Wiki

Wiki: Mind Upload / WBE Basics

Divide the large dream into smaller questions that can be checked.

Mind Uploading Research Project

Public Page Updated: 2026-04-03 Beginner guide (updated with the 2026-04-03 neuromodulatory family split)

How to use this page

Read this first to avoid getting lost

This page explains, from the beginning, what Mind Upload and WBE are trying to do and why strong claims must be broken into smaller technical questions before they can be judged. The emphasis is not on quick belief or disbelief, but on evidence class, verification, and claim ceiling.

  • Mind upload and WBE are related, but the strength of the claim still depends on what "reproduced" means.
  • Similar output is not the same as similar internal mechanism, state, or causal organization.
  • Recent literature makes it unsafe to read strong-looking decode, tractography, effective-connectivity, thermodynamic, or closed-loop headlines without route cards.
  • A human tractography connectome is not one stable beginner object either; acquisition, cortical endpoint assignment, graph construction, uncertainty, and external calibration can all change what the graph means.
  • An effective-connectivity graph is still a model-conditioned causal hypothesis unless observed-subsystem closure / latent-confound audit, node-definition policy, sampling / transformation sensitivity, validation, and reliability window are disclosed.
  • Same-brain functional connectomics and MICrONS-side digital-twin language now stay behind a beginner ceiling of sequential local scaffold plus stimulus-conditioned predictor, not a solved local twin or a presynaptic release-machinery readout.
  • Low latency is not the full body / environment boundary; fast-loop disclosure and slow internal-milieu disclosure now stay separate even on this beginner page.
  • A chemical connectome plus nominal inhibitory edges still does not fix shared extracellular / electrical state, because electrical coupling, extracellular-space geometry / diffusion barriers, and osmotic regime remain separate variables.
  • Human evidence now has to be read in order: family-internal comparison family first, then proxy class, operational maturity, and safe calibrator role.
  • Human neuromodulatory evidence is not one beginner row: mixed-arousal proxy, receptor / transporter atlas prior, occupancy PET, and challenge-linked release PET do not justify the same claim.
  • Regional synaptic-density PET and same-brain scaffold evidence still do not fix release-site number, docked-vesicle architecture, active-zone nanostructure / priming-site assembly, or current release competence.
  • On the human side, quantity type and route role are part of the claim: five-metabolite 1H-MRSI similarity, high-resolution 1H-MRSI metabolite-distribution mapping, 31P-MRS metabolite / pH balance, 31P MT exchange-flux, 31P NAD-content mapping, 31P functional NAD-dynamics, deuterium metabolite-mapping / absolute-quantification routes, deuterium kinetic-rate imaging, myelin-water, MT-family, bilayer-sensitive MRI, BBB water-exchange MRI, tracer-specific BBB PET, SMBT-1 MAO-B target-validation / AD-spectrum / brain-quantification / whole-body-biodistribution routes, I2BS PET, macroscopic CSF oscillation, parenchyma-CSF water exchange, intrathecal tracer retention / CSF-to-blood clearance capacity, CSF-mobility MRI, and model-based biomarker efflux are not interchangeable routes.
  • Human energetic imaging and astrocyte-related PET still do not directly identify glial substrate-routing: lactate-shuttle support, starvation ketone-body export, intensive-learning glia-to-neuron fatty-acid flux, and apoE / sortilin-dependent lipid delivery remain separate claim families with different supplier-sink logic and human observability ceilings.
  • Transcription / chromatin state is not the whole molecular maintenance layer either: post-transcriptional RNA-state, phospho-signaling / second-messenger state, local proteostasis / synaptic-tagging state, and cargo-transport / cytoskeletal trafficking state remain separate hidden-state families with different observables, compartments, timescales, and human ceilings.
  • Same-subject proxy bundles and sequential bridges still need composition and state-continuity audits before they are read as one state sample.
  • This site therefore builds verification infrastructure first instead of jumping directly to final conclusions.
Helpful next pages
Introduction to WBE Verification platform FAQ
Best for
People who have heard the term mind upload and want the technical big picture first
Reading time
12-15 minutes
Accuracy note
This page stays on the technology and natural-science side. Unresolved issues are kept unresolved rather than softened with vague language.

Relatively clear at this stage

What we know now

  • Some brain signals can already be measured and decoded for specific tasks under bounded conditions.
  • Without standards, audits, and explicit claim ceilings, progress cannot be compared honestly.
  • A strong-looking result can still be heavily conditioned on task structure, priors, candidate models, or omitted loops.
  • A living-human tractography connectome is still an acquisition-, endpoint-, graph-, uncertainty-, and calibration-conditioned estimate rather than one stable graph that is merely coarse.
  • Effective-connectivity results are still conditioned not only on candidate model space but also on omitted drivers, node-definition policy, sampling / observation transform, and the window in which reliability was shown.
  • Same-brain functional connectomics can strengthen local structure-function correspondence while still leaving predicted-versus-direct labels, release-site number, active-zone nanostructure / priming-site assembly, current release competence, and dynamical identifiability unresolved.
  • Closed-loop papers can preserve a fast local loop while still leaving circadian phase, glucocorticoid state, insulin / metabolic regime, and other slow internal-milieu variables unmatched or latent.
  • Even with the same chemical synapse graph, gap-junction coupling, endogenous field effects, extracellular-space geometry / dilution, and human wakefulness-linked extracellular-space clues remain separate evidence classes.
  • Human neuromodulatory evidence also splits internally: mixed-arousal proxy, receptor / transporter atlas prior, occupancy PET, and challenge-linked release PET do not measure the same object or justify the same beginner interpretation.
  • Current human energetic routes and astrocyte-related PET can calibrate bounded macro or target-defined proxies, but they still do not tell us which glial supplier, fuel object, and neuronal sink carried the operative substrate route in vivo.
  • Even when transcript or protein abundance looks informative, molecular maintenance still splits into transcription / chromatin, post-transcriptional RNA, phospho-signaling, local proteostasis / synaptic-tagging, and cargo-routing families rather than one generic molecular-state row.
  • Regional SV2A PET can calibrate a bounded density-comparison family, but it still does not tell us the operative release-site number, docked-vesicle architecture, active-zone nanostructure / priming-site assembly, or current release competence.
  • Recent human-side advances reduce different uncertainty terms, but mixed-arousal proxy, receptor / transporter atlas prior, occupancy PET, challenge-linked release PET, local ultrastructure, synaptic-density PET, five-metabolite 1H-MRSI similarity scaffolds, high-resolution 1H-MRSI metabolite-distribution mapping, 31P-MRS metabolite / pH balance routes, 31P MT exchange-flux routes, 31P NAD-content mapping, 31P functional NAD-dynamics, deuterium metabolite-mapping / absolute-quantification routes, deuterium kinetic-rate imaging, quantity-defined myelin MRI, BBB water-exchange MRI, tracer-specific BBB PET transport, SMBT-1 MAO-B target-validation / AD-spectrum / brain-quantification / whole-body-biodistribution routes, I2BS PET, macroscopic CSF oscillation, parenchyma-CSF water exchange, intrathecal tracer retention / CSF-to-blood clearance capacity, CSF-mobility MRI, and model-based biomarker efflux are not one near-direct readout.

Still unresolved beyond this point

What we still do not know

  • It remains unresolved whether functional reproduction would guarantee identity or phenomenal consciousness.
  • It is not yet determined which measurement set would be sufficient for WBE-level internal-state capture.
  • How current human proxy classes could be fused into same-subject, externally calibrated state identification remains unresolved.
  • Social deployment questions cannot be solved by technical progress alone.

Learn the basics

Check the basics in the wiki

What the wiki is for

The wiki is a learning aid. For the project's official current synthesis, success criteria, and operating rules, always return to the public pages.

First in one sentence

Mind uploading asks whether a person's brain-relevant processes could be preserved on another substrate. WBE is the technical research program that tries to make parts of that question operational. The missing step is that success is not automatic just because a system produces impressive outputs.

What this site does first

Mind-Upload does not begin by declaring that WBE is either near or impossible. It begins by fixing what must be measured, what must be disclosed, what must be reproduced, and where the claim ceiling still sits. That is why this site builds a verification commons first.

2026-03-19 beginner update: why the front door had to become stricter

The older beginner route was directionally correct, but it still left too much room for readers to treat high score, one graph, one model, or one real-time demo as if they were all one kind of progress. The recent literature does not support that shortcut. Decode, tractography, effective connectivity, irreversibility, and closed-loop results now have to be read through different route cards on this site.

2026-03-28 technical correction: even the tractography graph is not one stable beginner object

The tractography route at the beginner front door also had to become stricter. Gajwani et al. (2023) showed that hub location varies across 40 pipelines and 44 group-representative reconstructions, He et al. (2024) showed that tractogram filtering can move structural laterality for more than 10% of connections, McMaster et al. (2025) and Bramati et al. (2026) showed that voxel size and q-space sampling change the resulting connectome, and Manzano-Patrón et al. (2025) plus Zhu et al. (2025) showed that uncertainty propagation and MRI-microscopy fusion improve only parts of the tractography chain rather than fixing MRI-alone tractography. Therefore, even a beginner summary on this site now has to say that a human tractography connectome is an acquisition-, endpoint-, graph-construction-, uncertainty-, and calibration-conditioned estimate, not one stable graph that merely happens to be coarse. The shortest follow-up is Wiki: tractography route card.

2026-03-29 technical correction: a directed graph still needs more than candidate-model disclosure

The beginner route also had to become stricter about effective connectivity. Smith et al. (2011) showed that lag-based fMRI methods perform poorly and that functionally inaccurate ROIs are especially damaging to network estimation. Barnett & Seth (2017) showed that subsampling can create detectability black spots and sweet spots for Granger-causal interactions. Vink et al. (2020) showed that resting-state EEG functional connectivity explains less than 10% of TMS-evoked propagation variance. Villaverde et al. (2019) foregrounded full input-state-parameter observability instead of assuming the observed subsystem is effectively closed, Novelli et al. (2025) showed that realistic HRF variability alone need not force false positives while slow BOLD sampling can still induce spurious Granger-causal inference, Jafarian et al. (2024) showed that reliability can be strong within tightly matched MEG sessions, and Yan et al. (2026) showed that latent confounders remain an active reconstruction problem. Therefore, this beginner page now treats a DCM or effective-connectivity graph as a model-conditioned causal hypothesis unless it also discloses observed-subsystem closure / latent-confound audit, node-definition policy, sampling / transformation sensitivity, perturbation or external validation, and a reliability window. The shortest follow-up is Wiki: effective-connectivity route card and Verification: Observability Budget.

2026-03-27 technical correction: quantity type now belongs at the beginner front door

The beginner route also had to become stricter about human evidence itself. Local human ultrastructure, SV2A PET, whole-brain 1H-MRSI, 31P-MRS, deuterium metabolic imaging, myelin MRI, BBB MRI / PET, astrocyte-related PET, and CSF / glymphatic routes are all real advances, but the modality label is still too coarse. On this site, a beginner summary now has to say whether the route is a five-metabolite parcel-similarity scaffold, a high-resolution 1H-MRSI metabolite-distribution map, a 31P metabolite / pH balance route, a 31P MT exchange-flux route, a 31P NAD-content mapping route, a 31P functional NAD-dynamics route, a deuterium metabolite-mapping / absolute-quantification route, or deuterium kinetic-rate imaging; myelin-water, MT-family, bilayer-sensitive, or qT1 remyelination-sensitive; BBB water-exchange or tracer-specific BBB transport; SMBT-1 MAO-B target validation / AD-spectrum / brain quantification / whole-body biodistribution or I2BS astrocyte-related PET. Those rows do not carry the same model burden, do not constrain the same molecular object, and do not calibrate the same hidden-state family. The beginner questions are therefore now what proxy class, quantity type, and route role does this route constrain?, how specialized, small-cohort, or model-heavy is the route still?, and what bounded calibrator role can it safely play? Even after that, multimodal bundles and same-subject workflows still need composition and bridge audits before they are read as one state sample. The shortest follow-up is WBE 101: human observability ladder, Wiki: Human Proxy Composition and Route Maturity, Wiki: State-Continuity Bridge, and Wiki: observability and claim ceiling by measurement stack.

2026-04-01 technical correction: astrocyte PET also splits by route role

The beginner route had already learned to split MAO-B from I2BS, but the current primary literature requires one more step. Villemagne et al. (2022) is a first-in-human SMBT-1 MAO-B target-validation route with more than 85% selegiline blockade, Villemagne et al. (2022) is an AD-spectrum disease-context route across 77 volunteers, Hiraoka et al. (2025) is a brain-quantification route with arterial-sampling and model-comparison burden, Mesfin et al. (2026) is a whole-body biodistribution route in six healthy volunteers over 5.5 h, and Tyacke et al. (2018) plus Livingston et al. (2022) are I2BS routes rather than MAO-B routes. Best et al. (2026) and Jaisa-Aad et al. (2024) further show that even MAO-B-linked interpretation remains cohort-, smoking-, severity-, and pathology-conditioned. Therefore, even on a beginner page, astrocyte PET has to be typed by both target and route role before it is allowed to act as a human proxy.

2026-04-02 correction: synaptic-density is not presynaptic release machinery

The beginner route also had to become stricter about what a shorthand like current synaptic state hides. Johansen et al. (2024) gives a high-resolution healthy-human SV2A atlas, Smart et al. (2021) showed that brief visual activation does not measurably change [11C]UCB-J binding, and Holmes et al. (2022) found no measurable overall SV2A change 24 h after ketamine despite symptom improvement. Meanwhile, Molnár et al. (2016) showed that human synapses can contain multiple docked vesicles and multivesicular release, Sakamoto et al. (2018) showed that Munc13-1 assemblies set independent release sites, and Emperador-Melero et al. (2024) showed that CaV2 clustering and vesicle priming are executed by distinct active-zone machineries. Therefore, even a beginner page now has to keep regional synaptic-density proxy, release-site number, docked-vesicle architecture, active-zone nanostructure / priming-site assembly, and current release competence on separate rows.

Three words to align first

Word What it means here
Mind Upload The broad story of preserving or reproducing mind-relevant processes on another substrate.
WBE The technical program of reproducing enough brain-relevant function and internal organization on a different computational basis.
Verification Commons The shared standards, benchmarks, route cards, logs, and audits that let other people judge the same claim in the same way.

What is relatively clear now / what is still unresolved

Relatively clear Still unresolved
Some brain signals can already be decoded or controlled for specific tasks under bounded experimental conditions. That does not by itself establish preserved identity, consciousness, or WBE-level internal-state capture.
Public standards, benchmarks, registries, and audits make progress more comparable. There is still no agreement on which measurement and verification package would be sufficient for strong WBE claims.
Current technical progress comes in different evidence classes, and each class reduces a different uncertainty. It remains unknown how far any future functional reproduction would justify continuity claims about a person.

Why even a beginner page now needs a human observability ladder

The older beginner summary was still too coarse because it let very different human-side advances sound like one generic increase in observability. The recent primary literature does not support that compression. The safer beginner reading is to fix the family-internal comparison family first, and only then ask three questions: which proxy class, quantity type, and route role does the route constrain?, how specialized or model-heavy is the route still?, and what bounded hidden-state family can it safely calibrate?

2026-04-03 correction: family-internal comparison family comes before the three axes

The beginner route still had one meaningful compression left inside human proxy language. The current primary literature does not support treating human neuromodulatory evidence as one reusable row. Carro-Domínguez et al. (2025) used pupil size during human sleep as a mixed arousal proxy, not as a transmitter-specific readout. Hansen et al. (2022) built a cortical receptor / transporter atlas prior from more than 1,200 healthy individuals, and Nakuci & Bansal (2025) used those atlas maps as a modeling scaffold for spontaneous BOLD activity rather than a same-subject current-state readout. Wong et al. (2013) constrained D2-receptor occupancy under administered lurasidone, while Erritzoe et al. (2020) and Miederer et al. (2025) constrained challenge-linked release-sensitive PET routes under named ligands, regions, and task or pharmacological windows. Therefore, even on a beginner page, mixed arousal proxy, atlas prior, occupancy PET, and challenge-linked release PET have to be typed separately before proxy class, operational maturity, or calibrator role can be read safely.

Human route Proxy class / quantity type Operational maturity / burden Safe calibrator role What it still does not give you
Local human ultrastructure
Shapson-Coe et al. (2024)		 Local ex vivo structural scaffold at nanoscale resolution. Destructive, local, preservation- and registration-limited. Can calibrate a local structural scaffold route. Whole-brain in vivo coverage, current state, and longitudinal maintenance-state.
Human mixed-arousal proxy in sleep
Carro-Domínguez et al. (2025)		 Pupil-linked arousal proxy during human sleep with coupling to spindle-cluster and stimulation-response structure. Noninvasive same-session route, but autonomic and sleep-state mixed rather than transmitter-specific. Can calibrate a bounded mixed-arousal proxy route. Regional receptor / transporter density prior, drug-occupancy route, challenge-linked endogenous release proxy, and route-free whole-brain neuromodulatory state.
Human receptor / transporter atlas prior
Hansen et al. (2022); Nakuci & Bansal (2025)		 Normative cortical receptor / transporter density atlas and PET-informed modeling scaffold. Pooled healthy-participant atlas and model-conditioned route, not a same-subject current-state readout. Can calibrate a bounded chemoarchitectural prior / modeling-scaffold route. Same-subject current occupancy, challenge-linked endogenous release, moment-to-moment transmitter dynamics, and route-free whole-brain neuromodulatory state.
Human occupancy PET
Wong et al. (2013)		 Target-engagement occupancy under a named administered compound and tracer route. Tracer- and kinetic-model-conditioned PET route under explicit drug-administration burden; not an endogenous-release assay. Can calibrate a bounded target-engagement occupancy route. Baseline endogenous transmitter field, spontaneous or task-linked release dynamics, receptor-density prior, and route-free whole-brain neuromodulatory state.
Human challenge-linked displacement / release-sensitive PET
Erritzoe et al. (2020); Miederer et al. (2025)		 Challenge- and tracer-conditioned displacement proxy for endogenous transmitter release in a named region and time window. PET route with pharmacological or task challenge, kinetic-interpretation burden, and route-specific spatial / temporal scope. Can calibrate a bounded challenge-linked release proxy. Route-free baseline current field, normative receptor-density prior, target-engagement occupancy under administered drug, and whole-brain moment-to-moment neuromodulatory state.
Regional synaptic-density PET atlas
Johansen et al. (2024)		 Regional SV2A-density proxy atlas. Living-human in vivo route with tracer and quantification dependence. Can calibrate a regional synaptic-density comparison family. Moment-to-moment synaptic efficacy, release-site number, docked-vesicle architecture, active-zone nanostructure / priming-site assembly, current release competence, receptor occupancy, transmitter release, and branch-local plasticity state.
Whole-brain 1H-MRSI biochemical similarity scaffold
Lucchetti et al. (2025)		 Parcel-level biochemical similarity scaffold from five-metabolite 1H-MRSI. Living-human in vivo route with spectral QC, parceling, and similarity-definition burden. Can calibrate a macro biochemical scaffold family. Axonal wiring, kinetic flux, transmitter specificity, and local maintenance controller state.
High-resolution 1H-MRSI metabolite-distribution route
Guo et al. (2025)		 High-resolution ultrahigh-field metabolite-distribution maps from 1H-MRSI under explicit reconstruction and artifact-control burden. Living-human in vivo route with extended spatiospectral encoding, subspace modeling, and ghosting / aliasing / low-SNR handling burden. Can calibrate a bounded high-resolution metabolite-distribution proxy. Parcel-similarity structure, kinetic glucose-rate imaging, deuterium absolute quantification, and local maintenance-controller identity.
Human 31P-MRS metabolite / pH balance route
Ren et al. (2015)		 ATP-synthesis-related rate, phosphorus-metabolite concentrations, and intra-/extracellular pH balance. Living-human in vivo route, but still spectroscopy- and quantification-burdened rather than a local controller assay. Can calibrate a bounded metabolite / pH balance proxy family. Branch-local energetic fragility, mitochondrial positioning, cell-specific reserve, and kinetic glucose-rate imaging.
Human 31P MT exchange-flux route
Ren et al. (2017)		 Model-conditioned PCr→ATP and Pi→ATP exchange-flux estimates from a 7 T magnetization-transfer route. Living-human in vivo route, but still 7 T- and model-conditioned rather than a route-free energetic snapshot. Can calibrate a bounded exchange-flux proxy family. Route-independent metabolite / pH balance, whole-brain NAD content, task-evoked local NAD dynamics, branch-local mitochondrial residence, and direct controller identity.
Human 31P NAD-content mapping route
Guo et al. (2024)		 Whole-brain intracellular NAD-content mapping at 7 T under advanced denoising and explicit spectral fitting. Living-human in vivo route, but still low-concentration and long-acquisition-burdened rather than a task-dynamic controller assay. Can calibrate a bounded whole-brain NAD-content map proxy. Task-evoked local NAD dynamics, branch-local mitochondrial residence, task-general controller identity, and whole-brain moment-to-moment redox control.
Human 31P functional NAD-dynamics route
Kaiser et al. (2026)		 Visual-task fMRS detection of NAD+ dynamics in a functionally localized occipital voxel at 7 T. Living-human in vivo route, but task-specific, localized, and prior-fMRI-dependent rather than a whole-brain map. Can calibrate a bounded task-locked local NAD-dynamics proxy. Whole-brain NAD-content mapping, task-general energetic-controller identity, branch-local mitochondrial residence, and whole-brain moment-to-moment redox control.
Human deuterium metabolite-mapping / absolute-quantification route
Karkouri et al. (2026)		 Absolute deuterated HDO / Glc / Glx / Lac metabolite maps from specialized deuterium acquisition. Specialized 7 T route with custom hardware and explicit calibration burden. Can calibrate a bounded deuterium metabolite-mapping / absolute-quantification proxy. Kinetic glucose-rate imaging unless explicitly modeled, branch-local mitochondrial positioning, cell-specific energetic reserve, and routine field-ready deployment.
Human deuterium kinetic-rate imaging
Li et al. (2025)		 Kinetic glucose-rate imaging from dynamic deuterium acquisition and explicit kinetic modeling. Specialized 7 T route with custom hardware, blood-input burden, and kinetic-model burden. Can calibrate a bounded deuterium kinetic-rate proxy. Branch-local mitochondrial positioning, cell-specific energetic reserve, and routine field-ready deployment.
Human myelin MRI quantity-defined family
Arshad et al. (2017); Hagiwara et al. (2018); Baadsvik et al. (2024); Galbusera et al. (2025)		 Macro myelin proxy family whose quantity type may be myelin-water, MT-family macromolecular contrast, bilayer-sensitive mapping, or qT1 remyelination-sensitive contrast. Route-specific validity and signal-model burden; bilayer mapping currently needs high-performance hardware, while remyelination-sensitive contrasts are not interchangeable with healthy-white-matter myelin amount. Can calibrate a bounded, quantity-defined macro myelin proxy family. Per-axon timing-state, node / internode control, plasticity-ready conduction state, and routine field-ready whole-brain deployment.
Human BBB water-exchange MRI
Morgan et al. (2024); Padrela et al. (2025)		 ASL-based estimates of BBB water-exchange time / rate such as Kw or Tex. Method-dependent ASL route with fitting burden; DP-ASL and ME-ASL are not interchangeable. Can calibrate a bounded BBB water-exchange proxy. Tracer-specific permeability-surface-area, cell-specific pericyte / endothelial controller identity, local capillary recruitment state, and synapse-resolved neurovascular support.
Human tracer-specific BBB transport PET
Chung et al. (2025)		 Tracer-specific BBB transport / permeability-surface-area estimates under a named PET kinetic model. Total-body high-temporal-resolution PET with explicit kinetic modeling; tracer choice changes the route object. Can calibrate a bounded tracer-specific BBB transport proxy. Water-exchange time / rate, cell-specific pericyte / endothelial controller identity, local capillary recruitment state, and synapse-resolved neurovascular support.
Human SMBT-1 first-in-human MAO-B target-validation route
Villemagne et al. (2022)		 Brain 18F-SMBT-1 binding under selegiline-blockade-supported MAO-B target validation in healthy humans. First-in-human dynamic brain PET route in 14 healthy volunteers; blockade logic and quantification choices remain part of the object. Can calibrate a bounded MAO-B target-validation route. AD-spectrum contrast, brain-quantification generalization, whole-body tracer burden, I2BS-related burden, astrocyte-ensemble identity, and a generic whole-brain astrocyte-state scalar.
Human SMBT-1 AD-spectrum disease-context route
Villemagne et al. (2022)		 Regional 18F-SMBT-1 binding across the Alzheimer continuum relative to amyloid and tau burden. Cross-sectional disease-context cohort in 77 volunteers; a steady-state SUVR route rather than first-in-human target validation, quantification-setting, or whole-body biodistribution. Can calibrate a bounded AD-spectrum disease-context route. Route-free astrocyte-state interpretation, healthy-volunteer target validation beyond this design, arterial-sampled quantification transfer, whole-body tracer burden, I2BS-related burden, and astrocyte-ensemble identity.
Human SMBT-1 brain-quantification route
Hiraoka et al. (2025)		 Regional 18F-SMBT-1 binding under arterial-sampled dynamic PET with compartment-model and SUVR comparison. Healthy-elderly quantification-setting route with arterial input, model-comparison burden, and scan-window dependence. Can calibrate a bounded brain-quantification route. Disease-context contrast, whole-body biodistribution, I2BS-related burden, astrocyte-ensemble identity, and a route-free whole-brain astrocyte-state scalar.
Human SMBT-1 whole-body biodistribution route
Mesfin et al. (2026)		 Whole-body 18F-SMBT-1 organ uptake and excretion profile. Six healthy volunteers with nine whole-body scans over 5.5 h; prolonged deployment-burden route rather than a brain-side contrast or quantification study. Can calibrate a bounded whole-body biodistribution / tracer-burden route. Regional brain astrogliosis contrast, brain-quantification transfer, I2BS-related burden, astrocyte-ensemble identity, and a route-free whole-brain astrocyte-state scalar.
Human I2BS astrocyte PET route
Tyacke et al. (2018); Livingston et al. (2022)		 Tracer-specific I2BS-related astrocyte PET signal under BU99008 competition and disease-context interpretation. I2BS target class with idazoxan-competition logic, quantification-model burden, and cognitively-impaired-cohort dependence; not interchangeable with MAO-B routes. Can calibrate a bounded I2BS-related astrocyte proxy. MAO-B target validation, MAO-B disease-context contrast, MAO-B brain-quantification transfer, astrocyte-ensemble identity, minute-scale astrocyte-network state, and a generic whole-brain astrocyte-state scalar.
Human macroscopic CSF-oscillation proxy
Fultz et al. (2019)		 Coupled macroscopic EEG / hemodynamic / CSF oscillation during NREM sleep. Fast-fMRI plus EEG sleep route; macro oscillation rather than a solute-specific clearance assay. Can calibrate a bounded sleep-state CSF-oscillation proxy. Net molecular flux, parenchyma-CSF exchange, CSF-to-blood clearance capacity, cell-specific immune control, and local synaptic maintenance.
Human parenchyma-CSF water-exchange proxy
Kim, Huang, & Liu (2025)		 Parenchyma-CSF water-exchange estimate from magnetization-transfer spin labeling. Small-cohort noninvasive MRI route with exchange-model burden; not a protein-specific efflux assay. Can calibrate a bounded parenchyma-CSF water-exchange proxy. Protein-specific efflux, CSF-to-blood clearance capacity, cell-specific immune control, and local synaptic maintenance.
Human intrathecal tracer / CSF-to-blood clearance proxy
Eide et al. (2023)		 Intrathecal gadobutrol retention and pharmacokinetic CSF-to-blood-clearance-capacity variables. Intrathecal tracer plus contrast MRI and population-pharmacokinetic-model burden; not a routine natural-sleep route. Can calibrate a bounded intrathecal-tracer / CSF-to-blood-clearance-capacity proxy. Natural-sleep whole-brain clearance truth, cell-specific immune control, and local synaptic maintenance.
Human CSF-mobility MRI
Hirschler et al. (2025)		 Region-specific CSF mobility measured with a CSF-specific MRI technique. Specialized 7 T MRI route; mobility rather than net flow or direct solute clearance. Can calibrate a bounded CSF-mobility proxy. Net solute flux, CSF-to-blood clearance capacity, cell-specific immune control, and local synaptic maintenance.
Human model-based overnight biomarker efflux
Dagum et al. (2026)		 Model-based overnight Aβ / tau brain-to-plasma efflux estimate. Investigational-device route with multicompartment modeling and crossover-protocol burden. Can calibrate a bounded model-based biomarker-efflux proxy. Local immune-controller identity, local synaptic maintenance, and route-free whole-brain clearance truth.
Spectroscopy rows also split by quantity type

The same beginner rule now applies to spectroscopy-derived routes. Lucchetti et al. (2025) defined a five-metabolite parcel-similarity graph, Guo et al. (2025) produced a high-resolution 1H-MRSI metabolite-distribution route under explicit reconstruction and artifact-control burden, Ren et al. (2015) measured ATP synthesis, phosphorus metabolites, and pH balance with 31P-MRS in 12 resting participants, Guo et al. (2024) mapped whole-brain intracellular NAD content at 7 T, Kaiser et al. (2026) detected task-evoked NAD+ dynamics in a functionally localized occipital voxel, Li et al. (2025) mapped CMRGlc, CMRLac, VTCA, and Tmax in five healthy participants with dynamic deuterium MRSI, and Karkouri et al. (2026) generated absolute HDO / Glc / Glx / Lac maps and rate maps at 7 T. Therefore, a beginner summary can no longer say only "MRSI" or "energetic imaging." It has to say whether the object is similarity, high-resolution metabolite distribution, energetic balance, NAD-content mapping, localized functional NAD dynamics, deuterium absolute metabolite mapping / quantification, or kinetic rate imaging.

Quantity type is part of the claim, even in a beginner summary

Recent primary literature makes the compression problem concrete. Bhogal et al. (2020), Wright et al. (2022), and Baboli et al. (2024) showed that even within 1H-MRSI, metabolite maps depend on lipid suppression, tissue-fraction correction, and voxel-specific relaxation or water modeling; Guo et al. (2025) then showed that high-resolution metabolite-distribution mapping itself requires explicit handling of ghosting, aliasing, and low SNR. On the myelin side, Arshad et al. (2017) showed that calibrated T1w/T2w can be reliable yet have low criterion validity against MWF, Hagiwara et al. (2018) showed stronger white-matter agreement between SyMRI and MTsat than with T1w/T2w, Baadsvik et al. (2024) mapped the myelin lipid-protein bilayer only in two healthy volunteers on high-performance hardware, and Galbusera et al. (2025) showed that qT1, but not MWF or MTR, separated cortical remyelination conditions in postmortem multiple-sclerosis tissue. On the BBB side, Morgan et al. (2024) showed that DP-ASL and ME-ASL can yield substantially different Kw values in the same cohort, Padrela et al. (2025) used multi-echo ASL to estimate Tex in 194 healthy adults after QC, and Chung et al. (2025) used high-temporal-resolution total-body PET plus kinetic modeling to estimate tracer-specific PS across three radiotracers while explicitly noting the current lack of human ground truth. On the astrocyte side, Villemagne et al. (2022) showed more than 85% selegiline blockade of 18F-SMBT-1 in healthy humans, Villemagne et al. (2022) then showed higher cortical SMBT-1 binding in Aβ+ cognitively normal and AD groups across the AD continuum, Hiraoka et al. (2025) showed that the brain-side SMBT-1 readout still depends on arterial-sampled quantification choices, Mesfin et al. (2026) separated the whole-body biodistribution burden, Tyacke et al. (2018) showed that 11C-BU99008 is reduced by idazoxan but not by isocarboxazid as an I2BS route, and Livingston et al. (2022) plus Best et al. (2026) plus Jaisa-Aad et al. (2024) showed that human astrocyte-related signals remain target-, cohort-, smoking-, severity-, and pathology-conditioned rather than one monotonic scalar. On the clearance side, Fultz et al. (2019) showed macroscopic CSF oscillation during NREM sleep, Kim, Huang, & Liu (2025) measured parenchyma-CSF water exchange, Eide et al. (2023) combined intrathecal tracer retention with CSF-to-blood clearance-capacity modeling, Hirschler et al. (2025) measured CSF mobility, and Dagum et al. (2026) inferred model-based overnight biomarker efflux. Therefore, this page no longer lets the beginner modality label alone carry the claim. It names target, quantity type, route role, transport regime, and model burden before the claim ceiling is read.

2026-04-01 correction: glial substrate-routing is not a generic energetic row

The beginner route still needed one more stop rule. Suzuki et al. (2011) linked astrocyte-neuron lactate transport to long-term memory formation, Silva et al. (2022) identified a cortex-glia ketone-body export route under starvation, Pavlowsky et al. (2025) identified an intensive-learning glia-to-neuron fatty-acid route, and Greda et al. (2025) showed an apoE3 / sortilin-dependent neuronal lipid-uptake and fuel-choice route when glucose is limited. Qi et al. (2021) further showed that even neuron-astrocyte fatty-acid coupling itself is genotype-sensitive, so a single phrase such as glial metabolic support is not a safe beginner object anymore. By contrast, current human 31P fMRS, deuterium imaging, and astrocyte-related PET still constrain macro energetic or target-defined proxy classes rather than the operative supplier-fuel-sink route itself. Therefore, even this beginner page now keeps glial substrate-routing separate from neuronal mitochondrial state, astrocyte-network state, and generic human energetic imaging. If a claim depends on metabolic support, the minimum beginner rule is now to name the claim family, supplier cell / neuronal sink, fuel object / carrier, regime trigger, and human observability ceiling. The shortest follow-up is Wiki: glial substrate-routing route card.

2026-04-02 correction: transcription is not the whole molecular maintenance layer

The beginner route still needed one more stop rule on the molecular side. Santoni et al. (2024) showed that chromatin plasticity can predetermine neuronal eligibility for memory-trace formation, and Terceros et al. (2026) showed a time-dependent thalamocortical transcriptional gate for memory stabilization. But the current literature does not support collapsing the rest of molecular maintenance into that single transcription line. Wang et al. (2015) showed that a neuron-specific LSD1 splice-isoform route regulates memory formation, Peterson et al. (2025) showed ADAR2-mediated GluA2 RNA editing in homeostatic synaptic plasticity, Vierra et al. (2023) showed activity-coupled Ca2+-activated PKA signaling at neuronal ER-plasma-membrane junctions, Thomas et al. (2025) showed that actin dynamics and spine geometry can persist on the timescale of the synaptic tag after LTP, and Aiken & Holzbaur (2024) showed that local microtubule dynamics pattern presynaptic cargo pausing and retention in human iPSC-derived axons. Therefore, even this beginner page now keeps transcription / chromatin state, post-transcriptional RNA-state, phospho-signaling / second-messenger state, local proteostasis / synaptic-tagging state, and cargo-transport / cytoskeletal trafficking state as separate hidden-state families rather than one generic molecular-memory row. If a claim depends on molecular maintenance, the minimum beginner rule is to name the object family, direct observable, compartment or integration unit, timescale, and human observability ceiling. The shortest follow-up is Wiki: homeostatic plasticity and maintenance state.

More rows do not yet make one same-state sample

Even after the three-axis reading is made explicit, composition and bridge remain separate stop lines. Vafaii et al. (2024) showed that simultaneous multimodal recordings retain both common and divergent structure, and Chen et al. (2025) showed that tightly coupled temporal progression can coexist with distinct network patterns in simultaneous EEG-PET-MRI. Lu et al. (2023) then showed that preservation route changes extracellular-space retention, while Egger et al. (2024) showed that within-day EEG decoding conditions drift enough to motivate adaptive decoders. Therefore, same-subject, same-brain, or multimodal wording does not by itself convert several rows into one validated latent-state sample.

What still remains outside these human routes

Even after those advances, several maintenance-state families still remain outside comparable whole-brain human readout. Santoni et al. (2024) showed that chromatin plasticity can predetermine neuronal eligibility for memory-trace formation, Terceros et al. (2026) showed causal, time-dependent thalamocortical transcriptional gates for memory stabilization, Wang et al. (2015) and Peterson et al. (2025) showed that post-transcriptional RNA control already spans distinct splice-isoform and RNA-editing routes, Vierra et al. (2023) showed a local phospho-signaling / second-messenger control layer, Thomas et al. (2025) showed that tag-timescale stabilization can persist through actin / spine-state, Aiken & Holzbaur (2024) showed that axonal cargo pausing and presynaptic accumulation depend on local microtubule-state, Suzuki et al. (2011), Silva et al. (2022), Pavlowsky et al. (2025), and Greda et al. (2025) showed distinct glia-to-neuron fuel routes rather than one background energetic scalar, and Cahill et al. (2024) showed that local neurotransmitter inputs are encoded by broad astrocyte networks over minutes-long timescales. Those results justify keeping transcription / chromatin state, post-transcriptional RNA-state, phospho-signaling / second-messenger state, local proteostasis / synaptic-tagging state, cargo-transport / cytoskeletal trafficking state, glial substrate-routing, and astrocyte-network state visible as separate hidden-state families rather than background noise.

The practical beginner rule is therefore not "human evidence is weak" or "human evidence is almost complete." It is that human evidence is layered. Each layer constrains a different part of the problem, each route carries a different maturity burden, and each one calibrates only a bounded hidden-state family. After that, composition and bridge validity are still separate checks. That is why this site now routes beginners from this page to WBE 101, measurement-stack claim ceilings, Human Proxy Composition, State-Continuity Bridge, and maintenance-state rather than leaving "human evidence improved" as a single sentence.

Why this site does not say "done" quickly

WBE is a field where different levels are easy to collapse into one another. A system can decode text without reproducing the generating mechanism. A tractography graph can be useful without being an edge-complete connectome. A DCM graph can be informative without being discovered causal wiring. A thermodynamic result can be interesting without being a direct measurement of physical dissipation. A closed-loop demo can be real progress without solving the full body/environment boundary.

Representative reasons, from current primary literature

Chaibub Neto et al. (2019), Di et al. (2021), Tang et al. (2023), and Willett et al. (2023) together show why high decode scores still need shortcut and task-prior audits. Thomas et al. (2014) and Maier-Hein et al. (2017) show why human tractography must be read as a ceiling-limited structural prior, while Gajwani et al. (2023), He et al. (2024), McMaster et al. (2025), Bramati et al. (2026), Manzano-Patrón et al. (2025), and Zhu et al. (2025) show why even the headline tractography graph, hub map, and laterality can shift with protocol, graph-construction choices, uncertainty route, or ex vivo calibration. Penny et al. (2004) and Rosa et al. (2012) show why candidate-model comparison matters for effective connectivity, but Smith et al. (2011), Barnett & Seth (2017), Vink et al. (2020), Villaverde et al. (2019), Novelli et al. (2025), Jafarian et al. (2024), and Yan et al. (2026) show why node-definition policy, observed-subsystem closure / latent-confound audit, sampling / transformation sensitivity, and controlled reliability windows still have to be disclosed before a directed graph is read as discovered causal wiring. Lynn et al. (2021) and Ishihara & Shimazaki (2025) show why irreversibility results depend on estimator family and assumptions. Musall et al. (2019) and Flesher et al. (2021) show why local closed-loop success still needs a disclosed body/environment boundary.

2026-03-30 correction: three beginner shortcuts this site no longer allows

Three more shortcuts had become too weak even for a beginner page. First, same-brain functional connectomics is not a solved local twin. MICrONS Consortium et al. (2025) linked in vivo neurophysiology to later EM in the same volume of cortex ex vivo, Ding et al. (2025) used that stack for a validated stimulus-conditioned response model, Gamlin et al. (2025) still used predicted transcriptomic labels, Molnár et al. (2016) showed that human synapses can contain multiple docked vesicles and multivesicular release, Sakamoto et al. (2018) showed that Munc13-1 assemblies set independent release sites, Emperador-Melero et al. (2024) showed that CaV2 clustering and vesicle priming are executed by distinct active-zone machineries, Mittermaier et al. (2024) showed that membrane-potential state gates human synaptic consolidation, and Beiran & Litwin-Kumar (2025) showed that connectomes can still leave dynamics degenerate until extra recordings are added. So the beginner-safe ceiling is a sequential local scaffold plus task-bounded conditional predictor, not a readout of release-site number, active-zone nanostructure / priming-site assembly, current release competence, or a solved local twin. Second, low latency is not the whole body / environment boundary. de Quervain et al. (1998) and Oei et al. (2007) showed retrieval sensitivity to glucocorticoids, while Barone et al. (2023) and Birnie et al. (2023) showed that circadian and corticosteroid timing alters hippocampal plasticity and memory. So even on a beginner page, fast loop disclosure and slow internal-milieu disclosure have to stay separate. Third, a chemical connectome is not shared extracellular / electrical-state complete: Galarreta & Hestrin (1999) showed electrical-synapse networks, Graydon et al. (2014) showed that local extracellular geometry changes neurotransmitter dilution, and Voldsbekk et al. (2020) provided bounded human evidence consistent with wakefulness-related extracellular-space change. The shortest follow-up is therefore Wiki: why wiring diagrams alone are not enough, Wiki: closed loop, delay, jitter, safe stop, and Wiki: observability and claim ceiling by measurement stack, not one global reading of “multimodal progress.”

What to do with that information

The point is not to dismiss progress. The point is to read each result at the right level. On this site, the fastest route for that is now Wiki: how to read claims and evidence, followed by the specific route-card pages for measurement stack, connectomes, effective connectivity, thermodynamic claims, maintenance-state families, and closed loops.

What this site is trying to build

Mind-Upload is not itself the proof of WBE. It is a place for assembling the public goods that would make proof or disproof possible in a comparable way: standards, benchmarks, route cards, failure conditions, logs, and reproducible examples. That is why the site often sounds stricter than ordinary hype-driven discussion. The goal is not smaller ambition. The goal is cleaner evidence.

If you are currently stuck on... Go here next Why
How strong a headline really is How to read claims and evidence It translates headlines into claim level and route-card requirements.
Why structure alone is not enough Why wiring diagrams alone are not enough It separates structural progress from hidden-state completeness.
Why verification comes before strong claims Basics of verification infrastructure It explains the role of standards, benchmarks, preregistration, and audits.

Next

If you want the shortest technical page for reading strong-looking claims safely, continue here.

How to read claims and evidence →

References

  1. Chaibub Neto, E., Pratap, A., Perumal, T. M., et al. (2019). Detecting the impact of subject characteristics on machine learning-based diagnostic applications. npj Digital Medicine, 2, 99. doi:10.1038/s41746-019-0178-x
  2. Di, Y., An, X., Zhong, W., Liu, S., & Ming, D. (2021). The time-robustness analysis of individual identification based on resting-state EEG. Frontiers in Human Neuroscience, 15, 672946. doi:10.3389/fnhum.2021.672946
  3. Tang, J., LeBel, A., Jain, S., & Huth, A. G. (2023). Semantic reconstruction of continuous language from non-invasive brain recordings. Nature Neuroscience, 26, 858-866. doi:10.1038/s41593-023-01304-9
  4. Willett, F. R., Kunz, E. M., Fan, C., et al. (2023). A high-performance speech neuroprosthesis. Nature, 620, 1031-1036. doi:10.1038/s41586-023-06377-x
  5. Thomas, C., Ye, F. Q., Irfanoglu, M. O., et al. (2014). Anatomical accuracy of brain connections derived from diffusion MRI tractography is inherently limited. PNAS, 111(46), 16574-16579. doi:10.1073/pnas.1405672111
  6. Maier-Hein, K. H., Neher, P. F., Houde, J.-C., et al. (2017). The challenge of mapping the human connectome based on diffusion tractography. Nature Communications, 8, 1349. doi:10.1038/s41467-017-01285-x
  7. Gajwani, M., Oldham, S., Pang, J. C., Arnatkevičiūtė, A., Tiego, J., Bellgrove, M. A., & Fornito, A. (2023). Can hubs of the human connectome be identified consistently with diffusion MRI? Network Neuroscience, 7(4), 1277-1304. doi:10.1162/netn_a_00324
  8. He, Y., Hong, Y., Wu, Y., et al. (2024). Spherical-deconvolution informed filtering of tractograms changes laterality of structural connectome. NeuroImage, 297, 120904. doi:10.1016/j.neuroimage.2024.120904
  9. McMaster, E. M., Newlin, N. R., Rudravaram, G., et al. (2025). Harmonized connectome resampling for variance in voxel sizes. Magnetic Resonance Imaging, 122, 110424. doi:10.1016/j.mri.2025.110424
  10. Manzano-Patrón, J. P., Deistler, M., Schröder, C., et al. (2025). Uncertainty mapping and probabilistic tractography using Simulation-based Inference in diffusion MRI: A comparison with classical Bayes. Medical Image Analysis, 103, 103580. doi:10.1016/j.media.2025.103580
  11. Zhu, S., Huszar, I. N., Cottaar, M., et al. (2025). Imaging the structural connectome with hybrid MRI-microscopy tractography. Medical Image Analysis, 102, 103498. doi:10.1016/j.media.2025.103498
  12. Bramati, I. B., Szczupak, D., Carneiro Monteiro, M., Meireles, F., Menezes Guimarães, D., Dean, R. J., Paul, L. K., & Tovar-Moll, F. (2026). Diffusion MRI sampling schemes bias diffusion metrics and tractography. Frontiers in Neuroimaging, 5, 1670604. doi:10.3389/fnimg.2026.1670604
  13. Penny, W. D., Stephan, K. E., Mechelli, A., & Friston, K. J. (2004). Comparing dynamic causal models. NeuroImage, 22(3), 1157-1172. doi:10.1016/j.neuroimage.2004.03.026
  14. Rosa, M. J., Friston, K., & Penny, W. (2012). Post-hoc selection of dynamic causal models. Journal of Neuroscience Methods, 208(1), 66-78. doi:10.1016/j.jneumeth.2012.04.013
  15. Smith, S. M., Miller, K. L., Salimi-Khorshidi, G., Webster, M., Beckmann, C. F., Nichols, T. E., Ramsey, J. D., & Woolrich, M. W. (2011). Network modelling methods for FMRI. NeuroImage, 54(2), 875-891. doi:10.1016/j.neuroimage.2010.08.063
  16. Barnett, L., & Seth, A. K. (2017). Detectability of Granger causality for subsampled continuous-time neurophysiological processes. Journal of Neuroscience Methods, 275, 93-121. doi:10.1016/j.jneumeth.2016.10.016
  17. Villaverde, A. F., Tsiantis, N., & Banga, J. R. (2019). Full observability and estimation of unknown inputs, states and parameters of nonlinear biological models. Journal of the Royal Society Interface, 16(156), 20190043. doi:10.1098/rsif.2019.0043
  18. Vink, J. J. T., Klooster, D. C. W., Ozdemir, R. A., Westover, M. B., Pascual-Leone, A., & Shafi, M. M. (2020). EEG Functional Connectivity is a Weak Predictor of Causal Brain Interactions. Brain Topography, 33(2), 221-237. doi:10.1007/s10548-020-00757-6
  19. Jafarian, A., Karadag Assem, M., Kocagoncu, E., et al. (2024). Reliability of dynamic causal modelling of resting-state magnetoencephalography. Human Brain Mapping, 45(10), e26782. doi:10.1002/hbm.26782
  20. Novelli, L., Barnett, L., Seth, A. K., & Razi, A. (2025). Minimum-Phase Property of the Hemodynamic Response Function, and Implications for Granger Causality in fMRI. Human Brain Mapping, 46(10), e70285. doi:10.1002/hbm.70285
  21. Yan, J., Zhang, S.-W., Zhang, C., Huang, W., Shi, J., & Chen, L. (2026). Dynamical Causality under Latent Confounders for Biological Network Reconstruction. IEEE Transactions on Pattern Analysis and Machine Intelligence. doi:10.1109/TPAMI.2026.3658839
  22. Lynn, C. W., Cornblath, E. J., Papadopoulos, L., et al. (2021). Broken detailed balance and entropy production in the human brain. PNAS, 118(47), e2109889118. doi:10.1073/pnas.2109889118
  23. Ishihara, K., & Shimazaki, H. (2025). State-space kinetic Ising model reveals task-dependent entropy flow in sparsely active nonequilibrium neuronal dynamics. Nature Communications, 16, 10852. doi:10.1038/s41467-025-66669-w
  24. Musall, S., Kaufman, M. T., Juavinett, A. L., Gluf, S., & Churchland, A. K. (2019). Single-trial neural dynamics are dominated by richly varied movements. Nature Neuroscience, 22, 1677-1686. doi:10.1038/s41593-019-0502-4
  25. Flesher, S. N., Downey, J. E., Weiss, J. M., et al. (2021). A brain-computer interface that evokes tactile sensations improves robotic arm control. Science, 372(6544), 831-836. doi:10.1126/science.abd0380
  26. Shapson-Coe, A., Januszewski, M., Berger, D. R., et al. (2024). A petavoxel fragment of human cerebral cortex reconstructed at nanoscale resolution. Science, 384(6696), eadk4858. doi:10.1126/science.adk4858
  27. Johansen, A., Beliveau, V., Colliander, E., et al. (2024). An in vivo high-resolution human brain atlas of synaptic density. Journal of Neuroscience, 44(33), e1750232024. doi:10.1523/JNEUROSCI.1750-23.2024
  28. Lucchetti, F., Céléreau, E., Steullet, P., et al. (2025). Constructing the human brain metabolic connectome with MR spectroscopic imaging reveals cerebral biochemical organization. Nature Communications, 16, 11344. doi:10.1038/s41467-025-66124-w
  29. Ren, J., Sherry, A. D., & Malloy, C. R. (2015). 31P-MRS of healthy human brain: ATP synthesis, metabolite concentrations, pH, and T1 relaxation times. NMR in Biomedicine, 28(11), 1455-1462. doi:10.1002/nbm.3384
  30. Guo, R., Yang, S., Wiesner, H. M., Li, Y., Zhao, Y., Liang, Z.-P., Chen, W., & Zhu, X.-H. (2024). Mapping intracellular NAD content in entire human brain using phosphorus-31 MR spectroscopic imaging at 7 Tesla. Frontiers in Neuroscience, 18, 1389111. doi:10.3389/fnins.2024.1389111
  31. Kaiser, A., Vind, F. A., Duarte, J. M. N., Jelescu, I., Lin, Y., Yu, X., Widmaier, M., Wenz, D., & Xin, L. (2026). Ultra-high field 31P functional magnetic resonance spectroscopy reveals NAD+ dynamics in brain energy metabolism during visual stimulation. Journal of Cerebral Blood Flow & Metabolism. doi:10.1177/0271678X261415784
  32. Li, X., Zhu, X.-H., Li, Y., et al. (2025). Quantitative mapping of key glucose metabolic rates in the human brain using dynamic deuterium magnetic resonance spectroscopic imaging. PNAS Nexus, 4(3), pgaf072. doi:10.1093/pnasnexus/pgaf072
  33. Bhogal, A. A., Broeders, T. A. A., Morsinkhof, L., et al. (2020). Lipid-suppressed and tissue-fraction corrected metabolic distributions in human central brain structures using 2D 1H magnetic resonance spectroscopic imaging at 7 T. Brain and Behavior, 10(10), e01852. doi:10.1002/brb3.1852
  34. Wright, A. M., Murali-Manohar, S., & Henning, A. (2022). Quantitative T1-relaxation corrected metabolite mapping of 12 metabolites in the human brain at 9.4 T. NeuroImage, 261, 119574. doi:10.1016/j.neuroimage.2022.119574
  35. Baboli, M., Wang, F., Dong, Z., et al. (2024). Absolute Metabolite Quantification in Individuals with Glioma and Healthy Individuals Using Whole-Brain Three-dimensional MR Spectroscopic and Echo-planar Time-resolved Imaging. Radiology, 311(3), e232401. doi:10.1148/radiol.232401
  36. Guo, R., Li, Y., Zhao, Y., et al. (2025). High-Resolution Brain Metabolic Imaging at Ultrahigh Field Using Extended Spatiospectral Encoding and Subspace Modeling. IEEE Transactions on Biomedical Engineering, 72(12), 3558-3566. doi:10.1109/TBME.2025.3572448
  37. Karkouri, J., Deelchand, D. K., Van de Moortele, P.-F., et al. (2026). Quantification of deuterated metabolite concentrations and rates in the human brain from dynamic deuterium metabolic imaging at 7 T. Magnetic Resonance in Medicine. doi:10.1002/mrm.70308
  38. Carro-Domínguez, M., Huwiler, S., Oberlin, S., et al. (2025). Pupil size reveals arousal level fluctuations in human sleep. Nature Communications, 16, 5146. doi:10.1038/s41467-025-57289-5
  39. Hansen, J. Y., Shafiei, G., Markello, R. D., et al. (2022). Mapping neurotransmitter systems to the structural and functional organization of the human neocortex. Nature Neuroscience, 25, 1569-1581. doi:10.1038/s41593-022-01186-3
  40. Nakuci, J., & Bansal, K. (2025). The neuroreceptors and transporters underlying spontaneous brain activity. Communications Biology, 8, 485. doi:10.1038/s42003-025-08492-z
  41. Wong, D. F., Kuwabara, H., Brašić, J. R., et al. (2013). Determination of dopamine D2 receptor occupancy by lurasidone using positron emission tomography in healthy male subjects. Psychopharmacology, 229, 245-252. doi:10.1007/s00213-013-3103-z
  42. Erritzoe, D., Ashok, A. H., Searle, G. E., et al. (2020). Serotonin release measured in the human brain: a PET study with [11C]CIMBI-36 and d-amphetamine challenge. Neuropsychopharmacology, 45, 804-810. doi:10.1038/s41386-019-0567-5
  43. Miederer, I., Buchholz, H.-G., Rademacher, L., et al. (2025). Dopaminergic Mechanisms of Cognitive Flexibility: An [18F]Fallypride PET Study. Journal of Nuclear Medicine, 66(3), 398-404. doi:10.2967/jnumed.124.268317
  44. Suzuki, A., Stern, S. A., Bozdagi, O., Huntley, G. W., Walker, R. H., Magistretti, P. J., & Alberini, C. M. (2011). Astrocyte-neuron lactate transport is required for long-term memory formation. Cell, 144(5), 810-823. doi:10.1016/j.cell.2011.02.018
  45. Silva, B., et al. (2022). Glial ketogenesis regulates memory maintenance during starvation. Nature Metabolism, 4, 1534-1547. doi:10.1038/s42255-022-00528-6
  46. Qi, G., Mi, Y., Shi, X., Gu, H., Brinton, R. D., & Yin, F. (2021). ApoE4 impairs neuron-astrocyte coupling of fatty acid metabolism. Cell Reports, 34(1), 108572. doi:10.1016/j.celrep.2020.108572
  47. Pavlowsky, A., et al. (2025). Neuronal fatty acid oxidation fuels memory after intensive learning in Drosophila. Nature Metabolism, 7, 2467-2483. doi:10.1038/s42255-025-01416-5
  48. Greda, A. K., et al. (2025). Interaction of sortilin with apolipoprotein E3 enables neurons to use long-chain fatty acids as alternative metabolic fuel. Nature Metabolism, 7, 2346-2365. doi:10.1038/s42255-025-01389-5
  49. Arshad, M., Stanley, J. A., & Raz, N. (2017). Test-retest reliability and concurrent validity of in vivo myelin content indices: Myelin water fraction and calibrated T1w/T2w image ratio. Human Brain Mapping, 38(4), 1780-1790. PMC5342928
  50. Hagiwara, A., Hori, M., Kamagata, K., et al. (2018). Myelin Measurement: Comparison Between Simultaneous Tissue Relaxometry, Magnetization Transfer Saturation Index, and T1w/T2w Ratio Methods. Scientific Reports, 8, 10554. doi:10.1038/s41598-018-28852-6
  51. Baadsvik, E. L., Weiger, M., Froidevaux, R., Schildknecht, C. M., Ineichen, B. V., & Pruessmann, K. P. (2024). Myelin bilayer mapping in the human brain in vivo. Magnetic Resonance in Medicine, 91(6), 2332-2344. doi:10.1002/mrm.29998
  52. Galbusera, R., Weigel, M., Bahn, E., et al. (2025). Quantitative T1 is sensitive to cortical remyelination in multiple sclerosis: A postmortem MRI study. Brain Pathology, 35(5), e70010. doi:10.1111/bpa.70010
  53. Morgan, C. A., Thomas, D. L., Shao, X., et al. (2024). Measurement of blood-brain barrier water exchange rate using diffusion-prepared and multi-echo arterial spin labelling: Comparison of quantitative values and age dependence. NMR in Biomedicine, 37(12), e5256. doi:10.1002/nbm.5256
  54. Padrela, B. E., Slivka, M., Sneve, M. H., et al. (2025). Blood-brain barrier water permeability across the adult lifespan: A multi-echo ASL study. Neurobiology of Aging, 147, 176-186. doi:10.1016/j.neurobiolaging.2024.12.012
  55. Chung, K. J., Abdelhafez, Y. G., Spencer, B. A., et al. (2025). Quantitative PET imaging and modeling of molecular blood-brain barrier permeability. Nature Communications, 16, 3076. doi:10.1038/s41467-025-58356-7
  56. Villemagne, V. L., Harada, R., Dore, V., et al. (2022). First-in-Humans Evaluation of 18F-SMBT-1, a Novel 18F-Labeled Monoamine Oxidase-B PET Tracer for Imaging Reactive Astrogliosis. Journal of Nuclear Medicine, 63(10), 1551-1559. doi:10.2967/jnumed.121.263254
  57. Villemagne, V. L., Harada, R., Doré, V., et al. (2022). Assessing reactive astrogliosis with 18F-SMBT-1 across the Alzheimer disease spectrum. Journal of Nuclear Medicine, 63(10), 1560-1569. doi:10.2967/jnumed.121.263255
  58. Hiraoka, K., Mesfin, B., Wu, Y., et al. (2025). Kinetic and quantitative analysis of [18F]SMBT-1 PET imaging for monoamine oxidase B. Annals of Nuclear Medicine, 39, 255-265. doi:10.1007/s12149-025-02083-y
  59. Mesfin, B., Ishioka, Y., Ichinose, Y., et al. (2026). Whole-body biodistribution of [18F]SMBT-1: a novel PET tracer for monoamine oxidase B imaging in healthy humans. Annals of Nuclear Medicine. doi:10.1007/s12149-025-02144-2
  60. Tyacke, R. J., Myers, J. F. M., Venkataraman, A., et al. (2018). Evaluation of 11C-BU99008, a PET Ligand for the Imidazoline2 Binding Site in Human Brain. Journal of Nuclear Medicine, 59(10), 1597-1602. doi:10.2967/jnumed.118.208009
  61. Livingston, N. R., Calsolaro, V., Hinz, R., et al. (2022). Relationship between astrocyte reactivity, using novel 11C-BU99008 PET, and glucose metabolism, grey matter volume and amyloid load in cognitively impaired individuals. Molecular Psychiatry, 27(4), 2019-2029. doi:10.1038/s41380-021-01429-y
  62. Best, L. M., Truong, J., McCluskey, T., et al. (2026). MAO-B status in alcohol use disorder: a [11C]SL25.1188 PET imaging study of putative astrogliosis. Molecular Psychiatry. doi:10.1038/s41380-025-03355-9
  63. Jaisa-Aad, M., Muñoz-Castro, C., Healey, M. A., Hyman, B. T., & Serrano-Pozo, A. (2024). Characterization of monoamine oxidase-B (MAO-B) as a biomarker of reactive astrogliosis in Alzheimer's disease and related dementias. Acta Neuropathologica, 147(1), 66. doi:10.1007/s00401-024-02712-2
  64. Fultz, N. E., Bonmassar, G., Setsompop, K., et al. (2019). Coupled electrophysiological, hemodynamic, and cerebrospinal fluid oscillations in human sleep. Science, 366(6465), 628-631. doi:10.1126/science.aax5440
  65. Kim, D., Huang, Y., & Liu, J. (2025). Non-invasive MRI measurements of age-dependent in vivo human glymphatic exchange using magnetization transfer spin labeling. NeuroImage, 310, 121142. doi:10.1016/j.neuroimage.2025.121142
  66. Eide, P. K., Lashkarivand, A., Pripp, A. H., et al. (2023). Plasma neurodegeneration biomarker concentrations associate with glymphatic and meningeal lymphatic measures in neurological disorders. Nature Communications, 14, 2084. doi:10.1038/s41467-023-37685-5
  67. Hirschler, L., Runderkamp, B. A. R., Decker, A., et al. (2025). Region-specific drivers of CSF mobility measured with MRI in humans. Nature Neuroscience, 28, 2392-2401. doi:10.1038/s41593-025-02073-3
  68. Dagum, P., Elbert, D. L., Giovangrandi, L., et al. (2026). The glymphatic system clears amyloid beta and tau from brain to plasma in humans. Nature Communications, 17, 715. doi:10.1038/s41467-026-68374-8
  69. Vafaii, H., Mandino, F., Desrosiers-Grégoire, G., et al. (2024). Multimodal measures of spontaneous brain activity reveal both common and divergent patterns of cortical functional organization. Nature Communications, 15, 229. doi:10.1038/s41467-023-44363-z
  70. Chen, J. E., Lewis, L. D., Coursey, S. E., et al. (2025). Simultaneous EEG-PET-MRI identifies temporally coupled and spatially structured brain dynamics across wakefulness and NREM sleep. Nature Communications, 16, 8887. doi:10.1038/s41467-025-64414-x
  71. Lu, X., Han, X., Meirovitch, Y., et al. (2023). Preserving extracellular space for high-quality optical and ultrastructural studies of whole mammalian brains. Cell Reports Methods, 3(8), 100520. doi:10.1016/j.crmeth.2023.100520
  72. Egger, J., Kostoglou, K., & Müller-Putz, G. R. (2024). Chrono-EEG dynamics influencing hand gesture decoding: a 10-hour study. Scientific Reports, 14, 20449. doi:10.1038/s41598-024-70609-x
  73. Santoni, G., et al. (2024). Chromatin plasticity predetermines neuronal eligibility for memory trace formation. Science, 385(6707), eadg9982. doi:10.1126/science.adg9982
  74. Terceros, A., Chen, C., Harada, Y., et al. (2026). Thalamocortical transcriptional gates coordinate memory stabilization. Nature, 649, 1254-1263. doi:10.1038/s41586-025-09774-6
  75. Wang, J., Telese, F., Tan, Y., et al. (2015). LSD1n is an H4K20 demethylase regulating memory formation via transcriptional elongation control. Nature Neuroscience, 18(9), 1256-1264. doi:10.1038/nn.4069
  76. Peterson, L., Kasper, J. M., Allgaier, J. A., et al. (2025). ADAR2-mediated Q/R editing of GluA2 in homeostatic synaptic plasticity. Science Signaling, 18(886), eadr1442. doi:10.1126/scisignal.adr1442
  77. Vierra, N. C., et al. (2023). Neuronal ER-plasma membrane junctions couple excitation to Ca2+-activated PKA signaling. Nature Communications, 14, 5231. doi:10.1038/s41467-023-40930-6
  78. Thomas, M., Bogaciu, C.-A., Rizzoli, S. O., et al. (2025). Long-term potentiation-induced changes in actin dynamics and spine geometry persist on the timescale of the synaptic tag. Communications Biology, 8, 1065. doi:10.1038/s42003-025-08459-0
  79. Aiken, J., & Holzbaur, E. L. F. (2024). Spastin locally amplifies microtubule dynamics to pattern the axon for presynaptic cargo delivery. Current Biology, 34(8), 1687-1704.e8. doi:10.1016/j.cub.2024.03.010
  80. Cahill, M. K., et al. (2024). Network-level encoding of local neurotransmitters in cortical astrocytes. Nature, 629, 146-153. doi:10.1038/s41586-024-07311-5
  81. MICrONS Consortium, et al. (2025). Functional connectomics spanning multiple areas of mouse visual cortex. Nature, 640, 435-447. doi:10.1038/s41586-025-08790-w
  82. Ding, Z., et al. (2025). Functional connectomics reveals a general wiring rule in mouse visual cortex. Nature, 640, 459-469. doi:10.1038/s41586-025-08840-3
  83. Gamlin, C. R., et al. (2025). Connectomics of predicted Sst transcriptomic types in mouse visual cortex. Nature, 640, 497-505. doi:10.1038/s41586-025-08805-6
  84. Mittermaier, F. X., Kalbhenn, T., Xu, R., et al. (2024). Membrane potential states gate synaptic consolidation in human neocortical tissue. Nature Communications, 15, 10340. doi:10.1038/s41467-024-53901-2
  85. Beiran, M., & Litwin-Kumar, A. (2025). Prediction of neural activity in connectome-constrained recurrent networks. Nature Neuroscience, 28, 2561-2574. doi:10.1038/s41593-025-02080-4
  86. de Quervain, D. J.-F., Roozendaal, B., & McGaugh, J. L. (1998). Stress and glucocorticoids impair retrieval of long-term spatial memory. Nature, 394, 787-790. doi:10.1038/29542
  87. Oei, N. Y. L., Elzinga, B. M., Wolf, O. T., de Ruiter, M. B., Damoiseaux, J. S., Kuijer, J. P. A., Veltman, D. J., Scheltens, P., & Rombouts, S. A. R. B. (2007). Glucocorticoids decrease hippocampal and prefrontal activation during declarative memory retrieval in young men. Brain Imaging and Behavior, 1(1-2), 31-41. doi:10.1007/s11682-007-9003-2
  88. Barone, I., Gilette, N. M., Hawks-Mayer, H., et al. (2023). Synaptic BMAL1 phosphorylation controls circadian hippocampal plasticity. Science Advances, 9(43), eadj1010. doi:10.1126/sciadv.adj1010
  89. Birnie, M. T., Begum, G., Sugden, D., et al. (2023). Circadian regulation of hippocampal function is disrupted with corticosteroid treatment. Proceedings of the National Academy of Sciences of the United States of America, 120(15), e2211996120. doi:10.1073/pnas.2211996120
  90. Galarreta, M., & Hestrin, S. (1999). A network of fast-spiking cells in the neocortex connected by electrical synapses. Nature, 402, 72-75. doi:10.1038/47029
  91. Graydon, C. W., Cho, S., Diamond, J. S., Kachar, B., von Gersdorff, H., & Grimes, W. N. (2014). Specialized postsynaptic morphology enhances neurotransmitter dilution and high-frequency signaling at an auditory synapse. Journal of Neuroscience, 34(24), 8358-8372. doi:10.1523/JNEUROSCI.4493-13.2014
  92. Voldsbekk, I., Maximov, I. I., Zak, N., Roelfs, D., Geier, O., Due-Tønnessen, P., Elvsåshagen, T., Strømstad, M., Bjørnerud, A., & Groote, I. (2020). Evidence for wakefulness-related changes to extracellular space in human brain white matter from diffusion-weighted MRI. NeuroImage, 212, 116682. doi:10.1016/j.neuroimage.2020.116682