Coverage

stale · v3 provisional awaiting approval

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for Computational modelling identifies key determinants of subregion-specific dopamine dynamics in the striatum · this layer across all papers · json

Provisional

This layer needs a corpus-scope decision that has not been ruled on yet, so what it produces would change if the decision changed. It waits on claim-format, relation-vocab.

Awaiting approval

Waiting for approval. That is a statement about the record, not about whether anyone has read this: people read the corpus without stamping what they read, and only a stamp leaves a trace. Approval is an operation on a version, not a step of its own — it is recorded against the version it was granted to, so running this layer again does not carry it forward.

Out of date

These inputs changed after this ran:

  • claims/ejdrup-2026-dopamine/d1r-tracks-da-50ms-delay.md
  • claims/ejdrup-2026-dopamine/d2r-initialization-unjustified.md
  • claims/ejdrup-2026-dopamine/d2r-insensitive-to-brief-pauses.md
  • claims/ejdrup-2026-dopamine/d2r-integrates-over-seconds.md
  • claims/ejdrup-2026-dopamine/d2r-occupancy-higher-in-vs.md
  • claims/ejdrup-2026-dopamine/dat-clustering-greater-in-vs.md
  • claims/ejdrup-2026-dopamine/dat-immunostaining-dorsoventral-gradient.md
  • claims/ejdrup-2026-dopamine/dat-nanoclustering-slows-clearance.md
  • claims/ejdrup-2026-dopamine/ds-lacks-pervasive-tonic-da.md
  • claims/ejdrup-2026-dopamine/ds-vs-vmax-ratio-assumed.md
  • claims/ejdrup-2026-dopamine/fscv-matches-may-wightman-1989.md
  • claims/ejdrup-2026-dopamine/hypothesis-d1-d2-temporal-distinction.md
  • claims/ejdrup-2026-dopamine/hypothesis-nanoclustering-regulates-vmax.md
  • claims/ejdrup-2026-dopamine/hypothesis-vmax-explains-regional-difference.md
  • claims/ejdrup-2026-dopamine/interprets-cragg-rice-vmax-ratio.md
  • claims/ejdrup-2026-dopamine/interprets-may-wightman-1989-fscv.md
  • claims/ejdrup-2026-dopamine/low-burst-no-spillover-high-burst-does.md
  • claims/ejdrup-2026-dopamine/nanoclustering-constant-vmax-constraint.md
  • claims/ejdrup-2026-dopamine/nanoclustering-model-varicosity-scale.md
  • claims/ejdrup-2026-dopamine/vmat2-gradient-absent.md
  • claims/ejdrup-2026-dopamine/vmax-modulation-larger-impact-in-vs.md
  • claims/ejdrup-2026-dopamine/vmax-only-parameter-driving-regional-difference.md
  • claims/ejdrup-2026-dopamine/vs-low-active-fraction-resembles-ds-distribution.md
  • claims/ejdrup-2026-dopamine/vs-lowest-percentiles-above-10nm.md
  • claims/ejdrup-2026-dopamine/vs-maintains-pervasive-tonic-da.md

What it produced109 spans.orphans

Read from coverage/ejdrup-2026-dopamine.json · 38 KB. claims 25panels.pct 25.9statistics.total 3statistics.pct 100spans.segmented 513spans.obligations 227spans.textual 286spans.accounted 118spans.pct 52

# uidsectiontextstatspanels
1 results-002 results DA release sites on axons projecting from the midbrain were randomly simulated as uniformly distributed discrete points in a three-dimensional space ( Figure 1A ). [] ["fig1a"]
2 results-024 results ( A ) Average DA concentration across a 100 x 100 × 100 µm volume of simulated DS at pacemaker activity. [] ["fig1a"]
3 results-025 results ( B ) Mean concentration change in response to a single event with 60% release probability (see supplementary notes on electrical stimulation) in all neurons at time zero. [] ["fig1b"]
4 results-027 results ( C ) Modelling of predicted FSCV measurement and representative snapshot of simulation space just after a release event. [] ["fig1c"]
5 results-035 results Figure 1—figure supplement 1—source code 1. Source code used to generate data in Figure 1—figure supplement 1 . [] ["fig1s1"]
6 results-036 results Figure 1—figure supplement 2. Simulation size and granularity. [] ["fig1s2"]
7 results-037 results ( A ) Schematic of different simulation sizes. [] ["fig1s2a"]
8 results-038 results ( B ) Concentration percentiles at different simulation diameters. [] ["fig1s2b"]
9 results-040 results ( C ) Schematic of simulation granularity. [] ["fig1s2c"]
10 results-041 results ( D ) Effect of simulation voxel diameter on concentration percentiles. [] ["fig1s2d"]
11 results-044 results ( E ) Absolute difference in [DA] between simulations at 0.1 µm voxel diameter and 0.5, 1.0, and 2.0 µm. [] ["fig1s2e"]
12 results-046 results ( F ) Percentage difference in [DA] between simulations at 0.1 µm voxel diameter and 0.5, 1.0, and 2.0 µm. [] ["fig1s2f"]
13 results-047 results Figure 1—figure supplement 2—source code 1. Source code used to generate data in Figure 1—figure supplement 2 . [] ["fig1s2"]
14 results-068 results Importantly, our model faithfully mirrored DA release seen with fast-scan cyclic voltammetry (FSCV) recordings upon direct stimulation of striatal slices when we corrected for kinetics of the typical … [] ["fig1s1"]
15 results-073 results Further, when we calculated the average concentration of a larger area across time, which fibre photometry conceivably does, the results resembled a tonic DA concentration ( Figure 1—figure supplement… [] ["fig1s1"]
16 results-075 results Table 1. List of variables used in the simulation of the dorsal striatum. [] ["table1"]
17 results-076 results Variable Abbreviation Value Reference Firing rate 4 Hz Paladini et al., 2003 Release probability 6% Dreyer et al., 2010 DA molecules per vesicle 3000 Klaus et al., 2019 Diffusion coefficient 763 µm 2 … [] ["app2table2","fig1s2"]
18 results-078 results The finer the spatial grain, the higher the detail close to a release event; however, at a spatial granularity of 1 μm, [DA] deviated by <2% across most percentiles and only by >1 nM above the 99.5 th… [] ["fig1s2"]
19 results-085 results The relationship between firing rate and the sphere of influence by DA became further evident when plotting maximal concentration of the surrounding space ( Figure 1—figure supplement 1D ) and the vol… [] ["fig1s1"]
20 results-091 results D1 receptors (-Rs) were assumed to have a half maximal effective concentration (EC 50 ) of 1000 nM, and we extrapolated the reverse rate constant ( k off ) to 19.5 s –1 based on a linear fit of the re… [] ["fig1s1"]
21 results-092 results We set the EC 50 of D2Rs to 7 nM and k off to 0.2 s –1 based on the DA sensor kinetic fit ( Figure 1—figure supplement 1F ), which matches a recent binding study (0.197 s –1 for binding study vs. 0.20… [] ["fig1s1"]
22 results-100 results This made the D2R incapable of temporally separating closely linked bursts of activity and rather summarised the output, whereas the D1R occupancy reset between each individual burst ( Figure 1—figure… [] ["fig1s1"]
23 results-102 results Indeed, this finding was robust across an order of magnitude of D2R affinity (2 nm - 20 nM), although the sensitivity to a one-second pause was larger at an affinity of 20 nM ( Figure 1—figure supplem… [] ["fig1s1"]
24 results-106 results In line with this, most studies report lower dopaminergic density in the VS than in DS regardless of methodological modality with a median value of ~90% in VS relative to DS ( Appendix 2—table 1 ). [] ["app2table1"]
25 results-107 results Further, DAT-mediated uptake capacity is reported to be lower in VS with a median capacity at ~30% of DS ( Appendix 2—Tables 1 and 2 ). [] ["table1"]
26 results-110 results We simulated DA release during pacemaker activity in both DS and VS. DS values were set as previously described (25 µm 3 per terminal, uptake capacity of 6.0 μM s –1 ), but for VS we reduced the termi… [] ["table1"]
27 results-133 results ( H ) Peak occupancy at different distances from the area bursting, normalised to maximal and minimum occupancy. [] ["fig2h"]
28 results-143 results ( F ) Effect of complete pause in firing in VS for 1 s on both average [DA] and D1R and D2R occupation. [] ["fig2f"]
29 results-155 results Additionally, the larger DA overflow in VS after a burst caused a higher relative increase in receptor occupancy further away from the area actively bursting than compared to DS ( Figure 2H ). [] ["fig2h"]
30 results-157 results Changes to uptake capacity greatly affect [DA] in the ventral striatum The values used to model the striatum ( Table 1 ) in the previous simulations were chosen to best mimic the physiological system … [] ["table1"]
31 results-159 results First, we varied the number of varicosities actively releasing DA by setting the varicosity density to one site per 9 µm 3 ( Doucet et al., 1986 ) and simulating 4 Hz pacemaker activity with the relea… [] ["fig3a"]
32 results-166 results ( A ) Schematic of the fraction of active release sites. [] ["fig3a"]
33 results-171 results ( D ) Schematic of changing quantal size ( Q ). [] ["fig3d"]
34 results-175 results ( H ) Schematic of changing DAT K m . [] ["fig3h"]
35 results-179 results Shaded areas are median V max of the two regions (DS and VS) as found in the literature shown in Appendix 2—table 2 ± 50%. [] ["app2table2"]
36 results-181 results The shaded area indicates median V max for VS found in the literature shown in Appendix 2—table 2 ± 50%. [] ["app2table2"]
37 results-183 results ( A ) Schematic of our definitions of tonic (50 th percentile/median, dashed lines) and peak (99.5 th percentile, solid line) DA for both the dorsal and ventral striatum. [] ["fig3a"]
38 results-186 results ( D ) Ratio between 99.5 th and 50 th percentiles as a measure of focality for both regions. [] ["fig3d"]
39 results-192 results Figure 3—figure supplement 1—source code 1. Source code used to generate data in Figure 3—figure supplement 1 . [] ["fig3s1"]
40 results-193 results Figure 3—figure supplement 2. Fold change during inhibition, V max -sensitivity at different release parameters and release-uptake balance. [] ["fig3s2"]
41 results-194 results ( A ) Fold change over baseline (K m of 210 nM) for mean DA concentration in the dorsal (DS) and ventral striatum (VS) with changing DAT K m . [] ["fig3s2a"]
42 results-195 results ( B ) Relative difference between the dorsal and ventral striatum for both phasic and tonic DA at different K m values. [] ["fig3s2b"]
43 results-196 results ( C ) Effect of changing DAT V max on DA concentrations for three different quantal sizes ( Q ). [] ["fig3s2c"]
44 results-197 results [DA] normalised to highest values within each Q. Shaded area indicates median V max for DS and VS as found in the literature shown in Appendix 2—table 2 with ±50%. [] ["app2table2"]
45 results-198 results ( D ) Effect of changing DAT V max on DA concentrations for three different release probabilities (R % ). [] ["fig3s2d"]
46 results-200 results Shaded area indicates median V max for DS and VS as found in the literature shown in Appendix 2—table 2 with ±50%. [] ["app2table2"]
47 results-201 results ( E ) Least-square fit linear regression between release rate and autocorrelation decay rate (τ) ( Ejdrup et al., 2023 ). [] ["fig3s2e"]
48 results-203 results ( F ) Partial regression plot error of the regression in ( f ) and error between DA response to amphetamine as measured by microdialysis and the release rate from Ejdrup et al., 2023 to show that the … [] ["fig3s2f"]
49 results-205 results Figure 3—figure supplement 2—source code 1. Source code used to generate data in Figure 3—figure supplement 2A-D . [] ["fig3s2"]
50 results-206 results The predicted total DA content of a vesicle and the fraction of content released per fusion event is reported to range from 1,000–30,000 molecules ( Garris et al., 1994 ; Pothos et al., 1998 ; Staal e… [] ["fig3d"]
51 results-211 results We observed a largely similar pattern when changing either release probability or firing rate ( Figure 3—figure supplement 1B-G ). [] ["fig3s1"]
52 results-213 results To mimic competitive inhibition of DAT by, for example cocaine, we ran a simulation across various K m values ( Figure 3H ) showing that increasing K m caused a linear increase in DA levels, consisten… [] ["fig3h"]
53 results-216 results Further, we observe a convergence on a twofold difference in the absolute values at both tonic and peak levels, which matches reports from earlier FSCV studies ( Figure 3—figure supplement 2B ; Wu et … [] ["fig3s2"]
54 results-220 results Further, the impact of changing V max in VS was independent of both Q and R % within values typically reported in the literature ( Figure 3—figure supplement 2C, D ). [] ["fig3s2"]
55 results-223 results We therefore reanalysed data from our previously published comparison of fibre photometry and microdialysis ( Ejdrup et al., 2023 ) and found evidence of natural variations in the release-uptake balan… [] ["fig3s2"]
56 results-228 results We speculated that dense nanoclusters of DAT would produce domains of low [DA] due to uptake overpowering diffusion ( Figure 4A ). [] ["fig4a"]
57 results-229 results As the uptake rate is concentration dependent, this would reduce uptake efficiency ( Figure 4B ). [] ["fig4b"]
58 results-248 results ( A ) Schematic of dense DA cluster. [] ["fig4a"]
59 results-250 results ( B ) Effective transport rate dependent on local concentration. [] ["fig4b"]
60 results-260 results ( H ) Difference between DA concentration at the centre of clusters (or general surface of varicosity for unclustered) and mean concentration of the full simulation space ( I ) Concentrations across a… [] ["fig4h"]
61 discussion-011 discussion But our modelling suggested this is prevented by the significant DA uptake capacity of the DS, as measured by more recent reuptake studies (see Appendix 2—table 2 ). [] ["app2table2"]
62 captions-021 captions ( A ) Average DA concentration across a 100 x 100 × 100 µm volume of simulated DS at pacemaker activity. [] ["fig1a"]
63 captions-022 captions ( B ) Mean concentration change in response to a single event with 60% release probability (see supplementary notes on electrical stimulation) in all neurons at time zero. [] ["fig1b"]
64 captions-024 captions ( C ) Modelling of predicted FSCV measurement and representative snapshot of simulation space just after a release event. [] ["fig1c"]
65 captions-032 captions Figure 1—figure supplement 1—source code 1. Source code used to generate data in Figure 1—figure supplement 1 . [] ["fig1s1"]
66 captions-033 captions [panels detected: a, b, c, d, e, f, g, h] === Figure 1s2 === Figure 1—figure supplement 2. Simulation size and granularity. [] ["fig1s2"]
67 captions-034 captions ( A ) Schematic of different simulation sizes. [] ["fig1s1a"]
68 captions-035 captions ( B ) Concentration percentiles at different simulation diameters. [] ["fig1s1b"]
69 captions-037 captions ( C ) Schematic of simulation granularity. [] ["fig1s1c"]
70 captions-038 captions ( D ) Effect of simulation voxel diameter on concentration percentiles. [] ["fig1s1d"]
71 captions-041 captions ( E ) Absolute difference in [DA] between simulations at 0.1 µm voxel diameter and 0.5, 1.0, and 2.0 µm. [] ["fig1s1e"]
72 captions-043 captions ( F ) Percentage difference in [DA] between simulations at 0.1 µm voxel diameter and 0.5, 1.0, and 2.0 µm. [] ["fig1s1f"]
73 captions-044 captions Figure 1—figure supplement 2—source code 1. Source code used to generate data in Figure 1—figure supplement 2 . [] ["fig1s2"]
74 captions-046 captions ( A ) Representative snapshots of steady state dynamics at 4 Hz tonic firing with parameters mirroring the dorsal (left) and ventral striatum (right). [] ["fig1s2a"]
75 captions-047 captions ( B ) Cross-section of temporal dynamics for data shown in a. [] ["fig1s2b"]
76 captions-049 captions ( C ) Normalised density of DA concentration of simulations in ( a ). [] ["fig1s2c"]
77 captions-052 captions ( D ) Same data as in ( c ), but for concentration percentiles. [] ["fig1s2d"]
78 captions-054 captions ( E ) Convolved model response (Figure S1c) to mimic FSCV measurements mirroring the experimentally tested stimulation paradigm in May and Wightman, 1989 for the dorsal (left) and ventral striatum (ri… [] ["fig1s2e","fig1s2f"]
79 captions-060 captions ( G ) Top: representative [DA] trace 1 µm away from a release site during pacemaker and burst activity. [] ["fig1s2g"]
80 captions-063 captions ( H ) Peak occupancy at different distances from the area bursting, normalised to maximal and minimum occupancy. [] ["fig1s2h"]
81 captions-073 captions ( F ) Effect of complete pause in firing in VS for 1 s on both average [DA] and D1R and D2R occupation. [] ["fig2f"]
82 captions-082 captions ( F ) Ratio between peak and tonic concentrations across various quantal sizes in in DS (blue line) and VS (red line). [] ["fig2f"]
83 captions-084 captions ( H ) Schematic of changing DAT K m . [] ["fig2h"]
84 captions-088 captions Shaded areas are median V max of the two regions (DS and VS) as found in the literature shown in Appendix 2—table 2 ± 50%. [] ["app2table2"]
85 captions-090 captions The shaded area indicates median V max for VS found in the literature shown in Appendix 2—table 2 ± 50%. [] ["app2table2"]
86 captions-092 captions ( A ) Schematic of our definitions of tonic (50 th percentile/median, dashed lines) and peak (99.5 th percentile, solid line) DA for both the dorsal and ventral striatum. [] ["fig3a"]
87 captions-095 captions ( D ) Ratio between 99.5 th and 50 th percentiles as a measure of focality for both regions. [] ["fig3d"]
88 captions-101 captions Figure 3—figure supplement 1—source code 1. Source code used to generate data in Figure 3—figure supplement 1 . [] ["fig3s1"]
89 captions-102 captions [panels detected: a, b, c, d, e, f, g] === Figure 3s2 === Figure 3—figure supplement 2. Fold change during inhibition, V max -sensitivity at different release parameters and release-uptake balance. [] ["fig3s2"]
90 captions-103 captions ( A ) Fold change over baseline (K m of 210 nM) for mean DA concentration in the dorsal (DS) and ventral striatum (VS) with changing DAT K m . [] ["fig3s1a"]
91 captions-104 captions ( B ) Relative difference between the dorsal and ventral striatum for both phasic and tonic DA at different K m values. [] ["fig3s1b"]
92 captions-105 captions ( C ) Effect of changing DAT V max on DA concentrations for three different quantal sizes ( Q ). [] ["fig3s1c"]
93 captions-106 captions [DA] normalised to highest values within each Q. Shaded area indicates median V max for DS and VS as found in the literature shown in Appendix 2—table 2 with ±50%. [] ["app2table2"]
94 captions-107 captions ( D ) Effect of changing DAT V max on DA concentrations for three different release probabilities (R % ). [] ["fig3s1d"]
95 captions-109 captions Shaded area indicates median V max for DS and VS as found in the literature shown in Appendix 2—table 2 with ±50%. [] ["app2table2"]
96 captions-110 captions ( E ) Least-square fit linear regression between release rate and autocorrelation decay rate (τ) ( Ejdrup et al., 2023 ). [] ["fig3s1e"]
97 captions-112 captions ( F ) Partial regression plot error of the regression in ( f ) and error between DA response to amphetamine as measured by microdialysis and the release rate from Ejdrup et al., 2023 to show that the … [] ["fig3s1f"]
98 captions-114 captions Figure 3—figure supplement 2—source code 1. Source code used to generate data in Figure 3—figure supplement 2A-D . [] ["fig3s2"]
99 captions-116 captions ( A ) Schematic of dense DA cluster. [] ["fig3s2a"]
100 captions-118 captions ( B ) Effective transport rate dependent on local concentration. [] ["fig3s2b"]
101 captions-119 captions ( C ) Top view of unfolded DA varicosity. [] ["fig3s2c"]
102 captions-122 captions ( D ) Cross-section showing DA concentration in space surrounding varicosity unfolded in c. [] ["fig3s2d"]
103 captions-125 captions ( E ) Top view from ( c ), but colour coded for DA concentration immediately above membrane surface at different DAT cluster sizes. [] ["fig3s2e"]
104 captions-126 captions ( F ) Changes in [DA] from 15 nM unclustered (Un.) steady state with constant release after changing to four different cluster size scenarios (ø=diameter). [] ["fig3s2f"]
105 captions-127 captions ( G ) Clearance of 100 nM [DA] for different DAT cluster sizes. [] ["fig3s2g"]
106 captions-128 captions ( H ) Difference between DA concentration at the centre of clusters (or general surface of varicosity for unclustered) and mean concentration of the full simulation space ( I ) Concentrations across a… [] ["fig3s2h"]
107 tables-001 tables Table 1. List of variables used in the simulation of the dorsal striatum. [] ["table1"]
108 tables-002 tables Variable Abbreviation Value Reference Firing rate 4 Hz Paladini et al., 2003 Release probability 6% Dreyer et al., 2010 DA molecules per vesicle 3000 Klaus et al., 2019 Diffusion coefficient 763 µm 2 … [] ["app2table2","app2table1","keyresource"]
109 tables-012 tables Mouse Salvatore et al., 2005 FSCV - [DA] p 91 % 57 52 nM Rat May and Wightman, 1989 FSCV - [DA] p 76 % 89.3 67.5 nM Rat Garris and Wightman, 1994 Median 90 % - - - - - Appendix 2—table 2. Overview of … [] ["app2table2"]

Artifacts

Versions

From the run ledger. There is no changelog beside it to keep in step.

  1. v3 · 2026-09-13 · scripts/pipeline.py run

    ran via scripts/pipeline.py

    cd extract && python3 -m elife_extract.cli coverage --paper ejdrup-2026-dopamine --json ../coverage/ejdrup-2026-dopamine.json

  2. v2 · 2026-09-12 · scripts/pipeline.py run

    re-run after prepare v2

    cd extract && python3 -m elife_extract.cli coverage --paper ejdrup-2026-dopamine --json ../coverage/ejdrup-2026-dopamine.json

  3. v1 · 2026-09-11 · scripts/pipeline.py run

    coverage for the nine papers that had none

    cd extract && python3 -m elife_extract.cli coverage --paper ejdrup-2026-dopamine --json ../coverage/ejdrup-2026-dopamine.json

This layer across the corpus

Across the corpus

4 blocked upstream · 6 stale·a paper links to its own cell, where this layer's output for it is rendered

PaperStateVersionLast runOutputCell
A three-dimensional immunofluorescence atlas of the …ran via scripts/pipeline.pyblocked upstreamv32026-09-13artiushin-2026-spider-atlas.jsonjson
Distinct representational properties of cues and con…ran via scripts/pipeline.pystalev42026-09-13bouyeure-2026-fear-rsa.jsonjson
Computational modelling identifies key determinants …ran via scripts/pipeline.pystalev32026-09-13ejdrup-2026-dopamine.jsonjson
Contributions of insula and superior temporal sulcus…rebuild after edge completion passstalev72026-09-13gadeke-2026-guilt-insula.jsonjson
Spatially targeted inhibitory rhythms differentially…ran via scripts/pipeline.pystalev32026-09-13headley-2026-inhibitory-rhythms.jsonjson
Feedback of peripheral saccade targets to early fove…ran via scripts/pipeline.pyblocked upstreamv32026-09-13kammer-2026-foveal-feedback.jsonjson
iGABASnFR2 is an improved genetically encoded protei…ran via scripts/pipeline.pyblocked upstreamv52026-09-13kolb-2026-igabasnfr2.jsonjson
A deep learning pipeline for mapping in situ network…ran via scripts/pipeline.pyblocked upstreamv52026-09-13rozak-2026-neurovascular-dl.jsonjson
Self-association enhances early attentional selectio…ran via scripts/pipeline.pystalev32026-09-13scheller-2026-self-prioritization.jsonjson
Impaired excitability of fast-spiking neurons in a n…ran via scripts/pipeline.pystalev52026-09-13wengert-2026-kcnc1.jsonjson

Inputs and outputs

Produces
  • coverage/{paper}.json

One per paper — the table above links each one that exists.

Views
  • document — declared, and this artifact is not the shape this view needs
  • table — rendered above, over the 128 spans.orphans in the artifact

Running it

The command comes from the declaration, so this text and what actually runs cannot diverge. pipeline.py run also runs the unmet dependencies first. 

python3 scripts/pipeline.py run <paper> coverage

Underneath, that runs cd extract && python3 -m claim_graphs.cli coverage --paper {paper} --json ../coverage/{paper}.json.