Datasets / DANDI:001868

Chronic electrophysiology and two-photon calcium imaging of mouse primary somatosensory cortex during intracortical microstimulation learning

behavioral cell population imaging electrophysiological microscopy House mouse 7.5 GB 85 files
  • 16 reproduced
  • 4 partially reproduced
  • 4 not reproduced
  • 19 blocked

Tested on dataset version 0.260715.2016

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Claims

From the paper 10.1126/sciadv.aef0343

Reproducing Kim, Lycke et al. (2026) from dandiset 001868 24

Notebook reproduce_kim_2026.ipynb

Reproduced16

  • ReproducedResults, Behavioral learning enhances ICMS-evoked population activation over time, paragraph 1; Figure 2A

    Behavioural detection thresholds fall with learning; thresholds in the late phase (weeks 2 to 4) are lower than in the early phase (weeks 0 to 1), median 5.5 against 4.2 uA.

    The detection thresholds in the late phase were significantly lower than those in the early phase ( Fig. 2A ), decreasing from a median of 5.5 to 4.2 μA

    Results, Behavioral learning enhances ICMS-evoked population activation over time, paragraph 1; Figure 2A

    What the paper reports

    • two-sided Mann-Whitney U test, r = 0.40, p = 7.7 × 10⁻⁶ (n = 43 early and 80 late channel-session thresholds)

    psignifit thresholds at the default grid (the current at 50% of the fitted function, mean of block thresholds per channel and session, weeks 0 to 4, six mice) have medians of 5.39 uA early (n = 46) and 3.88 uA late (n = 89), both within tolerance (late 3.78 to 4.62), and late is lower (two-sided Mann-Whitney p = 5.32e-06, r = 0.39; paper p = 7.68e-6, r = 0.40). The early n is within 43 +- 4; the late n of 89 is one above 80 +- 8, an expected value the criterion does not judge. psignifit's unscaled threshold gives 5.41 and 3.84 uA. The bounded maximum likelihood variant, not the criterion's fit, gives late medians of 3.71 (mu) and 3.70 uA (x50), below the tolerance, with p = 4.9e-07 and 5.15e-06. Without Mouse 6, 5.17 and 3.91 uA (p = 7.61e-05). Every mouse is lower late.

    How the test differed from the paper’s method

    • Thresholds are refitted with psignifit 4.3 (Python), sigmoid norm, yes/no, at psignifit's default grid. The threshold is the current at which the fitted (scaled) function reaches 0.5, as the criterion says and as the paper's code takes it (processing/dataloader.py, get_block_detection_threshold, ps.getThreshold(result, 0.5), XieLuanLab/icms-activation-plasticity at d1922d1). The paper's code calls an older psignifit. The dataset stores no thresholds.
    • Blocks are the 100-trial groups of the volumetric trials; the planar fifth block is left out as the Methods count four blocks (Methods, Analysis of behavioral data). 38 channel-sessions have two blocks and 4 have one or three.
    • Mouse 6 (ICMS83) has no catch trials in its files, so its fits have no 0 uA point.
  • ReproducedResults, Behavioral learning enhances ICMS-evoked population activation over time, paragraph 1; Figure 2A; fig. S1Drests on an assumption

    The steepest fall in threshold is between weeks 0 and 1, followed by smaller changes and relative stability in weeks 2 to 4.

    detection thresholds decreased ( Fig. 2A and fig. S1D), with the steepest decline occurring during weeks 0 to 1, followed by more gradual changes and relative stability in weeks 2 to 4

    Results, Behavioral learning enhances ICMS-evoked population activation over time, paragraph 1; Figure 2A; fig. S1D

    Median psignifit threshold per week is 6.495, 4.658, 3.866, 4.310 and 3.808 uA for weeks 0 to 4. The fall from week 0 to 1 (1.837 uA) is larger than each later change (largest 0.792 uA, week 1 to 2). psignifit's unscaled threshold gives 1.775 against 0.846 uA. The maximum likelihood variant, which is not the criterion's fit, reverses the order (mu 1.016 against 1.186 uA, x50 1.139 against 1.257 uA, week 1 to 2).

    How the test differed from the paper’s method

    • The criterion's reading of Fig. 2A (week medians) is the claims file's assumption.
    • Thresholds as in fig2a-threshold-decrease (psignifit, default grid, 50% of the fitted function).
  • ReproducedResults, Behavioral learning enhances ICMS-evoked population activation over time, paragraph 1; fig. S1E

    Response times follow the same time course as thresholds, falling over training.

    Response times followed a similar time course (fig. S1E)

    Results, Behavioral learning enhances ICMS-evoked population activation over time, paragraph 1; fig. S1E

    Median over mice of the per-mouse median hit response time at 4 to 6 uA is 274.9 ms in week 0, 199.2 in week 1 and 193.9 over the late phase; 93% of the fall (75.7 of 81.0 ms) is between weeks 0 and 1. From the train onset, 265.4 and 186.8 ms (93%). Variant, hit trials early against late, 190.1 against 173.8 ms, p = 7.19e-17, r = -0.14.

    How the test differed from the paper’s method

    • The dataset's response_time is from trial start, which precedes the train onset by 5.5 to 37 ms in most sessions, 0 in Mouse 6 and 56 to 85 ms in ICMS101 2023-10-27; the time from train onset is shown as a variant and gives the same result.
    • Currents 4 to 6 uA, as the common method of the claims file fixes.
  • ReproducedResults, Millisecond pulse-locking dynamics differentiate learning-induced plasticity, paragraph 4; fig. S5, C and D

    Wheel-movement onset decreases from early to late training; median onset is 122 ms in the fastest animal (Mouse 1), 135 ms over all hit trials and 129 ms in the late phase.

    We evaluated three candidate cutoffs from stimulation onset ... the median wheel-movement onset in the fastest animal (Mouse 1, 122 ms; fig. S5D), the median onset across all hit trials (135 ms), and the median onset during the late training phase (129 ms)

    Results, Millisecond pulse-locking dynamics differentiate learning-induced plasticity, paragraph 4; fig. S5, C and D

    What the paper reports

    • two-sided Mann-Whitney U, r = -0.22, p = 4.2 × 10⁻³⁰ (n = 1,305 early and 2,922 late trials)

    Per-animal thresholds (11.8 to 14.5 a.u.) give median onsets of 119.0 ms in Mouse 1, 133.0 ms over all hit trials and 128.0 ms in the late phase, all within tolerance; early 144.0 ms (n = 1307) against late 128.0 ms (n = 2897), p = 6.09e-32, r = -0.23 (paper n 1305 and 2922, r = -0.22). The pooled threshold gives 118.0, 134.0 and 129.0 ms and p = 1.94e-29. The 568 of 4772 hit trials with no onset are the 568 with no stored wheel trace; every hit trial with a trace has an onset.

    How the test differed from the paper’s method

    • The paper takes the response threshold from hit and miss trials; the dataset's miss trials have no response time except 98, so the threshold is taken from hit trials, with those 98 added as a variant (same thresholds).
    • The stored wheel_position_processed is already low-passed at 20 Hz forward and backward (its description), so no filter is applied again.
    • Onset is measured from the train onset of the electrical_stimulation table.
  • ReproducedResults, Chronic electrophysiology confirms learning-driven strengthening of neuromodulation, paragraph 3; fig. S3B

    The modulation z-score of all modulated responses at fixed currents increases from the early to the late phase at 4, 5 and 6 uA.

    Modulation z-score at fixed currents of all modulated cells increased significantly over training

    Results, Chronic electrophysiology confirms learning-driven strengthening of neuromodulation, paragraph 3; fig. S3B

    What the paper reports

    • 4 uA: two-sided Mann-Whitney U, r = 0.18, p = 4.8 × 10⁻⁵ (n = 189 early, 729 late)
    • 5 uA: two-sided Mann-Whitney U, r = 0.12, p = 0.0033 (n = 283 early, 489 late)
    • 6 uA: two-sided Mann-Whitney U, r = 0.18, p = 1.5 × 10⁻⁴ (n = 303 early, 250 late)

    z rises at 4 uA (2.706 to 3.765, n = 191 and 738, p = 9.57e-05, r = 0.18), 5 uA (3.399 to 3.875, n = 284 and 498, p = 0.00656, r = 0.12) and 6 uA (3.559 to 4.570, n = 300 and 256, p = 0.000404, r = 0.17); the paper's n are 189/729, 283/489, 303/250. The Methods filters alone give p = 0.00032, 0.00853, 0.00055. Mice 2 and 5 have lower medians late.

  • ReproducedResults, Chronic electrophysiology confirms learning-driven strengthening of neuromodulation, paragraph 3; fig. S3Dquestion to the reader

    Modulation at each session's detection threshold does not change over training.

    When stimulation currents were adjusted to session-specific detection thresholds, the modulation score of these units remained stable across training

    Results, Chronic electrophysiology confirms learning-driven strengthening of neuromodulation, paragraph 3; fig. S3D

    What the paper reports

    • two-sided Mann-Whitney U, r = 0.21, p = 0.095 (n = 21 early, 34 late)

    Reading (a), channel-session medians at the current nearest the psignifit session threshold, early 3.93 (n = 42) against late 3.30 (n = 87), p = 0.16, r = -0.15. Reading (b), every response at that current, p = 0.789. With the maximum likelihood x50 threshold, p = 0.702. The n of (a) are twice the paper's 21 and 34; the criterion names (a) as the reading whose n matches, and it does not match here (asked in questions/001868.yaml).

  • ReproducedResults, Chronic electrophysiology confirms learning-driven strengthening of neuromodulation, paragraph 3; Figure 4, F and G

    The number of modulated units rose moderately in some animals (Mouse 1 at 4 uA), with no significant change at group level.

    electrophysiological recordings detected only a moderate increase in modulated unit numbers in some animals

    Results, Chronic electrophysiology confirms learning-driven strengthening of neuromodulation, paragraph 3; Figure 4, F and G

    What the paper reports

    • Mouse 1, 4 uA: one-sided Mann-Whitney U test, r = 0.42, p = 0.044 (n = 8 early, 21 late)

    Mouse 1 at 4 uA, modulated responses per channel and session, early median 12 (n = 8) against late 14 (n = 21), one-sided p = 0.0443, r = 0.42, as in the paper (n 8 and 21, p = 0.044, r = 0.42). All six mice, p = 0.0728, not significant, as the paper says. Mice 5 and 6 alone give p = 0.00232 and 0.0165.

  • ReproducedResults, Chronic electrophysiology confirms learning-driven strengthening of neuromodulation, paragraph 4; Figure 4, I and J

    Increases in modulation over training are carried by pyramidal cells, at 4, 5 and 6 uA, with latency decreasing at 5 and 6 uA.

    Notably, increases in modulation strength and decreases in latency were driven primarily by pyramidal cells

    Results, Chronic electrophysiology confirms learning-driven strengthening of neuromodulation, paragraph 4; Figure 4, I and J

    What the paper reports

    • modulation, 4 uA: two-sided Mann-Whitney U test, r = 0.25, p = 2.6 × 10⁻⁵
    • modulation, 5 uA: two-sided Mann-Whitney U test, r = 0.22, p = 5.3 × 10⁻⁵
    • modulation, 6 uA: two-sided Mann-Whitney U test, r = 0.23, p = 2 × 10⁻⁴
    • latency, 4 uA: two-sided Mann-Whitney U test, r = -0.06, p = 0.32
    • latency, 5 uA: two-sided Mann-Whitney U test, r = -0.17, p = 0.0017
    • latency, 6 uA: two-sided Mann-Whitney U test, r = -0.12, p = 0.049

    Pyramidal z rises at 4, 5 and 6 uA (p = 4.79e-05, 0.000587, 0.000878; r = 0.23, 0.18, 0.20) and time to maximum falls at 5 uA (65 to 25 ms, p = 0.000329, r = -0.18) and 6 uA (85 to 35 ms, p = 0.025, r = -0.13). It also falls at 4 uA (p = 0.0208), where the paper reports p = 0.32. The code's one-sided tests halve these p. With cell types from the trough-to-peak rule on the stored mean waveform (variant), z rises at all three currents (p = 0.000212, 0.000435, 0.000492) and time to maximum falls at 5 uA (p = 0.000362) but not at 6 uA (p = 0.0587).

    How the test differed from the paper’s method

    • Cell type is the stored cell_type (the files' t2p classification); the rule applied to the stored mean waveform is a variant, as that waveform is not the template the rule is defined on.
  • ReproducedResults, Chronic electrophysiology confirms learning-driven strengthening of neuromodulation, paragraph 4; Figure 4, I and J

    Interneurons show no significant change in modulation at any current; their latency changes significantly only at 6 uA.

    whereas interneurons showed little significant change

    Results, Chronic electrophysiology confirms learning-driven strengthening of neuromodulation, paragraph 4; Figure 4, I and J

    What the paper reports

    • modulation, 4 uA: two-sided Mann-Whitney U test, r = 0.07, p = 0.39 (n = 69 early, 254 late)
    • modulation, 5 uA: two-sided Mann-Whitney U test, r = -0.05, p = 0.45 (n = 113 early, 180 late)
    • modulation, 6 uA: two-sided Mann-Whitney U test, r = 0.10, p = 0.24 (n = 122 early, 86 late)
    • latency, 4 uA: two-sided Mann-Whitney U test, r = -0.01, p = 0.88
    • latency, 5 uA: two-sided Mann-Whitney U test, r = 0.07, p = 0.31
    • latency, 6 uA: two-sided Mann-Whitney U test, r = 0.21, p = 0.01

    Interneuron z does not change (p = 0.331, 0.884, 0.0885; r = 0.08, -0.01, 0.15; n 55/221, 90/158, 90/76 against the paper's 69/254, 113/180, 122/86). Time to maximum changes only at 6 uA (130 to 190 ms, p = 0.0335, r = 0.19; paper p = 0.010, r = 0.21) and not at 4 or 5 uA (p = 0.929, 0.361). With trough-to-peak cell types (variant), z p = 0.117, 0.706, 0.229 and time to maximum changes at no current (6 uA p = 0.419).

  • ReproducedResults, Millisecond pulse-locking dynamics differentiate learning-induced plasticity, paragraph 3; Figure 5, F and G

    PL modulation over the 700 ms train increases from early to late at 4, 5 and 6 uA, and PL time to maximum firing rate decreases at 5 and 6 uA.

    Specifically, PL modulation increased from early to late phases across all current amplitudes

    Results, Millisecond pulse-locking dynamics differentiate learning-induced plasticity, paragraph 3; Figure 5, F and G

    What the paper reports

    • modulation, 4 uA: two-sided Mann-Whitney U test, r = 0.23, p = 3.3 × 10⁻⁵ (n = 136 early, 469 late)
    • modulation, 5 uA: two-sided Mann-Whitney U test, r = 0.20, p = 2.4 × 10⁻⁴ (n = 188 early, 285 late)
    • modulation, 6 uA: two-sided Mann-Whitney U test, r = 0.26, p = 6.4 × 10⁻⁵ (n = 179 early, 135 late)
    • latency, 4 uA: two-sided Mann-Whitney U test, r = -0.05, p = 0.35
    • latency, 5 uA: two-sided Mann-Whitney U test, r = -0.15, p = 0.0059
    • latency, 6 uA: two-sided Mann-Whitney U test, r = -0.14, p = 0.04

    PL z rises at 4, 5 and 6 uA (p = 2.64e-05, 0.000333, 0.000345; r = 0.24, 0.19, 0.23). PL time to maximum falls at 5 uA (75 to 30 ms, p = 0.0031, r = -0.16) and 6 uA (75 to 35 ms, p = 0.041, r = -0.13), not at 4 uA (p = 0.0858). PL n are 137/468, 187/300, 180/141 against the paper's 136/469, 188/285, 179/135; 1413 of 2267 modulated responses at 4 to 6 uA are PL (0.623). Counting the 7 PL calls that peak in the last two unblanked bins as NPL gives the same results.

    How the test differed from the paper’s method

    • Pulse-locking follows the paper's code (processing/batch_process/postprocessing/responses_v2/pulse_locked_response_metrics.py, called from responses_v3/stim_condition_response.py, XieLuanLab/icms-activation-plasticity at d1922d1): 500 draws of 20% of the pulses without replacement, two points inserted at 1.5 ms, and a null of 100 uniform placements of the spikes of one 20% draw divided by the pulses drawn. The claims file's common method says only 'the same spike count'; round 1 used the spikes of all pulses, which narrowed the null and called 80% of responses PL. Pulses are taken 10 ms apart from the train onset, as the files store train onsets only (every 70-pulse train lasts 700.00 ms).
  • ReproducedResults, Millisecond pulse-locking dynamics differentiate learning-induced plasticity, paragraph 3; fig. S4, D and E

    NPL responses show no significant change in modulation or time to maximum firing rate over training at any current.

    whereas NPL responses showed no significant changes (fig. S4, D and E)

    Results, Millisecond pulse-locking dynamics differentiate learning-induced plasticity, paragraph 3; fig. S4, D and E

    What the paper reports

    • modulation, 4 uA: two-sided Mann-Whitney U, r = 0.12, p = 0.093 (n = 53 early, 260 late)
    • modulation, 5 uA: two-sided Mann-Whitney U, r = 0.02, p = 0.38 (n = 95 early, 204 late)
    • modulation, 6 uA: two-sided Mann-Whitney U, r = 0.08, p = 0.15 (n = 124 early, 115 late)
    • time to maximum, 4 uA: two-sided Mann-Whitney U, r = -0.03, p = 0.38
    • time to maximum, 5 uA: two-sided Mann-Whitney U, r = 0.00, p = 0.51
    • time to maximum, 6 uA: two-sided Mann-Whitney U, r = 0.08, p = 0.85

    NPL z does not change (p = 0.185, 0.756, 0.174; r = 0.11, 0.02, 0.10) nor does NPL time to maximum (p = 0.0824, 0.386, 0.942). NPL n are 54/270, 97/198, 120/115 against the paper's 53/260, 95/204, 124/115.

    How the test differed from the paper’s method

    • Pulse-locking follows the paper's code (processing/batch_process/postprocessing/responses_v2/pulse_locked_response_metrics.py, called from responses_v3/stim_condition_response.py, XieLuanLab/icms-activation-plasticity at d1922d1): 500 draws of 20% of the pulses without replacement, two points inserted at 1.5 ms, and a null of 100 uniform placements of the spikes of one 20% draw divided by the pulses drawn. The claims file's common method says only 'the same spike count'; round 1 used the spikes of all pulses, which narrowed the null and called 80% of responses PL. Pulses are taken 10 ms apart from the train onset, as the files store train onsets only (every 70-pulse train lasts 700.00 ms).
  • ReproducedResults, Millisecond pulse-locking dynamics differentiate learning-induced plasticity, paragraph 4; fig. S5, E to H

    In a 120 ms window after stimulation onset, before movement, PL modulation still increases at all currents while NPL modulation does not change, and PL time to maximum falls at 4 and 5 uA.

    This analysis yielded results consistent with those obtained using the longer 700-ms window (fig. S5, E to H)

    Results, Millisecond pulse-locking dynamics differentiate learning-induced plasticity, paragraph 4; fig. S5, E to H

    What the paper reports

    • PL modulation, 4 uA: two-sided Mann-Whitney U, r = 0.25, p = 2.2 × 10⁻⁵ (n = 127 early, 435 late)
    • PL modulation, 5 uA: two-sided Mann-Whitney U, r = 0.20, p = 4.7 × 10⁻⁴ (n = 164 early, 276 late)
    • PL modulation, 6 uA: two-sided Mann-Whitney U, r = 0.29, p = 2 × 10⁻⁵ (n = 165 early, 127 late)
    • NPL modulation, 4 uA: two-sided Mann-Whitney U, r = 0.16, p = 0.075 (n = 52 early, 225 late)
    • NPL modulation, 5 uA: two-sided Mann-Whitney U, r = 0.14, p = 0.089 (n = 83 early, 147 late)
    • NPL modulation, 6 uA: two-sided Mann-Whitney U, r = 0.02, p = 0.81 (n = 103 early, 104 late)
    • PL time to maximum, 4 uA: two-sided Mann-Whitney U, r = -0.12, p = 0.038
    • PL time to maximum, 5 uA: two-sided Mann-Whitney U, r = -0.18, p = 0.0017
    • PL time to maximum, 6 uA: two-sided Mann-Whitney U, r = -0.02, p = 0.77
    • NPL time to maximum, 4 uA: two-sided Mann-Whitney U, r = -0.07, p = 0.45
    • NPL time to maximum, 5 uA: two-sided Mann-Whitney U, r = -0.01, p = 0.9
    • NPL time to maximum, 6 uA: two-sided Mann-Whitney U, r = -0.03, p = 0.7

    Reading keep (700 ms modulated set and labels, 120 ms z), PL z rises at 4, 5, 6 uA (p = 0.000508, 0.000218, 0.000116), NPL z does not change (p = 0.254, 0.633, 0.776), PL time to maximum falls at 4 and 5 uA (p = 0.0107, 0.0119) and not at 6 (p = 0.994), NPL time to maximum does not change (p = 0.992, 0.964, 0.607), all as the paper reports. Reading recompute (modulated and PL from the 120 ms window), PL z rises at 4 and 5 uA (p = 0.000476, 0.00113) and not 6 (p = 0.14), and NPL z rises at all three (p = 0.0368, 0.000146, 5.56e-05). The criterion counts a match under one reading.

    How the test differed from the paper’s method

    • The paper does not say whether labels are recomputed in the 120 ms window; both readings are run and only one matches.
    • Pulse-locking follows the paper's code (processing/batch_process/postprocessing/responses_v2/pulse_locked_response_metrics.py, called from responses_v3/stim_condition_response.py, XieLuanLab/icms-activation-plasticity at d1922d1): 500 draws of 20% of the pulses without replacement, two points inserted at 1.5 ms, and a null of 100 uniform placements of the spikes of one 20% draw divided by the pulses drawn. The claims file's common method says only 'the same spike count'; round 1 used the spikes of all pulses, which narrowed the null and called 80% of responses PL. Pulses are taken 10 ms apart from the train onset, as the files store train onsets only (every 70-pulse train lasts 700.00 ms).
  • ReproducedResults, Differential behavioral correlates of PL and NPL neurons, paragraph 4; Figure 6F

    PL population coupling increases from early to late while NPL coupling stays stable, and NPL units have higher population coupling than PL units in both phases.

    PL coupling increased over time ( Fig. 6F ; n early = 494, n late = 899, P FDR = 1.95 × 10 −6 , r = 0.16) while NPL coupling remained stable

    Results, Differential behavioral correlates of PL and NPL neurons, paragraph 4; Figure 6F

    What the paper reports

    • PL early against late: two-sided Mann-Whitney U test with FDR-BH correction, r = 0.16, p = 1.9 × 10⁻⁶, FDR-BH-corrected (n = 494 early, 899 late)
    • NPL early against late: two-sided Mann-Whitney U test with FDR-BH correction, r = -0.04, p = 0.33, FDR-BH-corrected (n = 263 early, 553 late)
    • NPL against PL, early: two-sided Mann-Whitney U test with FDR-BH correction, r = 0.46, p = 3.4 × 10⁻²⁵, FDR-BH-corrected
    • NPL against PL, late: two-sided Mann-Whitney U test with FDR-BH correction, r = 0.31, p = 1.7 × 10⁻²², FDR-BH-corrected

    PL coupling rises (median -0.134 to 0.481, n = 503 and 906, FDR p = 0.000671, r = 0.11), NPL does not change (1.593 to 1.468, n = 270 and 581, FDR p = 0.303), and NPL exceeds PL early (FDR p = 6.08e-22, r = 0.42) and late (FDR p = 3.83e-22, r = 0.30). Paper n are 494/899 PL and 263/553 NPL. NPL is above PL in every mouse.

    How the test differed from the paper’s method

    • Only the zero-lag value of the spike-triggered population rate is computed, which is the coupling the Methods define; the +-100 ms window does not enter it.
    • Pulse-locking follows the paper's code (processing/batch_process/postprocessing/responses_v2/pulse_locked_response_metrics.py, called from responses_v3/stim_condition_response.py, XieLuanLab/icms-activation-plasticity at d1922d1): 500 draws of 20% of the pulses without replacement, two points inserted at 1.5 ms, and a null of 100 uniform placements of the spikes of one 20% draw divided by the pulses drawn. The claims file's common method says only 'the same spike count'; round 1 used the spikes of all pulses, which narrowed the null and called 80% of responses PL. Pulses are taken 10 ms apart from the train onset, as the files store train onsets only (every 70-pulse train lasts 700.00 ms).
  • ReproducedResults, Differential behavioral correlates of PL and NPL neurons, paragraph 4; fig. S9A

    In the 700 ms window of fig. S9A, PL population coupling increases from early to late while NPL coupling does not change, and PL coupling is lower than NPL coupling in both phases.

    PL coupling increased significantly over time (PL early vs late

    Results, Differential behavioral correlates of PL and NPL neurons, paragraph 4; fig. S9A

    What the paper reports

    • PL early against late: two-sided Mann-Whitney U with FDR-BH correction, r = 0.17, p = 1.3 × 10⁻⁶, FDR-BH-corrected (n = 446 early, 763 late)
    • NPL early against late: two-sided Mann-Whitney U with FDR-BH correction, r = -0.05, p = 0.22, FDR-BH-corrected (n = 255 early, 547 late)
    • PL against NPL, early: two-sided Mann-Whitney U with FDR-BH correction, r = 0.46, p = 5.5 × 10⁻²⁴, FDR-BH-corrected
    • PL against NPL, late: two-sided Mann-Whitney U with FDR-BH correction, r = 0.28, p = 3.4 × 10⁻¹⁷, FDR-BH-corrected

    Reading (a), without Mouse 6, PL rises (-0.206 to 0.513, n = 453 and 769, FDR p = 7.76e-06), NPL does not change (FDR p = 0.366), PL below NPL early (FDR p = 2.48e-22) and late (FDR p = 5.6e-19). Reading (b), population of PL units only, gives the same (FDR p = 4.84e-05, 0.24, 1.49e-22, 2.75e-19). The legend's PL n are 446 and 763.

    How the test differed from the paper’s method

    • Pulse-locking follows the paper's code (processing/batch_process/postprocessing/responses_v2/pulse_locked_response_metrics.py, called from responses_v3/stim_condition_response.py, XieLuanLab/icms-activation-plasticity at d1922d1): 500 draws of 20% of the pulses without replacement, two points inserted at 1.5 ms, and a null of 100 uniform placements of the spikes of one 20% draw divided by the pulses drawn. The claims file's common method says only 'the same spike count'; round 1 used the spikes of all pulses, which narrowed the null and called 80% of responses PL. Pulses are taken 10 ms apart from the train onset, as the files store train onsets only (every 70-pulse train lasts 700.00 ms).
  • ReproducedResults, Differential behavioral correlates of PL and NPL neurons, paragraph 4; fig. S9, B and Cverifier disagreed at firstrests on an assumption

    NPL coupling exceeds PL coupling also within the 120 ms premotion window, and the difference is absent in control animals without behavioural learning.

    This difference remained robust when computed within the 120-ms premotion time window (fig. S9B) and was absent in control animals without behavioral learning (fig. S9C)

    Results, Differential behavioral correlates of PL and NPL neurons, paragraph 4; fig. S9, B and C

    What the paper reports

    • 120 ms window, early: two-sided Mann-Whitney U with Benjamini-Hochberg correction, r = 0.37, p = 7.4 × 10⁻⁹, FDR-BH-corrected (n = 264 PL, 125 NPL)
    • 120 ms window, late: two-sided Mann-Whitney U with Benjamini-Hochberg correction, r = 0.31, p = 1.7 × 10⁻¹¹, FDR-BH-corrected (n = 484 PL, 255 NPL)
    • controls, early: two-sided Mann-Whitney U with Benjamini-Hochberg correction, r = -0.10, p = 0.48, FDR-BH-corrected (n = 49 PL, 64 NPL)
    • controls, late: two-sided Mann-Whitney U with Benjamini-Hochberg correction, r = 0.13, p = 0.48, FDR-BH-corrected (n = 25 PL, 128 NPL)

    The 120 ms part: NPL coupling exceeds PL early (0.751 against -0.520, FDR p = 1.12e-14, r = 0.35) and late (0.420 against -0.349, FDR p = 1.04e-15, r = 0.26). The control part at 5 uA, with the code's population of all units and segments from the first to the last pulse of each trial (s1, p1): PL and NPL coupling do not differ early (2.03 against 1.56, n 45 and 48, FDR p = 0.431) or late (2.46 against 1.58, n 31 and 90, FDR p = 0.134); paper n 49/64 and 25/128, FDR p = 0.48. With modulated units as the population, FDR p = 0.99 in both phases. With burst segments (s2) the early difference is significant with all units (FDR p = 0.00428, n 41 and 18) and not with modulated units (0.173). The outcome rests on the trial segments: the criterion says the full train, the dataset's trials table has one row per trial of 3 bursts (as many rows as such trials in 14 of the 15 files with stimulation, n_pulses 30 in most), and the s1 n are closer to the paper's in both phases (45/48 and 31/90 against 41/18 and 28/46). The previous revision numbered trials by the stored trial_index, which restarts in each block, and so joined trials of different blocks into one segment; the s1 numbers reviewed in round 2 came from those segments.

    How the test differed from the paper’s method

    • Control units follow the paper's control code (processing/control/postprocessing/create_session_responses.py and process_control_responses.py, python/fig_s7/generate_figure.py, XieLuanLab/icms-activation-plasticity at d1922d1): each 200 ms burst is a train, the baseline is the 700 ms before it, stimulation spikes are the unblanked ones in 20 ms pulse windows, pulse-locking uses 20 ms windows, filters P < 0.05, z > 0, baseline at least 0.5 Hz, more than 5 trains, more than 15 pulse-window spikes, weeks below 5, 5 uA; weeks from each animal's first ephys session, the first dates the code lists (python/utils/config.py). The code's timing file (control_timing_params.json) is not in the repository: the 20 ms window comes from the code's docstring, the 1.5 and 0.5 ms blanking is the behavioural one, and time to maximum is taken over the burst. These are assumptions.
    • Control coupling follows processing/pop_coupling/pop_coupling_control.py: per channel and current, segments from the first to the last pulse of each row of the code's trial table concatenated, population of every unit with a response at that condition, 50-spike minimum. The trial table is not public; its rows are taken as trials (3 bursts), an assumption, and bursts are shown as the other reading. Trials are numbered from burst_index_in_trial in time order, as the stored trial_index restarts in each recording block.
    • Pulse-locking follows the paper's code (processing/batch_process/postprocessing/responses_v2/pulse_locked_response_metrics.py, called from responses_v3/stim_condition_response.py, XieLuanLab/icms-activation-plasticity at d1922d1): 500 draws of 20% of the pulses without replacement, two points inserted at 1.5 ms, and a null of 100 uniform placements of the spikes of one 20% draw divided by the pulses drawn. The claims file's common method says only 'the same spike count'; round 1 used the spikes of all pulses, which narrowed the null and called 80% of responses PL. Pulses are taken 10 ms apart from the train onset, as the files store train onsets only (every 70-pulse train lasts 700.00 ms).
  • ReproducedResults, Millisecond pulse-locking dynamics differentiate learning-induced plasticity, paragraph 4; fig. S7rests on an assumption

    In control animals stimulated without behavioural training, neither PL nor NPL responses change in modulation, time to maximum firing rate or PL peak spike probability.

    in the control group without behavioral learning, neither PL nor NPL cells showed significant changes in any of these metrics (fig. S7)

    Results, Millisecond pulse-locking dynamics differentiate learning-induced plasticity, paragraph 4; fig. S7

    What the paper reports

    • PL modulation: two-sided Mann-Whitney U, r = 0.05, p = 0.83 (n = 29 early, 12 late)
    • NPL modulation: two-sided Mann-Whitney U, r = 0.29, p = 0.061 (n = 30 early, 29 late)
    • PL time to maximum: two-sided Mann-Whitney U, r = -0.39, p = 0.05
    • NPL time to maximum: two-sided Mann-Whitney U, r = -0.23, p = 0.13
    • PL maximum spike probability: two-sided Mann-Whitney U, r = 0.10, p = 0.62

    Reading (a), 5 uA (the code's current): 111 modulated control responses in 14 sessions of 4 mice, PL 32 early and 19 late, NPL 27 and 33 (paper 29/12 and 30/29). PL modulation p = 0.946 (r = -0.01), NPL modulation p = 0.122 (r = 0.23), PL time to maximum p = 0.112 (r = 0.25), NPL time to maximum p = 0.399 (r = 0.13), PL peak spike probability p = 0.182 (r = -0.23); none is significant. Reading (b), all currents pooled (n 59/73 PL, 74/103 NPL, further from the paper's), gives NPL modulation p = 0.00809 and PL time to maximum p = 0.0352. The criterion judges under the reading whose n is closest to the paper's, (a). The bursts of a trial start 0.5 s apart, so the 700 ms baseline holds an earlier burst for 5863 of 8795 bursts. With every burst's baseline taken from the 700 ms before its trial's first burst (variant, same PL labels), NPL modulation rises (0.334 to 0.988, p = 0.0059, r = 0.43, n 29 and 28) and the other four tests give p = 0.744, 0.957, 0.44 and 0.07. The absence of a change in NPL modulation therefore holds with the code's baseline and not with a baseline free of stimulation; the claim of absence is judged on the code's test as followed, and the variant is reported. PL time to maximum rises here (5 to 55 ms, r = 0.25), where the paper reports r = -0.39.

    How the test differed from the paper’s method

    • Control units follow the paper's control code (processing/control/postprocessing/create_session_responses.py and process_control_responses.py, python/fig_s7/generate_figure.py, XieLuanLab/icms-activation-plasticity at d1922d1): each 200 ms burst is a train, the baseline is the 700 ms before it, stimulation spikes are the unblanked ones in 20 ms pulse windows, pulse-locking uses 20 ms windows, filters P < 0.05, z > 0, baseline at least 0.5 Hz, more than 5 trains, more than 15 pulse-window spikes, weeks below 5, 5 uA; weeks from each animal's first ephys session, the first dates the code lists (python/utils/config.py). The code's timing file (control_timing_params.json) is not in the repository: the 20 ms window comes from the code's docstring, the 1.5 and 0.5 ms blanking is the behavioural one, and time to maximum is taken over the burst. These are assumptions.
    • The controls were stimulated at 2, 5, 7 and 10 uA, so reading (b) pools all four currents.
    • The 700 ms baseline before each burst holds the previous burst for the second and third burst of a trial (5863 of 8795 bursts). A baseline from the 700 ms before the trial's first burst is run as a variant; whether the code's missing timing file sets another baseline is asked of the authors (findings).

Partially reproduced4

  • Partially reproducedResults, Chronic electrophysiology confirms learning-driven strengthening of neuromodulation, paragraph 3; fig. S3Cverifier disagreed at first

    Time to maximum firing rate of all modulated responses decreases at 5 uA but not significantly at 4 or 6 uA.

    A decrease was observed at 5 μA (p = 0.012, r = -0.10) but not with statistical significance at 4 μA (p = 0.18, r = -0.04) or 6 μA (p = 0.19, r = -0.04)

    Results, Chronic electrophysiology confirms learning-driven strengthening of neuromodulation, paragraph 3; fig. S3C

    What the paper reports

    • 5 uA: two-sided Mann-Whitney U, r = -0.10, p = 0.012
    • 4 uA: two-sided Mann-Whitney U, r = -0.04, p = 0.18
    • 6 uA: two-sided Mann-Whitney U, r = -0.04, p = 0.19

    Time to maximum falls at 5 uA (80 to 45 ms, p = 0.00522, r = -0.12) and not significantly at 6 uA (105 to 75 ms, p = 0.168), but also falls at 4 uA (75 to 55 ms, p = 0.0465, r = -0.09; paper p = 0.18), so the absence at 4 uA does not hold.

  • Partially reproducedResults, Millisecond pulse-locking dynamics differentiate learning-induced plasticity, paragraph 2; Figure 5Everifier disagreed at first

    Pyramidal cells are more common among PL units (76.2%) than among NPL units (49.8%).

    Pyramidal cells are more prevalent among PL units than NPL units ( Fig. 5E

    Results, Millisecond pulse-locking dynamics differentiate learning-induced plasticity, paragraph 2; Figure 5E

    What the paper reports

    • two-sided Fisher's exact test, p = 1.6 × 10⁻⁹

    Pyramidal fraction 0.815 of 270 PL units (within tolerance) and 0.553 of 215 NPL units (0.005 above the tolerance of 0.448 to 0.548), Fisher exact p = 7.73e-10, odds ratio 3.55; 3 PL units of unknown type left out. With three other seeds of the PL call, NPL 0.565, 0.559 and 0.548 and PL 0.813 to 0.826. With trough-to-peak cell types (variant), 0.791 and 0.549, p = 1.67e-08. The effect holds; one fraction is just outside its tolerance.

    How the test differed from the paper’s method

    • Pulse-locking follows the paper's code (processing/batch_process/postprocessing/responses_v2/pulse_locked_response_metrics.py, called from responses_v3/stim_condition_response.py, XieLuanLab/icms-activation-plasticity at d1922d1): 500 draws of 20% of the pulses without replacement, two points inserted at 1.5 ms, and a null of 100 uniform placements of the spikes of one 20% draw divided by the pulses drawn. The claims file's common method says only 'the same spike count'; round 1 used the spikes of all pulses, which narrowed the null and called 80% of responses PL. Pulses are taken 10 ms apart from the train onset, as the files store train onsets only (every 70-pulse train lasts 700.00 ms).
    • Cell type is the stored cell_type (the files' t2p classification); the rule applied to the stored mean waveform is a variant.
  • Partially reproducedResults, Millisecond pulse-locking dynamics differentiate learning-induced plasticity, paragraph 2; fig. S4, B and C

    PL spike latency increases linearly with distance from the stimulation site, giving conduction velocities of about 0.2 to 0.27 m/s; latency jitter does not vary with distance.

    Linear fits to the monotonically increasing segment yielded conduction velocities of 0.2 to 0.27 m/s

    Results, Millisecond pulse-locking dynamics differentiate learning-induced plasticity, paragraph 2; fig. S4, B and C

    What the paper reports

    • latency against distance, 4 uA: OLS regression on binned means, slope = 0.005 ms/um, r2 = 0.97, p < 0.001
    • latency against distance, 5 uA: OLS regression on binned means, slope = 0.004 ms/um, r2 = 0.95, p < 0.001
    • latency against distance, 6 uA: OLS regression on binned means, slope = 0.004 ms/um, r2 = 0.91, p = 0.001
    • jitter against distance, 4 uA: OLS regression on binned means, p = 0.095
    • jitter against distance, 5 uA: OLS regression on binned means, p = 0.43
    • jitter against distance, 6 uA: OLS regression on binned means, p = 0.53

    With the paper's mapping, PL latency rises with distance at every current: binned means over 120 to 840 um give slopes 0.00591, 0.00445 and 0.00442 ms/um (p = 0.000384, 0.000314, 0.00079; r2 0.93, 0.94, 0.91) and velocities 0.169, 0.225 and 0.226 m/s; binned medians give p = 0.000147, 0.000356, 0.000486 and velocities 0.161, 0.188 and 0.199 m/s. At 4 uA both velocities are below 0.18 m/s, so no reading has all three inside 0.18 to 0.297. Jitter shows no slope (binned p = 0.148, 0.33, 0.377; unbinned 0.79, 0.819, 0.143). Check of the mapping: the stimulation contacts lie at 0 to 520 um depth, 12 of 17 between 150 and 420 um (Methods 200 to 400 um), and the fraction of modulated responses, pooled over mice, falls from 0.711 at 60 to 120 um to 0.390 beyond 960 um; the check is not clean, as the 0 to 60 um bin is the lowest (0.315) and per mouse the fall reverses in Mouse 5 (0.31 within 240 um, 0.48 beyond 960 um) and Mouse 6 (0.08 and 0.88, not in the fit), while Mouse 5 looks local under the electrode-table mapping (0.68 and 0.56). Without Mouse 5 the 4 uA velocity is 0.179 m/s from binned means and medians, still below 0.18; leaving out one mouse at a time it is 0.146 to 0.189 m/s, and 0.18 or more under both aggregations only without Mouse 1 (0.189 and 0.180). With the electrode table's rel_y (round 1, variant) the contacts lie at -156 to 1559 um, the modulated fraction does not fall with distance, and no latency slope is significant (p 0.294 to 0.962).

    How the test differed from the paper’s method

    • Distance follows the paper's code (processing/batch_process/postprocessing/longitudinal_data_utils.py, add_data_to_df and correct_df_unit_location; python/fig_s4/generate_figure.py; XieLuanLab/icms-activation-plasticity at d1922d1): the contact at (32 - stim_channel) x 60 um up the probe, the unit at unit_y_um, both converted to cortical depth with the animal's length of probe outside the brain and cos 30 degrees, responses at or above the surface dropped, Mouse 6 left out (the code has no length for it), PL responses with the z cap of 100. Responses at 120 to 840 um are binned in 120 um bins from 60 um, as the code does.
    • Pulse-locking follows the paper's code (processing/batch_process/postprocessing/responses_v2/pulse_locked_response_metrics.py, called from responses_v3/stim_condition_response.py, XieLuanLab/icms-activation-plasticity at d1922d1): 500 draws of 20% of the pulses without replacement, two points inserted at 1.5 ms, and a null of 100 uniform placements of the spikes of one 20% draw divided by the pulses drawn. The claims file's common method says only 'the same spike count'; round 1 used the spikes of all pulses, which narrowed the null and called 80% of responses PL. Pulses are taken 10 ms apart from the train onset, as the files store train onsets only (every 70-pulse train lasts 700.00 ms).
  • Partially reproducedResults, Millisecond pulse-locking dynamics differentiate learning-induced plasticity, paragraph 3; Figure 5Hverifier disagreed at first

    PL responses show increased peak spike probability within the 10 ms interpulse interval over training at 5 and 6 uA, not at 4 uA.

    At the finer 10-ms interpulse scale, PL responses also showed increased peak spike probability over time at 5 and 6 μA

    Results, Millisecond pulse-locking dynamics differentiate learning-induced plasticity, paragraph 3; Figure 5H

    What the paper reports

    • 4 uA: two-sided Mann-Whitney U test, r = 0.07, p = 0.22
    • 5 uA: two-sided Mann-Whitney U test, r = 0.12, p = 0.028
    • 6 uA: two-sided Mann-Whitney U test, r = 0.17, p = 0.01

    PL peak spike probability rises at 6 uA (0.047 to 0.061, p = 0.0113, r = 0.16) but not significantly at 5 uA (0.045 to 0.051, p = 0.0613, r = 0.10); 4 uA p = 0.105, as the paper reports no change there. The 5 uA test rests on the random PL call: three other seeds give p = 0.0197, 0.0275 and 0.0485, and counting late-peaking PL calls as NPL gives p = 0.0499.

    How the test differed from the paper’s method

    • Pulse-locking follows the paper's code (processing/batch_process/postprocessing/responses_v2/pulse_locked_response_metrics.py, called from responses_v3/stim_condition_response.py, XieLuanLab/icms-activation-plasticity at d1922d1): 500 draws of 20% of the pulses without replacement, two points inserted at 1.5 ms, and a null of 100 uniform placements of the spikes of one 20% draw divided by the pulses drawn. The claims file's common method says only 'the same spike count'; round 1 used the spikes of all pulses, which narrowed the null and called 80% of responses PL. Pulses are taken 10 ms apart from the train onset, as the files store train onsets only (every 70-pulse train lasts 700.00 ms).

Not reproduced4

  • Not reproducedResults, Chronic electrophysiology confirms learning-driven strengthening of neuromodulation, paragraphs 1, 3 and 4; Figure 4Hverifier disagreed at first

    Electrophysiology recorded 973 units in six animals (672 putative pyramidal, 301 interneurons), of which 484 were significantly modulated by the stimulation train (311 pyramidal, 173 interneurons).

    Modulated units ( n = 484; 311 pyramidal, 173 interneuron) showed a bimodal spike-width distribution similar to the full recorded population ( n = 973; 672 pyramidal, 301 interneuron)

    Results, Chronic electrophysiology confirms learning-driven strengthening of neuromodulation, paragraphs 1, 3 and 4; Figure 4H

    Weeks 0 to 4, reading (a) unit-sessions, 795 units (584 pyramidal, 204 interneurons, 7 unknown), 488 modulated (339 pyramidal, 146 interneurons). The modulated total and the modulated pyramidal count are within tolerance; the totals of all, pyramidal and interneuron units and the modulated interneurons are below it (2 of 6, the criterion asks 5). Over all 55 sessions, 999 units (732, 260) and 585 modulated (405, 177), 3 of 6. With the Methods filters alone the counts are the same. With the trough-to-peak rule on the stored mean waveform, 577 pyramidal and 218 interneurons, 334 and 154 modulated. Every unit is marked accepted. Reading (b) cannot be formed, as no unit identity across sessions is stored. The outcome rests on the counts, 4 of 6 outside their tolerance where the criterion asks 5 inside. The trough-to-peak histogram in 0.05 ms bins alternates (59, 22, 44 at 0.40 to 0.55 ms), but at 30 kHz a bin holds one or two possible values, and per value the counts are 29.5, 22 and 22, so the dip comes from the binning; no bimodality is judged.

    How the test differed from the paper’s method

    • Cell type is the stored cell_type, which the files describe as the putative cell type from t2p classification; Mouse 6 spells it 'Pyramidal Cell' and 'Narrow Interneuron', read as the same labels. By the trough-to-peak of the stored single-channel mean waveform, 72 units change class (shown as a variant).
  • Not reproducedResults, Millisecond pulse-locking dynamics differentiate learning-induced plasticity, paragraph 2; Figure 5D

    Over 80% of units keep their PL or NPL identity at a given stimulation contact as the current rises from 4 to 6 uA.

    Over 80% of units retained their PL and NPL identity at a given stimulation contact as current amplitude increased ( Fig. 5D )

    Results, Millisecond pulse-locking dynamics differentiate learning-induced plasticity, paragraph 2; Figure 5D

    740 unit-channel pairs with modulated responses at two or more of 4 to 6 uA; 389 all PL, 197 all NPL, 49 NPL to PL, 89 PL to NPL, 16 mixed; fraction keeping identity 0.792 (0.72 to 0.908 by mouse), below the 0.80 the claim states. With three other seeds of the PL call, 0.774, 0.793 and 0.789.

    How the test differed from the paper’s method

    • Pulse-locking follows the paper's code (processing/batch_process/postprocessing/responses_v2/pulse_locked_response_metrics.py, called from responses_v3/stim_condition_response.py, XieLuanLab/icms-activation-plasticity at d1922d1): 500 draws of 20% of the pulses without replacement, two points inserted at 1.5 ms, and a null of 100 uniform placements of the spikes of one 20% draw divided by the pulses drawn. The claims file's common method says only 'the same spike count'; round 1 used the spikes of all pulses, which narrowed the null and called 80% of responses PL. Pulses are taken 10 ms apart from the train onset, as the files store train onsets only (every 70-pulse train lasts 700.00 ms).
  • Not reproducedResults, Differential behavioral correlates of PL and NPL neurons, paragraph 2; Figure 6C

    Hit-miss separation of delta-spikes distributions (KL divergence) increases over time for both PL and NPL responses, and NPL responses separate hit from miss more than PL responses in both early and late phases.

    While both PL and NPL separation between hit and miss trials increased over time ... NPL responses showed greater separation between hit and miss trials than PL responses in both early ... and late ... phases

    Results, Differential behavioral correlates of PL and NPL neurons, paragraph 2; Figure 6C

    What the paper reports

    • PL early against late: two-sided Mann-Whitney U test with Benjamini-Hochberg FDR correction, r = 0.18, p = 7.2 × 10⁻²⁶, FDR-BH-corrected
    • NPL early against late: two-sided Mann-Whitney U test with Benjamini-Hochberg FDR correction, r = 0.05, p = 0.0061, FDR-BH-corrected
    • NPL against PL, early: two-sided Mann-Whitney U test with Benjamini-Hochberg FDR correction, r = 1.00, p = 7.3 × 10⁻¹¹⁶, FDR-BH-corrected
    • NPL against PL, late: two-sided Mann-Whitney U test with Benjamini-Hochberg FDR correction, r = 0.34, p = 7.3 × 10⁻¹¹⁶, FDR-BH-corrected

    Reading k1, median KL early against late, PL 0.0403 to 0.0279 (FDR p = 3e-192, r = -0.49), falling, and NPL 0.2089 to 0.2204 (FDR p = 5.26e-08, r = 0.09), rising. NPL exceeds PL in both phases (FDR p = 0). Reading k2, PL 0.0586 to 0.0348 and NPL 0.4185 to 0.2192, both falling, NPL above PL in both phases (FDR p = 0 and 1.81e-115). PL separation does not rise under either reading.

    How the test differed from the paper’s method

    • Trials are pooled over the modulated responses of a week and label; a constant of 1e-10 is added to empty histogram bins, a standard choice.
    • Pulse-locking follows the paper's code (processing/batch_process/postprocessing/responses_v2/pulse_locked_response_metrics.py, called from responses_v3/stim_condition_response.py, XieLuanLab/icms-activation-plasticity at d1922d1): 500 draws of 20% of the pulses without replacement, two points inserted at 1.5 ms, and a null of 100 uniform placements of the spikes of one 20% draw divided by the pulses drawn. The claims file's common method says only 'the same spike count'; round 1 used the spikes of all pulses, which narrowed the null and called 80% of responses PL. Pulses are taken 10 ms apart from the train onset, as the files store train onsets only (every 70-pulse train lasts 700.00 ms).
  • Not reproducedResults, Differential behavioral correlates of PL and NPL neurons, paragraph 3; Figure 6D

    PL responses show no significant hit-miss divergence in firing rate at any time, while NPL responses diverge from 65 ms after ICMS onset early in training and from 35 ms late in training, both before movement onset.

    PL responses did not show any significant hit-miss divergence at any time point. In contrast, NPL responses diverged significantly beginning at 65 ms post-ICMS onset during the early period. With training, the onset of divergence in NPL neurons shifted earlier to 35 ms

    Results, Differential behavioral correlates of PL and NPL neurons, paragraph 3; Figure 6D

    PL responses diverge between hit and miss in both phases (10 ms bins: significant clusters from 365 ms early and from 5 ms late; 5 ms bins: from 107.5 and 2.5 ms). NPL onsets with 10 ms bins are 75 ms early (223 rows), at the edge of 65 +- 10, and 5 ms late (506 rows, a single-bin cluster) for the first cluster (o1); 385 and 45 ms for the last (o2). With 5 ms bins, 87.5 and 7.5 ms (o1), 637.5 and 437.5 ms (o2). No reading has both NPL onsets within tolerance, and PL has significant clusters under all of them.

    How the test differed from the paper’s method

    • The PSTH bin width and smoothing are not in the paper or its code (claims); 10 and 5 ms bins without smoothing are run, and both fail on the PL part.
    • Labels are permuted by swapping a response's hit and miss rows, a standard choice for paired rows; the code's scheme is not in the claims file.
    • Pulse-locking follows the paper's code (processing/batch_process/postprocessing/responses_v2/pulse_locked_response_metrics.py, called from responses_v3/stim_condition_response.py, XieLuanLab/icms-activation-plasticity at d1922d1): 500 draws of 20% of the pulses without replacement, two points inserted at 1.5 ms, and a null of 100 uniform placements of the spikes of one 20% draw divided by the pulses drawn. The claims file's common method says only 'the same spike count'; round 1 used the spikes of all pulses, which narrowed the null and called 80% of responses PL. Pulses are taken 10 ms apart from the train onset, as the files store train onsets only (every 70-pulse train lasts 700.00 ms).

Not tested in a notebook 19

17 metadata insufficient, 2 data missing from the dataset

Blocked19

  • BlockedResults, Behavioral learning enhances ICMS-evoked population activation over time, paragraph 3; Figure 2C· Metadata insufficient

    At each fixed current (4, 5 and 6 uA), more neurons are activated by ICMS in the late phase than in the early phase.

    resulting in significantly larger activated populations at all current levels in the late learning phase than in the early phase

    Results, Behavioral learning enhances ICMS-evoked population activation over time, paragraph 3; Figure 2C

    What the paper reports

    • 4 uA: two-sided Mann-Whitney U test, r = 0.28, p = 0.043 (n = 13 early, 41 late)
    • 5 uA: two-sided Mann-Whitney U test, r = 0.33, p = 0.015 (n = 20 early, 35 late)
    • 6 uA: two-sided Mann-Whitney U test, r = 0.39, p = 0.0069 (n = 22 early, 26 late)

    The behavioural files hold the volumetric ROI masks (voxel_mask in pixel and plane indices) and per-ROI dF/F, but no position of the stimulation contacts in the imaging volume (the electrode table gives only positions along the probe, rel_y), no activation calls, and no voxel-level responses, which the activation rule (at least 50% of an ROI's voxels above 2 x baseline STD) needs. Distances from the stimulation site, and the paper's counts within 800 um, cannot be formed.

  • BlockedResults, Behavioral learning enhances ICMS-evoked population activation over time, paragraph 3; Figure 2B· Metadata insufficient

    In one representative animal at 4 uA, the number of activated neurons in the same field of view rises from 71 at week 0 to 114 at week 1 and 220 at week 2.

    In one representative animal at a fixed stimulation current of 4 μA, the number of ICMS-activated neurons increased over time within the same FOV, with progressively more cells recruited from week 0 (71 neurons) to week 1 (114 neurons) and to week 2 (220 neurons)

    Results, Behavioral learning enhances ICMS-evoked population activation over time, paragraph 3; Figure 2B

    The behavioural files hold the volumetric ROI masks (voxel_mask in pixel and plane indices) and per-ROI dF/F, but no position of the stimulation contacts in the imaging volume (the electrode table gives only positions along the probe, rel_y), no activation calls, and no voxel-level responses, which the activation rule (at least 50% of an ROI's voxels above 2 x baseline STD) needs. Distances from the stimulation site, and the paper's counts within 800 um, cannot be formed.

  • BlockedResults, Behavioral learning enhances ICMS-evoked population activation over time, paragraph 3; fig. S2, A and B· Metadata insufficient

    Activation density, as a function of distance from the stimulation site, peaks at around 200 um.

    The activation density peaked at around 200 μm (fig. S2, A and B)

    Results, Behavioral learning enhances ICMS-evoked population activation over time, paragraph 3; fig. S2, A and B

    The behavioural files hold the volumetric ROI masks (voxel_mask in pixel and plane indices) and per-ROI dF/F, but no position of the stimulation contacts in the imaging volume (the electrode table gives only positions along the probe, rel_y), no activation calls, and no voxel-level responses, which the activation rule (at least 50% of an ROI's voxels above 2 x baseline STD) needs. Distances from the stimulation site, and the paper's counts within 800 um, cannot be formed.

  • BlockedResults, Behavioral learning enhances ICMS-evoked population activation over time, paragraph 3; Figure 2E· Metadata insufficient

    At 5 uA, activation density within 200 um of the stimulation site is higher in the late phase than in the early phase.

    resulting in significantly higher activation density with learning within a radius of 200 μm ( Fig. 2E

    Results, Behavioral learning enhances ICMS-evoked population activation over time, paragraph 3; Figure 2E

    What the paper reports

    • two-sided Mann-Whitney U test, r = 0.31, p = 0.015 (n = 22 early, 40 late)

    The behavioural files hold the volumetric ROI masks (voxel_mask in pixel and plane indices) and per-ROI dF/F, but no position of the stimulation contacts in the imaging volume (the electrode table gives only positions along the probe, rel_y), no activation calls, and no voxel-level responses, which the activation rule (at least 50% of an ROI's voxels above 2 x baseline STD) needs. Distances from the stimulation site, and the paper's counts within 800 um, cannot be formed.

  • Blockedfig. S2, D and E legend· Metadata insufficient

    In the behaviourally trained animals, compared over training as for the controls, density within 200 um and the number of activated neurons at 5 uA increase.

    whereas behaviorally trained animals exhibited significant increases in activation density (two-sided Mann-Whitney U, nearly = 22, nlate = 29, p = 0.044, r = 0.28)

    fig. S2, D and E legend

    What the paper reports

    • density within 200 um: two-sided Mann-Whitney U, r = 0.28, p = 0.044 (n = 22 early, 29 late)
    • number of activated neurons at 5 uA: two-sided Mann-Whitney U, r = 0.54, p = 9 × 10⁻⁴ (n = 18 early, 20 late)

    The behavioural files hold the volumetric ROI masks (voxel_mask in pixel and plane indices) and per-ROI dF/F, but no position of the stimulation contacts in the imaging volume (the electrode table gives only positions along the probe, rel_y), no activation calls, and no voxel-level responses, which the activation rule (at least 50% of an ROI's voxels above 2 x baseline STD) needs. Distances from the stimulation site, and the paper's counts within 800 um, cannot be formed.

  • BlockedResults, Behavioral learning enhances ICMS-evoked population activation over time, paragraph 4; Figure 2, F and G· Metadata insufficient

    When the current is set to each session's detection threshold, the activation density within 200 um and the number of activated neurons do not change between learning phases.

    when the stimulation current was adjusted to the behavioral detection threshold for each session, both the activation density and number of activated neurons remained stable across learning phases

    Results, Behavioral learning enhances ICMS-evoked population activation over time, paragraph 4; Figure 2, F and G

    What the paper reports

    • density within 200 um: two-sided Mann-Whitney U test, r = 0.07, p = 0.511 (n = 32 early, 69 late)
    • number of activated neurons: two-sided Mann-Whitney U test, r = 0.11, p = 0.253 (n = 32 early, 69 late)

    The behavioural files hold the volumetric ROI masks (voxel_mask in pixel and plane indices) and per-ROI dF/F, but no position of the stimulation contacts in the imaging volume (the electrode table gives only positions along the probe, rel_y), no activation calls, and no voxel-level responses, which the activation rule (at least 50% of an ROI's voxels above 2 x baseline STD) needs. Distances from the stimulation site, and the paper's counts within 800 um, cannot be formed.

  • BlockedResults, Behavioral learning enhances ICMS-evoked population activation over time, paragraph 3; Figure 2E· Metadata insufficient

    The activation radius, the distance enclosing 50% of activated neurons, increases from the early to the late phase at 5 uA.

    we defined the distance enclosing 50% of all activated neurons as the activation radius (Materials and Methods), which also increased over time

    Results, Behavioral learning enhances ICMS-evoked population activation over time, paragraph 3; Figure 2E

    What the paper reports

    • two-sided Mann-Whitney U test, r = 0.56, p = 1.1 × 10⁻⁵

    Depends on fig2e-density-200um-increase, blocked for the same reason. The behavioural files hold the volumetric ROI masks (voxel_mask in pixel and plane indices) and per-ROI dF/F, but no position of the stimulation contacts in the imaging volume (the electrode table gives only positions along the probe, rel_y), no activation calls, and no voxel-level responses, which the activation rule (at least 50% of an ROI's voxels above 2 x baseline STD) needs. Distances from the stimulation site, and the paper's counts within 800 um, cannot be formed.

  • BlockedResults, Behavioral learning enhances ICMS-evoked population activation over time, paragraph 3; fig. S2, D and E· Metadata insufficient

    In control animals stimulated without behavioural training, activation density within 200 um and the number of activated neurons do not change from early to late weeks.

    In control animals not undergoing ICMS-driven behavioral learning, both the number and spatial distribution of activated neurons remained stable and resembled those in the initial training weeks

    Results, Behavioral learning enhances ICMS-evoked population activation over time, paragraph 3; fig. S2, D and E

    What the paper reports

    • control density within 200 um: two-sided Mann-Whitney U, r = 0.05, p = 0.808 (n = 18 early, 7 late)
    • control number of activated neurons: two-sided Mann-Whitney U, r = 0.22, p = 0.066 (n = 42 early, 26 late)

    The density half cannot be formed: the control survey files hold ROI masks and binary activation per channel and current, with contact positions in um, but no voxel size or plane spacing (the imaging plane has no grid_spacing; its description gives 51 to 171 planes), and 2 to 9 of the 32 contact positions are at the origin with no stated mapping from Ripple channel to contact index. The count half was run on the stored activation calls (all activated ROIs, as the 800 um limit cannot be applied): at 5 uA the count falls from a median of 10 early to 2 late, channel x session p = 0.0146 (late weeks 2 and 3) and 0.00573 (weeks 2 to 4), channel x week medians p = 0.0816 and 0.0261 (n 63 early, 43 to 67 late; paper p = 0.066, n 42 and 26), so it gives P >= 0.05 under one of four combinations. The files hold 24 control imaging sessions where the paper reports 18; the controls were imaged at 2, 5, 7 and 10 uA.

  • BlockedResults, Longitudinal tracking of individual neurons reveals learning-sensitive subpopulations, paragraph 1; Figure 3B· Metadata insufficient

    Of 10,608 neurons tracked in three animals, 4469 were activated by ICMS; 114 were activated at all weeks, 370 in 4 of 5 weeks, and 2063 in only one session.

    Among 10,608 tracked neurons in three animals, 4469 neurons were activated by ICMS. Among the ICMS-activated neurons, 114 were activated at all weeks, 370 neurons were activated in 4 out of 5 weeks, and 2063 were only activated in one session

    Results, Longitudinal tracking of individual neurons reveals learning-sensitive subpopulations, paragraph 1; Figure 3B

    The planar ROIs are segmented in each session (385, 172, 388, 359, 697, 329, 543 and 443 ROIs in Mouse 1), their roi_id_source is the row index 0 to N-1 in every session, so the files carry no identity of an ROI across sessions, and no mean or reference images are stored (reference_images is empty in every planar and volumetric segmentation), so the PatchWarp alignment and mask matching of the Methods cannot be repeated. The ROI description says only that the planar ROIs are a separate segmentation from the volumetric one. Every Fig. 3 and fig. S6 claim rests on tracked neurons.

  • BlockedResults, Longitudinal tracking of individual neurons reveals learning-sensitive subpopulations, paragraph 2; Figure 3, D to F· Metadata insufficient

    Neurons reliably activated and tracked across sessions show no change in dF/Fmax or response duration over training, and a decrease in fluorescence onset time.

    these longitudinally stable cells exhibited no significant changes in either Δ F / F max or fluorescence duration ... and a significant decrease in fluorescence onset times

    Results, Longitudinal tracking of individual neurons reveals learning-sensitive subpopulations, paragraph 2; Figure 3, D to F

    What the paper reports

    • dF/Fmax: two-sided Mann-Whitney U test, r < 0.01, p = 0.8478 (n = 226 cells)
    • duration: two-sided Mann-Whitney U test, r = 0.01, p = 0.8306 (n = 226 cells)
    • onset time: two-sided Mann-Whitney U test, r = 0.30, p = 5.7 × 10⁻¹¹ (n = 226 cells)

    The planar ROIs are segmented in each session (385, 172, 388, 359, 697, 329, 543 and 443 ROIs in Mouse 1), their roi_id_source is the row index 0 to N-1 in every session, so the files carry no identity of an ROI across sessions, and no mean or reference images are stored (reference_images is empty in every planar and volumetric segmentation), so the PatchWarp alignment and mask matching of the Methods cannot be repeated. The ROI description says only that the planar ROIs are a separate segmentation from the volumetric one. Every Fig. 3 and fig. S6 claim rests on tracked neurons.

  • BlockedResults, Longitudinal tracking of individual neurons reveals learning-sensitive subpopulations, paragraph 2; Figure 3I· Metadata insufficient

    By week 4, more tracked neurons have dF/Fmax above the mean plus one standard deviation, indicating enhanced excitability.

    By week 4, more neurons exhibited fluorescence intensities exceeding mean + standard deviation (STD) ( Fig. 3I ), indicating enhanced excitability

    Results, Longitudinal tracking of individual neurons reveals learning-sensitive subpopulations, paragraph 2; Figure 3I

    The planar ROIs are segmented in each session (385, 172, 388, 359, 697, 329, 543 and 443 ROIs in Mouse 1), their roi_id_source is the row index 0 to N-1 in every session, so the files carry no identity of an ROI across sessions, and no mean or reference images are stored (reference_images is empty in every planar and volumetric segmentation), so the PatchWarp alignment and mask matching of the Methods cannot be repeated. The ROI description says only that the planar ROIs are a separate segmentation from the volumetric one. Every Fig. 3 and fig. S6 claim rests on tracked neurons.

  • BlockedResults, Longitudinal tracking of individual neurons reveals learning-sensitive subpopulations, paragraph 2; Figure 3J· Metadata insufficient

    The subgroup of tracked neurons with high dF/Fmax in the final week shows a significant increase in fluorescence over training, while the remaining neurons do not.

    These neurons showed significant increases in fluorescence over training, whereas the remaining neurons (blue) did not

    Results, Longitudinal tracking of individual neurons reveals learning-sensitive subpopulations, paragraph 2; Figure 3J

    What the paper reports

    • high-intensity subgroup, early against late: two-sided Mann-Whitney U test, r = 0.32, p = 9.8 × 10⁻⁴ (n = 52 early, 57 late)
    • remaining neurons, early against late: two-sided Mann-Whitney U test, r = 0.08, p = 0.1176 (n = 174 early, 220 late)

    The planar ROIs are segmented in each session (385, 172, 388, 359, 697, 329, 543 and 443 ROIs in Mouse 1), their roi_id_source is the row index 0 to N-1 in every session, so the files carry no identity of an ROI across sessions, and no mean or reference images are stored (reference_images is empty in every planar and volumetric segmentation), so the PatchWarp alignment and mask matching of the Methods cannot be repeated. The ROI description says only that the planar ROIs are a separate segmentation from the volumetric one. Every Fig. 3 and fig. S6 claim rests on tracked neurons.

  • BlockedResults, Longitudinal tracking of individual neurons reveals learning-sensitive subpopulations, paragraph 2; Figure 3J· Metadata insufficient

    The high-intensity subgroup starts with fluorescence comparable to the rest (weeks 0 and 1), diverges by week 2, and stays stronger through weeks 3 and 4.

    The high intensity subgroup initially exhibited fluorescence comparable to the rest of the population ... but diverged over time, showing significantly stronger responses by week 2

    Results, Longitudinal tracking of individual neurons reveals learning-sensitive subpopulations, paragraph 2; Figure 3J

    What the paper reports

    • week 0: two-sided Mann-Whitney U test, r = 0.0355, p = 0.5078
    • week 1: two-sided Mann-Whitney U test, r = 0.0885, p = 0.0988
    • week 2: two-sided Mann-Whitney U test, r = 0.16, p = 0.0018
    • week 3: two-sided Mann-Whitney U test, r = 0.41, p = 8.6 × 10⁻¹⁵
    • week 4: two-sided Mann-Whitney U test, r = 0.31, p = 2.5 × 10⁻⁹

    The planar ROIs are segmented in each session (385, 172, 388, 359, 697, 329, 543 and 443 ROIs in Mouse 1), their roi_id_source is the row index 0 to N-1 in every session, so the files carry no identity of an ROI across sessions, and no mean or reference images are stored (reference_images is empty in every planar and volumetric segmentation), so the PatchWarp alignment and mask matching of the Methods cannot be repeated. The ROI description says only that the planar ROIs are a separate segmentation from the volumetric one. Every Fig. 3 and fig. S6 claim rests on tracked neurons.

  • BlockedResults, Longitudinal tracking of individual neurons reveals learning-sensitive subpopulations, paragraph 3; Figure 3, L and M· Metadata insufficient

    In deconvolved spike trains, the high-intensity subgroup's spike counts rise and its spike onset latencies fall from the early to the late phase, while the remaining neurons change little; the subgroup starts with spike counts and onset latencies comparable to the rest (weeks 0 and 1) and diverges from week 2 to week 4, with more spikes and shorter latency.

    Neurons in the high intensity subgroup exhibited a significant increase in spike counts and a decrease in spike onset latency from early to late phases, while the remaining neurons exhibited little change

    Results, Longitudinal tracking of individual neurons reveals learning-sensitive subpopulations, paragraph 3; Figure 3, L and M

    What the paper reports

    • spike count, week 0: two-sided Mann-Whitney U test, r < 0.01, p = 0.9699
    • spike count, week 1: two-sided Mann-Whitney U test, r = 0.08, p = 0.1076
    • spike count, week 2: two-sided Mann-Whitney U test, r = 0.14, p = 0.007
    • spike count, week 3: two-sided Mann-Whitney U test, r = 0.40, p = 3.1 × 10⁻¹⁴
    • spike count, week 4: two-sided Mann-Whitney U test, r = 0.30, p = 1.2 × 10⁻⁸
    • onset latency, week 0: two-sided Mann-Whitney U test, r < 0.01, p = 0.8767
    • onset latency, week 1: two-sided Mann-Whitney U test, r = 0.02, p = 0.6763
    • onset latency, week 2: two-sided Mann-Whitney U test, r = 0.10, p = 0.0442
    • onset latency, week 3: two-sided Mann-Whitney U test, r = 0.16, p = 0.0024
    • onset latency, week 4: two-sided Mann-Whitney U test, r = 0.14, p = 0.0059

    The planar ROIs are segmented in each session (385, 172, 388, 359, 697, 329, 543 and 443 ROIs in Mouse 1), their roi_id_source is the row index 0 to N-1 in every session, so the files carry no identity of an ROI across sessions, and no mean or reference images are stored (reference_images is empty in every planar and volumetric segmentation), so the PatchWarp alignment and mask matching of the Methods cannot be repeated. The ROI description says only that the planar ROIs are a separate segmentation from the volumetric one. Every Fig. 3 and fig. S6 claim rests on tracked neurons.

  • BlockedResults, Longitudinal tracking of individual neurons reveals learning-sensitive subpopulations, paragraph 4; Figure 3N· Metadata insufficient

    Neurons recruited only in the last week have lower dF/Fmax and fewer deconvolved spikes than the high-intensity subgroup in weeks 2 to 4, with similar onset time.

    Neurons recruited exclusively during the last week of training exhibited lower Δ F / F max values ... and fewer deconvolved spikes ( P = 3.29 × 10 −17 ) than the high-intensity subgroup during weeks 2 to 4 while activation onset time remained similar between the two groups ( P = 0.0633)

    Results, Longitudinal tracking of individual neurons reveals learning-sensitive subpopulations, paragraph 4; Figure 3N

    What the paper reports

    • dF/Fmax, newly recruited against subgroup at weeks 2 to 4: Kruskal-Wallis with Tukey-Kramer post hoc correction, p = 3.1 × 10⁻¹⁹ (n = 23 week-0, 57 weeks 2 to 4, 700 week-4 neurons)
    • deconvolved spikes, newly recruited against subgroup at weeks 2 to 4: Kruskal-Wallis with Tukey-Kramer post hoc correction, p = 3.3 × 10⁻¹⁷
    • onset time, newly recruited against subgroup: Kruskal-Wallis with Tukey-Kramer post hoc correction, p = 0.0633

    The planar ROIs are segmented in each session (385, 172, 388, 359, 697, 329, 543 and 443 ROIs in Mouse 1), their roi_id_source is the row index 0 to N-1 in every session, so the files carry no identity of an ROI across sessions, and no mean or reference images are stored (reference_images is empty in every planar and volumetric segmentation), so the PatchWarp alignment and mask matching of the Methods cannot be repeated. The ROI description says only that the planar ROIs are a separate segmentation from the volumetric one. Every Fig. 3 and fig. S6 claim rests on tracked neurons.

  • BlockedResults, Longitudinal tracking of individual neurons reveals learning-sensitive subpopulations, paragraph 4; Figure 3N· Metadata insufficient

    The enhanced subgroup at weeks 2 to 4 has higher dF/Fmax, more spikes and shorter spike onset than tracked neurons at week 0, and newly recruited week-4 neurons have lower dF/Fmax than tracked neurons at week 0.

    the enhanced subgroup at weeks 2 to 4 had significantly increased Δ F / F max , greater spike counts, and shorter spike onset time compared to all longitudinally tracked neurons at week 0

    Results, Longitudinal tracking of individual neurons reveals learning-sensitive subpopulations, paragraph 4; Figure 3N

    What the paper reports

    • subgroup weeks 2 to 4 against week 0, dF/Fmax: Kruskal-Wallis with Tukey-Kramer post hoc correction, p = 0.0114
    • subgroup weeks 2 to 4 against week 0, spike count: Kruskal-Wallis with Tukey-Kramer post hoc correction, p = 0.0059
    • subgroup weeks 2 to 4 against week 0, spike onset: Kruskal-Wallis with Tukey-Kramer post hoc correction, p = 0.003
    • newly recruited week 4 against week 0, dF/Fmax: Kruskal-Wallis with Tukey-Kramer post hoc correction, p = 0.0349
    • newly recruited week 4 against week 0, spike onset: Kruskal-Wallis with Tukey-Kramer post hoc correction, p = 0.479

    The planar ROIs are segmented in each session (385, 172, 388, 359, 697, 329, 543 and 443 ROIs in Mouse 1), their roi_id_source is the row index 0 to N-1 in every session, so the files carry no identity of an ROI across sessions, and no mean or reference images are stored (reference_images is empty in every planar and volumetric segmentation), so the PatchWarp alignment and mask matching of the Methods cannot be repeated. The ROI description says only that the planar ROIs are a separate segmentation from the volumetric one. Every Fig. 3 and fig. S6 claim rests on tracked neurons.

  • BlockedResults, Millisecond pulse-locking dynamics differentiate learning-induced plasticity, paragraph 4; fig. S6, B and C· Metadata insufficient

    Within the first five imaging frames after ICMS onset, both the recruitment of tracked neurons and the response amplitude of the increasing subset rise from early to late training.

    Both recruitment of tracked neurons and the response amplitude of the increasing subset ( Fig. 3J ) increased significantly from early to late training (fig. S6, B and C)

    Results, Millisecond pulse-locking dynamics differentiate learning-induced plasticity, paragraph 4; fig. S6, B and C

    What the paper reports

    • number of recruited neurons: two-sided Mann-Whitney U, r = 0.75, p = 1.8 × 10⁻⁵ (n = 15 early, 37 late animal x session x channel samples)
    • maximum dF/F of the increasing subset: two-sided Mann-Whitney U, r = 0.63, p = 4.1 × 10⁻⁴

    The planar ROIs are segmented in each session (385, 172, 388, 359, 697, 329, 543 and 443 ROIs in Mouse 1), their roi_id_source is the row index 0 to N-1 in every session, so the files carry no identity of an ROI across sessions, and no mean or reference images are stored (reference_images is empty in every planar and volumetric segmentation), so the PatchWarp alignment and mask matching of the Methods cannot be repeated. The ROI description says only that the planar ROIs are a separate segmentation from the volumetric one. Every Fig. 3 and fig. S6 claim rests on tracked neurons.

  • BlockedResults, Chronic electrophysiology confirms learning-driven strengthening of neuromodulation, paragraph 2; Figure 4D; fig. S3A· Data missing from the dataset

    Stimulation-period waveforms match the same unit's non-stimulation waveforms and stay distinct from neighbouring units; within-unit distances in PC space are smaller than between-unit distances.

    Within-unit Euclidean distances in the PC space (between stimulation and nonstimulation waveforms of the same unit; n = 243) are significantly smaller than the between-unit distances

    Results, Chronic electrophysiology confirms learning-driven strengthening of neuromodulation, paragraph 2; Figure 4D; fig. S3A

    What the paper reports

    • two-sided Mann-Whitney U test, r = 0.49, p = 1.6 × 10⁻³¹ (n = 243 within-unit, 801 between-unit distances)

    The units table holds one mean waveform per unit (90 samples on one channel); there are no waveforms of spikes in and out of stimulation and no multi-channel waveforms for the PCA.

  • BlockedResults, Millisecond pulse-locking dynamics differentiate learning-induced plasticity, paragraph 5; fig. S8, C and D· Data missing from the dataset

    UnitMatch tracking gives 34 units spanning early and late phases in six animals; tracked pairs are 6.8-fold closer in waveform than nearby non-matched units; in tracked units PL modulation increases and PL time to maximum decreases, while NPL modulation does not change.

    Analyses of the 34 tracked units support the main findings ... PL modulation increased significantly over training while NPL modulation did not, and PL time to maximum firing rate decreased (fig. S8D)

    Results, Millisecond pulse-locking dynamics differentiate learning-induced plasticity, paragraph 5; fig. S8, C and D

    What the paper reports

    • waveform distance, tracked pairs against nearby non-matched units: two-sided Mann-Whitney U, p = 6.6 × 10⁻⁸³
    • PL modulation, 5 uA: two-sided Mann-Whitney U, r = 0.19, p = 0.049
    • PL modulation, 6 uA: two-sided Mann-Whitney U, r = 0.39, p = 0.0024
    • PL time to maximum, 4 uA: two-sided Mann-Whitney U, r = -0.21, p = 0.026
    • PL time to maximum, 5 uA: two-sided Mann-Whitney U, r = -0.28, p = 0.0028
    • PL time to maximum, 6 uA: two-sided Mann-Whitney U, r = -0.28, p = 0.03

    UnitMatch needs each unit's multi-channel template; the files hold one single-channel mean waveform per unit and no labels of tracked units.

Findings about the data

What attempts found about the dataset itself, for its owner and the archive.

  • Description missingThe behavioural files give no position of the stimulation contacts in the volumetric imaging volume (the electrode table gives positions along the probe only) and no activation calls, and they hold per-ROI dF/F, not the voxel-level responses the activation rule uses. The paper's counts, densities and radii of activated neurons (Fig. 2B to 2G, fig. S2) cannot be formed.
  • Identifier missingPlanar ROIs are segmented separately in each session and carry no identity across sessions, and no mean or reference images are stored for alignment, so the neurons tracked across weeks in Fig. 3 and fig. S6 cannot be found.
  • Data missingEach unit has one mean waveform of 90 samples on one channel. Waveforms of spikes in and out of stimulation (Fig. 4D) and the multi-channel templates UnitMatch needs (fig. S8) are not in the files, nor are labels of the tracked units.
  • Paper inconsistentThe Methods say the control animals had similar stimulation parameters to the behavioural group, but the control files describe and hold 3 bursts of 10 biphasic pulses at 50 Hz per trial (200 ms bursts starting 0.5 s apart), where the behavioural trains are 70 pulses at 100 Hz over 700 ms.
  • Description missingThe control survey imaging gives no voxel size or plane spacing, and 2 to 9 of its 32 stimulation contact positions sit at the origin with no stated mapping from Ripple channel number to contact index, so distances of ROIs from the stimulation site cannot be formed.
  • OtherThe files hold 795 unit-sessions in the 46 sessions of weeks 0 to 4 (999 over all 55 behavioural sessions), where the paper reports 973 units; 24 control imaging sessions where the paper reports 18; and units in 14 sessions of 4 control mice where fig. S7 reports 4 animals and 15 sessions.
  • Description missingThe trials table gives no response time for miss trials except 98, so the movement-onset threshold of fig. S5 (25th percentile over hit and miss trials) cannot be formed as stated; and response_time is measured from trial start, which precedes the train onset by 0 to 85 ms depending on the session (56 to 85 ms in ICMS101 2023-10-27).
  • Data missingMouse 6 (ICMS83) has no catch trials in its files (currents 2 to 10 uA only), though the Methods give each block 10 catch trials; its thresholds cannot use the false-alarm point.
  • Metadata errorThe electrical_stimulation tables describe 700 ms, 70-pulse trains, but in sessions with planar imaging they also hold 37 to 90 trains of up to 242 pulses lasting seconds, after the end of the electrophysiology recording.
  • Metadata errorCell types are spelled 'Pyramidal Cell', 'Narrow Interneuron' and 'unknown' in ICMS83 and 'pyramidal' and 'interneuron' elsewhere; and the stored type disagrees with the 0.45 ms trough-to-peak rule applied to the stored mean waveform for 72 units (29 interneurons at 0.45 ms or more, 43 pyramidal below).
  • Methods not availableQuestion for the authors (fig. 6D). What bin width and smoothing were used for the hit and miss PSTHs, and was the divergence onset the first significant cluster or the last? With 10 or 5 ms bins and no smoothing, PL responses show significant clusters in both phases and the early NPL onset (first cluster) is 75 ms with 10 ms bins and 87.5 ms with 5 ms bins.
  • Methods not availableQuestion for the authors (fig. S5, E to H). Were the modulated set and the PL labels recomputed in the 120 ms window or kept from the 700 ms analysis? Kept, the NPL modulation does not change; recomputed, it rises at all three currents.
  • Metadata errorstim_channel and the electrode table's channel_name are both described as Ripple channel numbers, but they are not the same numbering. The paper's code places stimulation channel c at (32 - c) x 60 um on the probe; with that the contacts lie at 0 to 520 um depth and, pooled over mice, the fraction of modulated units falls with distance from them (not in Mice 5 and 6, nor in the nearest 60 um), while taking the electrode table's rel_y for channel_name = c puts them at -156 to 1559 um, some outside the electrode table, and shows no fall-off pooled. The mapping from stim_channel to a contact position is not stated in the files.
  • Description missingNo per-pulse stimulation times are stored; the trains have onsets and offsets only (every behavioural train lasts 700.00 ms), so pulse-locked analyses must place the pulses at their nominal 10 ms (20 ms in the controls) spacing.
  • Methods not availableQuestion for the authors (fig. S7, fig. S9C). The control code reads control_timing_params.json and condensed_trials.csv, which are not in the repository. Were the pulse window 20 ms and the blanking 1.5 and 0.5 ms, and was time to maximum taken over the burst, and was the baseline the 700 ms before each burst, which holds the previous burst? With a baseline before each trial's first burst, control NPL modulation rises (p = 0.0059). Is a row of condensed_trials.csv a trial of 3 bursts or one burst? With trial rows the control PL and NPL coupling do not differ (FDR p = 0.431 and 0.134); with burst rows they differ early (FDR p = 0.00428).
  • Metadata errorIn the control files the trials table's trial_index is described as the trial index within the session, but it restarts in each recording block (rep_num), so the same index names up to several trials of a session, and the electrical_stimulation table's trial_index, which refers to it, cannot tell them apart without the block.

Questions

Where the author or a verifier believed the claims file misread the paper, they asked, citing the paper, and a reader who had not seen the data answered from the paper alone, amending the claims file where it was wrong. The author then ran the claim as answered. Attempts before protocol pilot-11 also asked here for method details the claims file did not give.

  • openasked 2026-11-04about figs3d-modulation-stable-at-threshold

    The criterion judges on reading (a), one value per channel and session, as "the reading whose n matches" the legend's 21 early and 34 late. On the dataset, reading (a) gives 42 early and 87 late channel-sessions with modulated responses at the rounded threshold current, about twice the legend's n, so its n does not match. Is (a) still the reading to judge on, or does the paper's n point to another unit (for example one value per session, or only thresholds that round to 4, 5 or 6 uA, the currents the Methods analyse)?

    Passage. fig. S3D legend, "Modulation at each session's psychometric detection threshold remained stable over training (two-sided Mann-Whitney U, nearly = 21, nlate = 34, p = 0.095, r = 0.21)"; Methods, Analysis of behavioral data, "Only data that included 4, 5, and 6 uA stimulation amplitudes were analyzed since these were the most consistently presented across animals and sessions."

    Why it matters. The claim is one of absence, judged on P >= 0.05. Readings (a) and (b) both give P >= 0.05 here (0.16 and 0.789), but a unit with fewer samples changes the test's power, and the criterion's stated reason for choosing (a) does not hold on these data.

    No reader has answered it yet.

Notebooks

Every registered entry that uses this dataset. An entry can hold several notebooks, and can use more than one dataset.

Reproduction Learning-induced plasticity of ICMS responses in mouse S1 (Kim, Lycke et al. 2026)

Tests the findings of Kim, Lycke, Zolotavin, Montes, Xie and Luan (2026) on dandiset 001868, which holds chronic electrophysiology, wheel behaviour and two-photon imaging of mouse S1 during an intracortical microstimulation detection task. It tests the behavioural thresholds, response times and movement onset, the modulation and pulse-locking of sorted units over training, their hit-miss separation and population coupling. The imaging claims are blocked, as the files lack the stimulation-site position in the imaging volume, the activation calls and the identity of planar ROIs across sessions. All files

Verification

  • Round 1
    2026-11-04
    Agreed with 38 of 43 outcomes. Independent agent run by CatalystNeuro (same harness, separate context, did not write the notebook). Reviewed: Round 1. The executed notebook of the author-answers run with the conclusion cells and the summary removed, the helper module and the claims file, with the paper. The verifier queried the archive's file list and metadata and tested the helper on synthetic inputs. Report in transcripts/001868/verifier-1-report.md.
    • fig4-unit-counts: the verifier’s outcome was “Not reproduced”. Cell 19: 795 units, 584 pyramidal, 204 interneurons, 488 modulated (339 pyramidal, 146 interneurons). Only 2 of 6 counts are within tolerance (modulated, modulated pyramidal); the all-55-sessions variant gets 3 of 6. Cell type comes from the stored cell_type, not from the criterion's trough-to-peak rule (the two disagree for 72 of 795 units).
    • figs3c-latency-all: the verifier’s outcome was “Not reproduced”. Cell 23: the 5 uA test holds (p = 0.0052), but 4 uA is also significant (p = 0.0465). The criterion requires P >= 0.05 there.
    • fig5e-pyramidal-fraction-pl: the verifier’s outcome was “Partially reproduced”. Cell 35, stored labels: PL 0.747 and NPL 0.536 are both within tolerance, Fisher p = 4.9e-5. Under the criterion's own cell-type rule (trough-to-peak, the variant), NPL is 0.582, outside 0.448 to 0.548.
    • fig5h-pl-spike-probability-increase: the verifier’s outcome was “Not reproduced”. Cell 43: 6 uA holds (p = 0.036), but 5 uA does not (p = 0.86).
    • figs9-coupling-window-and-control: the verifier’s outcome was “Partially reproduced”. Cell 55: in the 120 ms window NPL > PL in both phases (FDR p = 2e-7 and 1e-12). The control half is blocked (methods_underspecified): the control ephys exists but the trains differ; modulation and time to maximum over the 200 ms bursts could have been tested under a stated assumption.
  • Round 2
    2026-11-04
    Agreed with 43 of 43 outcomes. Independent agent run by CatalystNeuro (same harness, separate context, did not write the notebook). Reviewed: Round 2. The full executed notebook of the author-revise-1 run, with its conclusions and summary, the helper module, the claims file and this record, with the paper. Report in transcripts/001868/verifier-2-report.md.

Method differences

How the tests differed from the paper’s method or data, and the claims each applies to.

  • Pulse-locking follows the paper's code (processing/batch_process/postprocessing/responses_v2/pulse_locked_response_metrics.py, called from responses_v3/stim_condition_response.py, XieLuanLab/icms-activation-plasticity at d1922d1): 500 draws of 20% of the pulses without replacement, two points inserted at 1.5 ms, and a null of 100 uniform placements of the spikes of one 20% draw divided by the pulses drawn. The claims file's common method says only 'the same spike count'; round 1 used the spikes of all pulses, which narrowed the null and called 80% of responses PL. Pulses are taken 10 ms apart from the train onset, as the files store train onsets only (every 70-pulse train lasts 700.00 ms).fig5d-pulse-locking-stable-across-currents fig5e-pyramidal-fraction-pl figs4b-pl-latency-distance fig5fg-pl-modulation-increase figs4de-npl-no-change fig5h-pl-spike-probability-increase figs5-restricted-window-consistent fig6c-kl-divergence fig6d-npl-hit-miss-divergence fig6f-population-coupling figs9a-coupling-700ms figs9-coupling-window-and-control
  • Control units follow the paper's control code (processing/control/postprocessing/create_session_responses.py and process_control_responses.py, python/fig_s7/generate_figure.py, XieLuanLab/icms-activation-plasticity at d1922d1): each 200 ms burst is a train, the baseline is the 700 ms before it, stimulation spikes are the unblanked ones in 20 ms pulse windows, pulse-locking uses 20 ms windows, filters P < 0.05, z > 0, baseline at least 0.5 Hz, more than 5 trains, more than 15 pulse-window spikes, weeks below 5, 5 uA; weeks from each animal's first ephys session, the first dates the code lists (python/utils/config.py). The code's timing file (control_timing_params.json) is not in the repository: the 20 ms window comes from the code's docstring, the 1.5 and 0.5 ms blanking is the behavioural one, and time to maximum is taken over the burst. These are assumptions.figs9-coupling-window-and-control figs7-control-no-change
  • Thresholds are refitted with psignifit 4.3 (Python), sigmoid norm, yes/no, at psignifit's default grid. The threshold is the current at which the fitted (scaled) function reaches 0.5, as the criterion says and as the paper's code takes it (processing/dataloader.py, get_block_detection_threshold, ps.getThreshold(result, 0.5), XieLuanLab/icms-activation-plasticity at d1922d1). The paper's code calls an older psignifit. The dataset stores no thresholds.fig2a-threshold-decrease
  • Blocks are the 100-trial groups of the volumetric trials; the planar fifth block is left out as the Methods count four blocks (Methods, Analysis of behavioral data). 38 channel-sessions have two blocks and 4 have one or three.fig2a-threshold-decrease
  • Mouse 6 (ICMS83) has no catch trials in its files, so its fits have no 0 uA point.fig2a-threshold-decrease
  • The criterion's reading of Fig. 2A (week medians) is the claims file's assumption.fig2a-steepest-decline-early
  • Thresholds as in fig2a-threshold-decrease (psignifit, default grid, 50% of the fitted function).fig2a-steepest-decline-early
  • The dataset's response_time is from trial start, which precedes the train onset by 5.5 to 37 ms in most sessions, 0 in Mouse 6 and 56 to 85 ms in ICMS101 2023-10-27; the time from train onset is shown as a variant and gives the same result.figs1e-response-times-decrease
  • Currents 4 to 6 uA, as the common method of the claims file fixes.figs1e-response-times-decrease
  • The paper takes the response threshold from hit and miss trials; the dataset's miss trials have no response time except 98, so the threshold is taken from hit trials, with those 98 added as a variant (same thresholds).figs5c-wheel-onset-decrease
  • The stored wheel_position_processed is already low-passed at 20 Hz forward and backward (its description), so no filter is applied again.figs5c-wheel-onset-decrease
  • Onset is measured from the train onset of the electrical_stimulation table.figs5c-wheel-onset-decrease
  • Cell type is the stored cell_type, which the files describe as the putative cell type from t2p classification; Mouse 6 spells it 'Pyramidal Cell' and 'Narrow Interneuron', read as the same labels. By the trough-to-peak of the stored single-channel mean waveform, 72 units change class (shown as a variant).fig4-unit-counts
  • Cell type is the stored cell_type (the files' t2p classification); the rule applied to the stored mean waveform is a variant, as that waveform is not the template the rule is defined on.fig4ij-pyramidal-modulation-increase
  • Cell type is the stored cell_type (the files' t2p classification); the rule applied to the stored mean waveform is a variant.fig5e-pyramidal-fraction-pl
  • Distance follows the paper's code (processing/batch_process/postprocessing/longitudinal_data_utils.py, add_data_to_df and correct_df_unit_location; python/fig_s4/generate_figure.py; XieLuanLab/icms-activation-plasticity at d1922d1): the contact at (32 - stim_channel) x 60 um up the probe, the unit at unit_y_um, both converted to cortical depth with the animal's length of probe outside the brain and cos 30 degrees, responses at or above the surface dropped, Mouse 6 left out (the code has no length for it), PL responses with the z cap of 100. Responses at 120 to 840 um are binned in 120 um bins from 60 um, as the code does.figs4b-pl-latency-distance
  • The paper does not say whether labels are recomputed in the 120 ms window; both readings are run and only one matches.figs5-restricted-window-consistent
  • Trials are pooled over the modulated responses of a week and label; a constant of 1e-10 is added to empty histogram bins, a standard choice.fig6c-kl-divergence
  • The PSTH bin width and smoothing are not in the paper or its code (claims); 10 and 5 ms bins without smoothing are run, and both fail on the PL part.fig6d-npl-hit-miss-divergence
  • Labels are permuted by swapping a response's hit and miss rows, a standard choice for paired rows; the code's scheme is not in the claims file.fig6d-npl-hit-miss-divergence
  • Only the zero-lag value of the spike-triggered population rate is computed, which is the coupling the Methods define; the +-100 ms window does not enter it.fig6f-population-coupling
  • Control coupling follows processing/pop_coupling/pop_coupling_control.py: per channel and current, segments from the first to the last pulse of each row of the code's trial table concatenated, population of every unit with a response at that condition, 50-spike minimum. The trial table is not public; its rows are taken as trials (3 bursts), an assumption, and bursts are shown as the other reading. Trials are numbered from burst_index_in_trial in time order, as the stored trial_index restarts in each recording block.figs9-coupling-window-and-control
  • The controls were stimulated at 2, 5, 7 and 10 uA, so reading (b) pools all four currents.figs7-control-no-change
  • The 700 ms baseline before each burst holds the previous burst for the second and third burst of a trial (5863 of 8795 bursts). A baseline from the 700 ms before the trial's first burst is run as a variant; whether the code's missing timing file sets another baseline is asked of the authors (findings).figs7-control-no-change

Provenance

Attempt record
001868-kim-2026-01
Paper
10.1126/sciadv.aef0343
Date
2026-11-03
Made by
An agent, run by CatalystNeuro
Dataset
DANDI:001868
Version
0.260715.2016 (doi:10.48324/dandi.001868/0.260715.2016)
Run on
GitHub Actions
Harness
Claude Code
Model
claude-opus-5-5
Protocol
pilot-13

Transcripts of the agent runs

Each run’s prompt, messages and tool calls, with long output cut and the text of the paper left out, and its final report.