2024). Larger or faster search within a declared DCM family. | It does not turn the graph into preprocessing-invariant, node-invariant, or competitor-complete causal truth. |
| Processing / first-level design robustness | Almgren et al. (2020); Zhang et al. (2024). Stronger disclosure of how GSR, GLM design, contrast definition, and thresholding change the inferred edges and parameter certainty. | It does not let one reasonable pipeline stand in for pipeline-robust effective connectivity. |
| Reliability window | Frässle et al. (2016); Ma et al. (2024). A bounded statement about how stable the result remains under named priors, session structure, scan duration, and sample size. | It does not show that the same graph will survive different sites, longer horizons, weaker scans, or changed processing policies. |
This page is the beginner map. If you need the full DCM / effective-connectivity submission rule, including latent-confound audit, node-definition policy, and abstention boundary, continue to Wiki: From observation to estimation.
4. Verification: How to trust estimates
The final question is, "Can other people confirm this estimation or model under the same conditions?" This is where words like Benchmark, Baseline, Preregistration, and Model Card come into play.
| Term | What is it needed for |
|---|---|
| Benchmark | Fix what will be compared and what indicators will be used to score. |
| Baseline | Places a starting point for advocating for improvements. |
| Pre-registration | Avoid changing the conditions later. |
| Model card | In addition to the score, we will also publish weaknesses, failure examples, leak countermeasures, and calculation conditions. |
| Experiment-design leverage | Name which surviving ambiguity the next measurement or perturbation targets, why it was chosen by the stated identifiability objective, and what minimum-sufficiency stop rule would end further collection. |
| Route card | When ESI, connectivity, or DCM is used, we disclose the assumptions, validation class, abstention boundary, and what the result still does not identify. |
| Companion card | When a claim spans several routes or stages, we disclose whether the unresolved burden is fusion, human-proxy composition, or state continuity, instead of hiding that relation behind words such as multimodal, same-subject, or same-brain. |
On this site, verification is no longer only a place to list what was measured. When ambiguity remains, the stronger workflow must also explain which ambiguity class survived, which identifiability objective selected the next condition, whether the new condition exposed omitted-mechanism error, and what minimum-sufficiency design would have been enough. Otherwise, even a careful benchmark can still look like open-ended data accumulation rather than an ambiguity-breaking design.
References
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What has been learned from this process and what is still unknown
| What we know | What we still don't know |
|---|---|
| Which stage of work does the term belong to? | Which model ultimately adequately explains consciousness and identity? |
| How to read without confusing observation, estimation, and verification. | Is it possible to obtain sufficient information for WBE with non-invasive measurements alone? |
| Why are BIDS and QC part of the technology rather than the outside? | Which multimodal integration is ultimately best? |
| Why inverse-problem papers must be separated into visibility, forward-model burden, solver uncertainty, and validation class. | Which inverse route or validation ladder generalizes beyond focal or clinical benchmark regimes. |
| Why route cards and companion cards must be separated before multimodal, proxy-rich, or same-brain language is read strongly. | Which combination of Fusion, Human Proxy Composition, State-Continuity Bridge, and Identifiability cards should become the site's default front-door bundle for cross-stack claims. |
Where to go back next
Please use Glossary to return to a short definition, Introduction to EEG to read the role of EEG again, and Verification infrastructure to proceed to comparable verification.