Wiki: observability and claim ceiling by measurement stack

Weaknesses to be explored in depth

Conventional public pages already knew that connectome alone was not enough, maintenance-state remained, and comparisons should be made using augmentation/ablation. However, this alone leaves room for readers to overinterpret when they see the words multimodal, atlas, connectome, and same-brain, saying, ``I see pretty much everything.'' The weakness is that the discussion ofstate variables andmeasurement stacks have not yet been fully integrated.

Therefore, on this page, we will integrate the direct observables of each stack, what can be said a little more strongly, what will still remain in latent state, and the claim ceiling allowed by this site into one table.

Observability and claim ceiling per measurement stack

Why ceiling is so different

1. atlas strengthens identity but does not directly provide current state

Yao et al. combined scRNA-seq and MERFISH on whole mouse brain and presented a high-resolution atlas consisting of 34 classes, 338 subclasses, 1,201 supertypes, and 5,322 clusters. This is a major step forward in terms of cell-type taxonomy and spatial location. However, what we can say directly from this is which molecular class the cell belongs to and where it is located, but does not includethe current moment's threshold, gain, synaptic efficacy, sleep-dependent renormalization, or transmitter occupancy. So while atlas is very important, it is safe to read it first as identity prior.

2. Patch-seq is a bridge but does not erase the coverage wall

Gouwens et al. showed that morpho-electric variation remains continuously within the transcriptomic family, and Gamlin et al. mapped MET-types defined by Patch-seq to large-scale EM and showed differences in myelination and synaptic output for each Sst MET-type. This means that cell-type label alone is not enough; adding electrophysiology and morphology is of great value. On the other hand, Patch-seq is sparse and destructive sampling and does not provide the whole brain current state or longitudinal history of the same individual. Therefore, it is abridge and notwhole-brain completeness.

3. EM connectome is a scaffold but does not freeze dynamic state

Dorkenwald et al.'s adult fly whole-brain connectome is a huge step forward, reorganizing approximately 5 × 10⁷ chemical synapses and 139,255 neurons. However, EM is strong because it is a structural scaffold, and it is not a method that directly measures current weight, release probability, neuromodulatory context, and glial/metabolic background. The criticism that follows from this is simple: we must not equate knowing the wiring with knowing the generative state of the moment.

4. Adding same-brain function reduces degeneration, but does not by itself fix labels, current synaptic state, or unique dynamics

MICrONS combines dense calcium imaging, behavioral states, and a later EM connectome in the same brain, and the linked analyses show that same-brain local circuits can support stronger structure-function correspondences and connectome-constrained conditional prediction. This is a real advance over connectome-only. However, the measurable object is still a sequential, local, and regime-bounded bridge, not a same-time whole-brain state sample. It is therefore too weak to treat the label same-brain functional connectomics as one solved class. The remaining walls are not only whole-brain coverage and maintenance-state, but also whether transcriptomic labels were directly measured or morphology-bridged, whether the result constrains current synaptic efficacy / release state / active-zone architecture, and whether the resulting dynamical explanation is unique rather than one member of a still-degenerate family. Therefore, the ceiling of this stack on this site is a sequential local structure-function scaffold or local conditional-prediction route, not current synaptic-state, presynaptic release-machinery readout, or a whole-brain twin.

4.5. Same-brain functional connectomics is a sequential local scaffold, not current synaptic-state or a unique whole-brain twin

Another weakness that remained on this page was that same-brain functional connectomics could still be overread as if one frontier stack had already solved specimen identity, direct cell typing, current synaptic state, presynaptic release machinery, and dynamical identifiability in one move. That is too weak. Bosch et al. (2022) showed a strong correlative workflow from in vivo physiology to synchrotron microtomography and volume EM, but through a multistage landmark-based route. MICrONS Consortium et al. (2025) then showed a same-brain local pipeline that can support rich structure-function analysis, and Ding et al. (2025) showed that same-brain functional connectomics can reveal a generalized local wiring rule. But Gamlin et al. (2025) still mapped predicted transcriptomic types through morphology-based classification rather than direct transcriptomic readout inside the EM volume. And the remaining latent state is not trivial: Holler et al. (2021), Molnár et al. (2016), Sakamoto et al. (2018), Dürst et al. (2022), Emperador-Melero et al. (2024), and Mittermaier et al. (2024) show that current synaptic efficacy, release probability, active-zone architecture, and membrane-state-gated consolidation are not exhausted by structure-function correspondence, while Beiran & Litwin-Kumar (2025) show that connectome-constrained recurrent networks can still remain dynamically degenerate until extra recordings collapse the solution space. What follows directly is that same-brain functional connectomics is a strong local scaffold, but not a solved route to current synaptic-state, presynaptic release-machinery truth, direct transcriptomic truth, or unique whole-brain dynamics.

4.6. Destructive ultrastructure still faces a preservation / registration / throughput wall

Another weakness that remained on this page was that volume EM, petascale, or same-brain could still be overread as if destructive ultrastructure had already solved native-state preservation, whole-brain scaling, and reconstruction quality in one move. That is too weak. Lu et al. (2023) showed that conventional aldehyde fixation collapses extracellular space, that the fixation time course itself is not instantaneous, and that high-pressure freezing preserves extracellular space only in samples thinner than roughly 200 μm. Shapson-Coe et al. (2024) then showed that a rapidly preserved human sample can yield a remarkable nanoscale reconstruction, but still as a 1.05 mm³ surgical fragment with 1.8 PB raw data and 326 days of imaging. MICrONS Consortium et al. (2025) showed that same-brain function plus EM is a sequential local pipeline rather than simultaneous state capture, and Dorkenwald et al. (2024) showed that even the adult fly whole-brain frontier still depended on proofreading, thresholding, and substantial manual correction effort. What follows directly is that resolution alone does not erase preservation artifacts, local registration limits, or reconstruction burden.

4.7. Human diffusion MRI tractography is a route-conditioned macro pathway prior, not one stable graph

Another weakness that remained on this page was that connectome, dMRI, or tractography graph could still be overread as if a living-human structural stack already produced one stable object. That is too weak. Reveley et al. (2015) showed that superficial white matter can impede detection of long-range cortical connections, and Schilling et al. (2018) showed that tractography endpoints remain biased toward gyral crowns across algorithms and diffusion models. The newer literature then shows that even after tracking, the graph itself still moves. Gajwani et al. (2023) showed across 40 pipelines and 44 group-representative reconstructions that hub location is highly variable, and He et al. (2024) showed that tractogram filtering can significantly change connectome laterality. Upstream of that, McMaster et al. (2025) showed that voxel-size variance changes the resulting connectome and recommended harmonized resampling, while Bramati et al. (2026) showed on the same 3 T scanner with uniform processing that common diffusion-sampling schemes can still shift voxel-wise metrics and tractography outputs. Downstream, Manzano-Patrón et al. (2025) showed that fibre-orientation uncertainty can be propagated into tractography rather than hidden, and Zhu et al. (2025) showed that MRI plus microscopy can improve reconstruction in a hybrid calibration setting. What follows directly is that a diffusion-MRI-derived human connectome is not one stable graph by default; it is an acquisition-, endpoint-, graph-construction-, and calibration-conditioned estimate.

4.8. EEG / MEG still face a visibility / inverse / validation wall

Another weakness that remained on this page was that source-localized, deep-source detectable, or intracranially validated could still be overread as if non-invasive field recordings had already crossed from macro observables into general internal-state recovery. That is too weak. Before any inverse solver runs, there is already a field-formation wall. Ahlfors et al. (2010) quantified source-orientation sensitivity with realistic tissue boundaries and found that the median ratio between the least and most sensitive orientations was 0.63 for EEG but only 0.06 for MEG. Ahlfors et al. (2010) then showed that extended and distributed sources can cancel substantially at the surface. Goldenholz et al. (2009) showed that source extent and anatomy strongly change detectability, with mesial temporal source patches of about 3 cm² versus 8 cm² differing by roughly 10 dB in SNR. Piastra et al. (2021) further showed that ignoring the CSF compartment overestimates EEG SNR and that cortical / subcortical sensitivity depends jointly on depth and orientation. Only after that upstream filter do benchmark papers become readable. Mikulan et al. (2020) created the first open human ground-truth benchmark by combining 256-channel HD-EEG with precisely known intracerebral stimulation sites, but they also stated explicitly that stimulation artifacts are non-physiological and that spatial sampling remains anatomically clustered. Unnwongse et al. (2023) then evaluated 3,619 known stimulation locations in 11 patients with simultaneous SEEG and scalp EEG and found mean localization errors ranging from 10.3 to 26 mm, worsening with source depth and lower skull conductivity. Zauli et al. (2024) showed that hidden interictal discharges not visible on single-trial scalp HD-EEG can be uncovered with simultaneous SEEG-triggered averaging, but the resulting ESI still remained method- and parameter-dependent with localization accuracy of only about 2 cm. Hao et al. (2025) reported that simultaneous HD-EEG/SEEG ictal ESI localizes better than interictal ESI, yet still at 14.07 ± 4.62 mm versus 17.38 ± 4.16 mm, with accuracy strongly influenced by source depth and spike power. Finally, Pizzo et al. (2019) showed that MEG can detect direct hippocampal or amygdalar contributions under simultaneous intracranial validation, but only after blind source separation because the deep contribution reaching the surface was small but significant rather than dominating the sensor signal. What follows directly is that EEG / MEG can gain conditional access to some deeper generators, but they still do not collapse field formation, the inverse problem, or general deep-state observability.

5. Hemodynamic stacks also observe through a vascular transfer state

The weak point that needed another pass was not only that BOLD amplitude could be overread as a neural difference, but that improved hemodynamic papers could still be collapsed into one stronger modality row. That was too weak. Murphy et al. (2011), Williams et al. (2023), and Wu et al. (2023) constrain the transfer-side calibration problem: baseline perfusion and CVR move the amplitude. Özbay et al. (2019) and Bolt et al. (2025) constrain a distinct autonomic / body-coupling route in which large-scale fMRI fluctuations co-vary with physiology rather than transparently reporting neural drive. Epp et al. (2025) then showed that about 40% of task-responsive voxels can display oxygen-metabolism changes opposite in sign to the BOLD response, while Jaroszynski et al. (2025) showed that an oxygen-metabolism route itself already depends on an explicit constrained qBOLD + pCASL model stack. What follows directly is that hemodynamic work must now be split into at least three route families on this site: uncalibrated amplitude, transfer-audited amplitude, and model-conditioned oxygen-metabolism estimation.

6. Neuromodulatory routes form a ladder, not one stack

The weakness that needed another pass was that this page still let pupil / HRV, local transmitter imaging, receptor maps, occupancy PET, and displacement / release-sensitive PET sound closer than they are. That was too weak. Reimer et al. (2016) showed that pupil fluctuations track both adrenergic and cholinergic activity rather than a single transmitter. Lohani et al. (2022) showed that cortical cholinergic signals are spatially heterogeneous across behavioral states, and Neyhart et al. (2024) showed that local ACh depends on axon activity and local clearance kinetics. On the human side, Hansen et al. (2022) and Goulas et al. (2021) showed that receptor maps are structured regional priors, Wong et al. (2013) showed selected D₂-receptor target engagement by an administered drug, and Koepp et al. (1998), Lippert et al. (2019), and Erritzoe et al. (2020) showed challenge- and window-limited dopamine or serotonin release proxies. What follows directly is that neuromodulation is not one measurement class.

The same caution extends to glia. Cahill et al. (2024) showed that local neurotransmitter inputs are minute-long encoded into broad astrocyte networks. That is a real advance, but it is still a slow-state / support-state route, not an automatic shortcut to whole-brain internal-state completeness.

7. Human maintenance-state routes also form a ladder

Another weakness that remained on this page was that it separated generic measurement stacks while still leaving recent human maintenance-state evidence too easy to compress into one sentence such as ``human in vivo observability is getting close.'' That is too weak. Shapson-Coe et al. (2024) pushed up local human nanoscale ultrastructure, Johansen et al. (2024) pushed up a regional synaptic-density atlas, Lucchetti et al. (2025) pushed up a whole-brain biochemical scaffold, Ren et al. (2015) pushed up a ³¹P metabolite / pH balance route, Ren et al. (2017) pushed up a model-conditioned ³¹P MT exchange-flux route, Guo et al. (2024) pushed up a whole-brain ³¹P NAD-content mapping route, Kaiser et al. (2026) pushed up a localized functional ³¹P NAD-dynamics route, Karkouri et al. (2026) pushed up a deuterium metabolite-mapping / absolute-quantification route, and Li et al. (2025) pushed up a deuterium kinetic-rate route. Qian et al. (2012) and Qian et al. (2025) pushed up macro ionic routes, Rzechorzek et al. (2022) pushed up macro thermal routes, van Blooijs et al. (2023) pushed up a tract-scale transmission-speed estimation route, and Baadsvik et al. (2024) pushed up macro myelin mapping. Human support-state evidence then splits again rather than converging into one route: Morgan et al. (2024) plus Padrela et al. (2025) push up a BBB water-exchange route, Chung et al. (2025) pushes up a tracer-specific BBB transport route, Zhao et al. (2020) pushes up a choroid-plexus perfusion route, Petitclerc et al. (2021) push up a blood-to-CSF water-transport route, Anderson et al. (2022) push up a choroid-plexus water-cycling route, Wu et al. (2026) push up an apparent BCSFB-exchange route, and Petitclerc et al. (2026) push up a simultaneous BBB-versus-BCSFB exchange route. Astrocyte PET also splits by route role: Villemagne et al. (2022) push up an SMBT-1 first-in-human MAO-B target-validation route, Villemagne et al. (2022) push up an SMBT-1 AD-spectrum disease-context route, Hiraoka et al. (2025) push up an SMBT-1 brain-quantification route, Mesfin et al. (2026) push up an SMBT-1 whole-body biodistribution route, Matsuoka et al. (2026) push up an SL25.1188 simplified-quantification route, Best et al. (2026) push up an SL25.1188 severity- and smoking-conditioned MAO-B route, and Tyacke et al. (2018) plus Livingston et al. (2022) push up an I2BS astrocyte PET route. Jaisa-Aad et al. (2024) plus Best et al. (2026) show that even MAO-B-linked interpretation remains cohort- and pathology-conditioned. Human neuroimmune PET then splits again rather than converging into one immune lane: Biechele et al. (2023) plus Wijesinghe et al. (2025) push up a TSPO disease-context / validation-bounded PET route, Horti et al. (2022) plus Ogata et al. (2025) push up a CSF1R route-setting PET route, and Yan et al. (2025) pushes up a COX-2 enzyme-defined PET route. Fultz et al. (2019) pushes up a macroscopic CSF-oscillation route, Kim, Huang, & Liu (2025) pushes up a parenchyma-CSF water-exchange route, Eide et al. (2023) pushes up an intrathecal-tracer / CSF-to-blood-clearance-capacity route, Hirschler et al. (2025) pushes up a CSF-mobility MRI route, and Dagum et al. (2026) pushes up a model-based overnight biomarker-efflux route. Human excitability-side evidence then splits again rather than converging into one route: Tallman et al. (2025) push up a local human clinical single-unit allocation route, but with firing explicitly treated as only an indirect excitability index; Huber et al. (2013), Kuhn et al. (2016), and Fehér et al. (2026) push up a sleep-homeostasis / plasticity-recalibration route; and Zrenner et al. (2018) plus Khatri et al. (2025) push up state-gated perturbation routes. What follows directly is that human evidence is layered across proxy classes, not a single near-direct route to current whole-brain maintenance-state. A second correction is also required: those rows are not all equally mature, routine, or deployment-ready, and they do not safely calibrate the same hidden-state family.

8. Proxy-rich human evidence still does not become state-complete by composition

A remaining weakness after separating proxy class, operational maturity, and calibrator role was that a reader could still mentally add the strongest human rows together and conclude that the hidden-state problem is almost solved. The primary literature does not support that shortcut. The rows above live on different spatial units, time windows, and inference layers, and several of the strongest demonstrations are still specialized or model-heavy. The safe reading on this site is therefore not ``the proxies add up to completeness,'' but ``the proxies reduce different error terms and still leave a fusion problem.''

Practical rules arising from this criticism

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