Claims 39
From the paper 10.1038/s41593-025-02190-z · claims file · checked 2026-10-22: ready
- dataset-ki-counts
Dataset KI holds 24,248 single units from 99 Neuropixels recordings in awake head-fixed mice, 52% of them in the PFC and 48% in other brain regions.
We recorded the firing activity of 24,248 single units from awake, head-fixed mice (dataset KI; Fig. 1a–c ). Overall, 52% of the units originated from the PFC and 48% from other brain regions
Results, Capturing the diversity of spontaneous firing patterns, paragraph 1; Methods, Unit quality controlHow it will be judged
Units counted under both readings of G3. The PFC fraction is the share of units whose location is one of the 11 PFC subregions the Results list. The recording count has no tolerance, as it is a count of files the paper names; a dandiset holding only part of dataset KI is reported as such (the author finds what 001260 holds), and the expected values are then compared for the whole of dataset KI only if 000473 is used as well.
- fig1h-waveform-classes
Peak-to-trough durations are bimodal, splitting units into 19,186 ww units (81.1%) and 4,200 nw units (17.7%), with 862 intermediate units left unclassified.
As expected, spike widths were bimodally distributed, allowing us to distinguish ww ( n = 19,186; 81.1%) and nw ( n = 4,200; 17.7%) units (Fig. 1h )
Figure 1h and legend; Results, Capturing the diversity of spontaneous firing patterns, paragraph 2How it will be judged
Thresholds from the Methods (Waveform classification), < 0.38 ms nw and > 0.43 ms ww, on the peak-to-trough duration of each unit's mean waveform. The percentages do not follow from the counts given (19,186 of 24,248 is 79.1%); they match a total of 23,667 units, the preprint's count. Readings of the denominator are (a) all units, classified and unclassified (the legend's 24,248); (b) ww plus nw units only. The fractions hold if they match under either reading; the conflict between counts and percentages is recorded as a paper inconsistency. Bimodality is shown by the histogram (1-ms bins as in the legend are too coarse for a 0.4 ms valley; a finer bin is a plotting choice).
- fig1i-nw-higher-metrics
Narrow-waveform units have significantly higher firing rates, burstiness and memory than wide-waveform units.
indeed, nw units displayed significantly higher firing rates, along with higher burstiness and memory compared to ww units (Fig. 1i )
Figure 1i; Results, Capturing the diversity of spontaneous firing patterns, paragraph 2How it will be judged
For each metric (log10FR, B, M), a linear mixed-effect model Metric ~ 1 + Unittype + (1|Mouse) (Methods, equation 6), with the unit-type effect tested by a post hoc F test with Kenward-Roger degrees of freedom and Tukey's correction for multiple comparisons (Methods; Fig. 1i legend; code Fig1i__MixModel_Statistics.jl, emmeans pairwise contrast). The VoR text gives no p-value; the figure marks are not in the text. Reproduced when, for all three metrics, the nw estimate is higher than the ww estimate with p < 0.05; partially reproduced for one or two metrics. The code also drops units with log10FR < 0.1 and pools active and other epoch states; shown as a variant.
- extfig1d-sleep-like-differ
Firing metrics in sleep-like epochs differ from those in active spontaneous epochs, which is why sleep-like epochs are excluded.
Epochs contaminated by ‘sleep-like’ periods 27 were excluded due to their fundamentally different, nonstationary firing characteristics
Results, Capturing the diversity of spontaneous firing patterns, paragraph 1; Extended Data Fig. 1d,eHow it will be judged
Sleep-like periods detected as in Methods (Detection of sleep-like periods; mean cortical rate in 10-ms bins, Savitzky-Golay 11 points order 3, troughs below 0.2 of the geometric mean for longer than 5 ms, extended to the geometric mean, merged within 1.5 s, padded by 1 s or 0.3 s). Sleep-like epochs overlap such a period. Per unit, metrics computed over active epochs (all ww units with enough active epochs, n = 19,186) and over sleep-like epochs (ww units with at least 12 sleep-like epochs of at least 6 spikes, n = 11,599), as the Extended Data Fig. 1 legend gives the two samples. A mixed model Metric ~ 1 + EpochType + (1|Mouse) (Methods, equation 7) per metric and unit type, post hoc F test with Kenward-Roger degrees of freedom and Tukey's correction (Methods). The legend's per-metric marks and any direction are in the figure only, so the count of metrics required is this file's rule. Reproduced when the epoch-type effect has p < 0.05 for all three metrics in ww units; partially reproduced for one or two; the direction of each is reported.
- extfig1-epoch-counts
Sleep-like episodes occupy a median 6% (IQR 0 to 18%) of recording time; sessions hold a median 227 (IQR 172 to 284) spontaneous active epochs and 19 (IQR 1 to 84) sleep-like epochs.
Sleep-like episodes occupied 6% (IQR, 0–18%) of the time in recording sessions ... Recording sessions contained 227 (IQR, 172–284) spontaneous active epochs and 19 (IQR, 1–84) sleep-like epochs
Methods, Detection of sleep-like periods and Epoch selection; Extended Data Fig. 1 legend, Data (unit count)How it will be judged
Sleep-like detection and epoch rules as in extfig1d-sleep-like-differ. Whether a "recording session" is one probe recording (99) or one session of up to 3 probes is open; both readings are shown, and the values hold under either.
- fig2a-som-memory-burstiness
On the ww SOM, burstiness and firing rate vary along perpendicular gradients, while high memory occurs only with medium to low burstiness, so high memory and high burstiness are mutually exclusive.
The SOM displayed perpendicular gradients of burstiness and firing rate, showing that these two metrics vary independently of each other. In contrast, high memory patches coincided with medium to low burstiness, indicating that high memory and high burstiness are mutually exclusive
Figure 2a; Results, Brain regions have distinct firing repertoires, paragraph 1How it will be judged
The paper shows this in the component planes and gives no measure. Across the nodes of the ww SOM trained as in the header, using the de-standardized prototype values. Part 1 (perpendicular gradients) is judged by the angle between the directions of steepest increase of B and of log10FR over the node grid, from a linear fit of each prototype value on the node coordinates; reproduced when the angle is between 60 and 120 degrees. Part 2 is judged by the nodes with the highest memory, read as (a) the top 10% of nodes or (b) the top 25%; reproduced when, under both, none of these nodes lies in the top quartile of node burstiness. The angle bounds and the quantiles are this file's, as the paper gives none, and are recorded as a deviation; the claim is reproduced only when both parts hold under both readings, and blocked as methods_underspecified when the readings disagree.
- fig2b-pfc-regular-territory
PFC ww units are widely spread on the SOM, with many matching regular-firing (low burstiness) nodes and few in anti-memory territory, while units from other regions populate different territories.
Overall, PFC units were widely distributed on the SOM, with many units matching best to regular firing (that is, low burstiness) and avoiding anti-memory territories (Fig. 2b ), while units from other brain regions populated different territories
Figure 2b; Extended Data Fig. 2e; Results, Brain regions have distinct firing repertoires, paragraph 2How it will be judged
The paper shows this as SOM hit maps and gives no measure or test, so the comparison below is a reading of this file, not the paper's, and is recorded as a deviation. Compared with all non-PFC ww units, reproduced when PFC ww units have a lower median burstiness and a smaller fraction of units with negative memory (M < 0, "anti-memory" as in Fig. 1e), each by a two-sided Mann-Whitney test (burstiness) or a chi-square test (fraction) at p < 0.05. The 54% is the share of PFC units among the ww units the SOM is trained on (all regions and layers).
- fig2e-category-stability
About 57% of ww units keep their category from one block of about 50 spontaneous epochs to the next, far above the 18% expected by chance from the marginal distributions.
Consistently, around 57% of units remained in the category they had in the previous block, which is considerably above the 18% expected from chance (marginal distributions; Fig. 2e )
Figure 2e; Results, Stable classification of single-unit firing patterns, paragraph 1How it will be judged
Blocks as in Methods (Stability analyses), the recording's own blocks of 50 or 100 epochs (100 split in two) where the setting has them, else blocks of about 50 epochs; blocks with fewer than 12 epochs of at least 6 spikes dropped for that unit (Methods; the code uses at least 3 valid epochs per block and 5 per transition, shown as a variant). Block metrics standardized to the full-data ww SOM and assigned their best matching node's category. Stability per transition t is the trace of the summed transition matrix M_t over its total, and chance is the same for the matrix expected from the marginals of M_t (Methods, Assessment of overall stability per transition; Fig. 2e plots it per transition). The paper does not say how "around 57%" summarizes the transitions; readings (a) the mean over transitions, (b) the pooled matrix summed over transitions. Both are shown with the per-transition values, and the value holds under either. The dataset may not mark the recordings' inherent block structure; if it does not, blocks of about 50 consecutive epochs are used and this is a deviation.
- fig2f-low-rate-less-stable
Block-wise category assignment is less stable for low-rate categories (1 to 3, 7 and 8) than for high-rate categories, and low-rate regular categories 1 to 3 mostly transition among themselves, as do the low-rate bursty categories 7 and 8.
Indeed, the block-wise assignment of categories to units was substantially less stable for low-rate categories (1–3, 7 and 8) compared to high-rate categories (Fig. 2f and Extended Data Fig. 3d ) ... low-rate, regular-firing categories (1–3) mostly transitioned among themselves, as did the low-rate, bursty categories (7 and 8)
Figure 2f; Extended Data Fig. 3d; Results, Stable classification of single-unit firing patterns, paragraph 1How it will be judged
From the summed transition count matrix of fig2e-category-stability, the transition probability matrix P (rows sum to 1, Methods). Part 1, the stability of a category read as its diagonal entry of P; reproduced when the mean diagonal of categories 1 to 3, 7 and 8 is lower than the mean diagonal of 4 to 6, with the diagonal of the coincidence coefficient matrix X (Methods, equation 4) shown as a second reading that must agree. Part 2, for each category in 1 to 3, the summed probability of moving to the other two categories of 1 to 3 exceeds that of moving to any other single category outside the group, and likewise for 7 and 8. Reproduced when both parts hold; partially reproduced when one does.
- extfig3f-layer-enrichment
Superficial cortical layers (L2/3) are enriched in bursty ww units, while deep layers (L5 to 6) are enriched in regular-firing ww units.
E-score analysis of ww unit activity revealed substantial enrichment in bursty units in the superficial cortical layers (L2/3), while deep cortical layers (L5–6) were enriched in regular-firing units
Extended Data Fig. 3f,g; Results, Enrichment of low-rate, regular-firing units in the PFC, paragraph 1How it will be judged
E-scores of cortical layer against ww category, all cortical ww units, with each combination of region and behavioral setting as a group (Methods, Group-bias control), 1,000 shuffles. Supplementary Tables 1 and 2 (the p-values) were not read. Reproduced when L2/3 has a significant positive E-score for at least one of the bursty categories 6 to 8, and L5 and L6 each have a significant positive E-score for at least one of categories 1 to 3. Layers as labeled in the dataset (L6a and L6b counted as L6). Under G1 and G2.
- fig2g-pfc-regular-enrichment
Most PFC subregions (ILA, PL, ACAd, ACAv, ORBm and ORBvl) are enriched in low-rate, regular-firing ww units (categories 1 to 3).
The majority of the cytoarchitectural PFC subregions, that is, ILA, PL, ACAd, ACAv, ORBm and ORBvl, were generally enriched in low-rate, regular-firing units (categories 1–3)
Figure 2g; Results, Enrichment of low-rate, regular-firing units in the PFC, paragraph 2How it will be judged
E-scores of brain (sub)region against ww category, deep-layer units for cortical regions and all units elsewhere, against all such units (the code's refall), grouped by behavioral setting (Methods), 1,000 shuffles, regions with more than 20 units. "Generally enriched" is read as (a) a positive sum of the E-scores of categories 1 to 3, or (b) a significant positive E-score in at least one of categories 1 to 3. Reproduced when all six subregions meet the reading, under both readings; partially reproduced when at least four do under both. Under G1 and G2; readings that disagree block the claim as methods_underspecified.
- fig2g-ila-pl-category1
ILA and PL are both strongly and significantly enriched in the most regular-firing category 1.
Interestingly, ILA and PL were both heavily and significantly enriched in units of the most regular-firing category 1 (Supplementary Table 3 )
Figure 2g; Supplementary Table 3 (not read); Results, Enrichment of low-rate, regular-firing units in the PFC, paragraph 2How it will be judged
The E-scores of fig2g-pfc-regular-enrichment. Reproduced when the category 1 E-score is positive and significant (P < 0.05) for both ILA and PL; partially reproduced for one.
- fig2g-bursty-orbl-mos-aiv
ORBl, MOs and AIv are significantly enriched in bursty category 8 units, setting them apart from the activity-defined prefrontal entity.
The ORBl, MOs and AIv, in contrast, were significantly enriched in bursty category 8 units
Figure 2g,h; Results, Enrichment of low-rate, regular-firing units in the PFC, paragraph 2How it will be judged
The E-scores of fig2g-pfc-regular-enrichment. Reproduced when the category 8 E-score is positive and significant for all three; partially reproduced for one or two.
- fig2g-th-hpf-bursty
Thalamus and hippocampal formation are strongly and significantly enriched in bursty units (categories 6 to 8), unlike the PFC.
Other brain regions like thalamus (TH) and hippocampal formation (HPF) were heavily and significantly enriched in bursty units (categories 6–8)
Figure 2g; Results, Enrichment of low-rate, regular-firing units in the PFC, paragraph 2How it will be judged
The E-scores of fig2g-pfc-regular-enrichment, with TH and HPF as the code's parent groups. Readings (a) a significant positive E-score in each of categories 6, 7 and 8; (b) in at least one of them. Reproduced when both TH and HPF meet the reading under both readings; when they differ, the claim is blocked as methods_underspecified with both shown.
- fig2i-ibl-replication
Regional enrichment profiles of dataset KI are highly similar to those of dataset IBL Passive; for category 1, enrichment across 14 cortical (sub)regions correlates between the two datasets.
Repeating the analyses with spontaneous activity obtained from the International Brain Laboratory (dataset IBL Passive 31 ) revealed a high degree of similarity between the regional enrichment profiles of the two datasets
Figure 2i; Extended Data Fig. 4; Results, Enrichment of low-rate, regular-firing units in the PFC, paragraph 3How it will be judged
IBL Passive units' metrics from the 99 epochs of 3 s of the spontaneous block, standardized to dataset KI's ww features and assigned to dataset KI's ww SOM (Methods, Projection of the dataset IBL Passive). E-scores for the 14 cortical (sub)regions both datasets share (deep layers), as in fig2g-pfc-regular-enrichment (grouping for IBL Passive is open, as it has one setting; no grouping is the reading). Pearson correlation of the category 1 E-scores across the 14, two-sided (Fig. 2i legend). The r and p are in the figure only. Reproduced when r > 0 with p < 0.05 for category 1, and at least half of categories 2 to 8 (Extended Data Fig. 4d to j) have r > 0.
- extfig5g-nw-enrichment
Among nw units, ILA, PL and ACAd/v are significantly enriched in high-rate, regular-firing units with high memory, and ORBl in high-rate, bursty units.
ILA, PL and ACAd/v were significantly enriched in high-rate, regular-firing nw units with high memory (Extended Data Fig. 5g and Supplementary Table 4 ). ORBl was consistently and significantly enriched in high-rate, bursty units
Extended Data Fig. 5g; Supplementary Table 4 (not read); Results, Enrichment of low-rate, regular-firing units in the PFC, paragraph 4How it will be judged
An nw SOM of 220 nodes and 5 categories as in the header, E-scores as in fig2g-pfc-regular-enrichment for nw units. The text does not number the nw categories. The regular high-memory category is read as the nw category with above-median log10FR whose median B is lowest, and the bursty one as the category with above-median log10FR whose median B is highest (by category medians); under the code's labels (G2 a) the category numbers are shown as well. Reproduced when ILA, PL and at least one of ACAd and ACAv have a significant positive E-score for the first, and ORBl for the second; partially reproduced when either part holds.
- fig2j-nw-ww-correspondence
Brain (sub)regions enriched in low-rate, regular-firing ww units are enriched in high-rate, regular-firing nw units, and regions enriched in low-rate bursty ww units are enriched in high-rate bursty nw units.
we observed that brain (sub)regions enriched in low-rate, regular-firing ww units were enriched in high-rate, regular-firing nw units. Similarly, regions enriched in low-rate, bursty ww units were enriched in high-rate, bursty nw units
Figure 2j; Results, Enrichment of low-rate, regular-firing units in the PFC, paragraph 4How it will be judged
Pearson correlation across the n = 9 regions with sufficient nw units (Fig. 2j legend), between ww and nw category E-scores. The text does not define "sufficient"; the code's Nmin_maps (more than 20 nw units) is used and the number of regions it keeps is reported against 9. Reproduced when the mean r between ww categories 1 to 3 and the nw regular high-memory category of extfig5g-nw-enrichment is positive, and the mean r between ww categories 7 and 8 and the nw high-rate bursty category is positive. The full 8 by 5 matrix is shown.
- fig3a-harris-regular-positive
In dataset KI, enrichment of cortical (sub)regions in low-rate, regular-firing units (categories 1 to 3, category 2 shown) correlates positively with the Harris cortical hierarchy score, driven by the split between low-hierarchy sensory regions and high-hierarchy PFC subregions.
We found a positive correlation between cortical hierarchy and enrichment in low-rate, regular-firing units (categories 1–3). This positive correlation was not driven by a continuous relationship but due to a bimodal distribution of hierarchical scores
Figure 3a,c; Extended Data Fig. 6a; Results, Enrichment in units displaying regular spontaneous firing reflects high cortical hierarchy, paragraph 1How it will be judged
Harris et al. 2019 cortical hierarchy scores (the code reads 'CC+TC+CT iterated', cortex rows of hierarchy_summary_CreConf.xlsx, Zenodo 14205018). E-scores of the n = 17 cortical (sub)regions with a score, deep-layer ww units (Fig. 3a legend), grouped by behavioral setting as the Methods give for (sub)regions; the code's Fig. 3 script shuffles without groups, shown as a variant. Pearson correlation, two-sided. The r values are in the figure only. Reproduced when category 2 has r > 0 with p < 0.05 and categories 1 and 3 have r > 0; partially reproduced when category 2 holds alone. Under G1 and G2.
- fig3b-harris-regular-positive-ibl
In dataset IBL Passive, across 32 cortical (sub)regions, enrichment in the PFC's signature categories 1 to 3 correlates positively with cortical hierarchy.
we repeated the analysis with the dataset IBL Passive and found positive correlations between cortical hierarchy and the PFC’s signature categories 1–3
Figure 3b,c; Extended Data Fig. 6b; Results, Enrichment in units displaying regular spontaneous firing reflects high cortical hierarchy, paragraph 1How it will be judged
IBL Passive units projected on dataset KI's ww SOM as in fig2i-ibl-replication; n = 32 cortical (sub)regions, deep layers; Pearson, two-sided. Reproduced when categories 1, 2 and 3 each have r > 0 and at least one has p < 0.05.
- fig3c-harris-bursty-negative
Cortical hierarchy correlates even more strongly, and negatively, with enrichment in bursty, low-memory units (categories 6 to 8), in both datasets.
We identified even stronger, negative correlations between cortical hierarchy and enrichment in bursty, low-memory units (categories 6–8; Fig. 3c and Extended Data Fig. 6a,b )
Figure 3c; Extended Data Fig. 6a,b; Results, Enrichment in units displaying regular spontaneous firing reflects high cortical hierarchy, paragraph 1How it will be judged
As fig3a-harris-regular-positive for dataset KI (and fig3b for IBL Passive, shown as a part counted with that claim's data). Reproduced for dataset KI when categories 6, 7 and 8 each have r < 0, at least one with p < 0.05, and the largest |r| among 6 to 8 exceeds the largest |r| among 1 to 3.
- supptable5-no-hierarchy-within-pfc
Restricted to PFC subregions, enrichment in unit categories shows no relationship to cortical hierarchy.
Importantly, when we limited the correlation analysis to PFC subregions, we found no direct relationship between cortical hierarchy and enrichment/depletion in unit categories (Supplementary Table 5 )
Results, Enrichment in units displaying regular spontaneous firing reflects high cortical hierarchy, paragraph 1; Supplementary Table 5 (not read)How it will be judged
A claim of absence. The test, from the Results and the Fig. 3 methods, is a two-sided Pearson correlation of E-scores with the Harris score across the PFC subregions among the 17 of fig3a-harris-regular-positive, per category. Supplementary Table 5, which gives it, was not read, so whether a multiple-comparison correction was applied is unknown; none is applied. Reproduced when no category has p < 0.05; not reproduced when any does; r per category given.
- fig4b-dataroi-parcellation
The PFC flatmap is parcellated into 42 dataROIs that respect subregion boundaries, each holding about 200 deep-layer ww units.
we parcellated the PFC subregions into smaller ROIs (dataROIs, n = 42), each containing a similar number of deep-layer ww units (~200
Figure 4b; Extended Data Fig. 7a; Methods, Parcellation into ROIs of similar unit countHow it will be judged
Per subregion, n = (unit count)/200 rounded down (code, int()), FRP not split; flatmap (u, v) of deep ww units clustered by constrained k-means. Methods give 160 to 240 units per ROI; the code uses 180 to 220 (marg 0.1 around 200). The Methods' bounds are the main reading and the code's a variant; the count holds if either is within tolerance. Needs each unit's flatmap position, which the paper takes from Gao et al.'s projection code (not public; positions are in the Zenodo 14205018 metrics files); a dataset without flatmap positions blocks this and the dataROI and GaoROI claims as data_missing unless those files are used as an input (then data with_inputs).
- fig4cd-dataroi-enrichment
Low-rate, regular-firing categories 1 and 3 are significantly enriched in dataROIs in ILA and PL, while low-rate, bursty categories 7 and 8 are enriched in dataROIs in MOs and ORBl, the ORBl enrichment pinpointed to its most ventral dataROI.
Low-rate, regular-firing units (categories 1 and 3) were significantly enriched in dataROIs located in ILA and PL, while low-rate, bursty units (categories 7 and 8) were enriched in dataROIs located in the MOs and the ORBl
Figure 4c,d; Extended Data Fig. 7b,c; Supplementary Table 6 (not read); Results, Toward an activity-defined map of the PFC, paragraphs 1 and 2How it will be judged
E-scores of dataROI against ww category, deep-layer PFC ww units, grouped by behavioral setting (Methods), 1,000 shuffles. The reference population is open; readings (a) all PFC deep ww units (the code's refPFC, used for its flatmaps); (b) all units as for regions. Reproduced when, under both readings, at least one dataROI in ILA and one in PL have a significant positive E-score in category 1 or 3, and at least one dataROI in MOs and one in ORBl have a significant positive E-score in category 7 or 8. Disagreeing readings block it. The ORBl part of the statement (most ventral dataROI) is not tested, as the flatmap's dorsoventral axis is not given in the text; the ORBl dataROI with the highest category 7 and 8 E-scores is reported.
- fig4e-activity-modules
Clustering dataROIs by enrichment profile gives activity modules; ILA forms a homogeneous ILA-specific module bordering a similar module in adjacent PL and ACAd, while other modules cross cytoarchitectural boundaries and occur in several places.
This revealed a homogeneous enrichment profile in ILA, creating an ILA-specific activity module that bordered a module with similar firing patterns in the adjacent parts of PL and ACAd (Fig. 4e ). Other activity modules stretched over cytoarchitectural boundaries and occurred in multiple places
Figure 4e; Extended Data Fig. 7c,d; Results, Toward an activity-defined map of the PFC, paragraph 1How it will be judged
dataROI E-score profiles (8 categories) standardized and clustered by Ward linkage into 5 modules (Extended Data Fig. 7d; Methods, Hierarchical clustering analyses). Reproduced when all ILA dataROIs fall in one module, and at least two other modules each hold dataROIs of more than one subregion. "ILA-specific" is read as (a) the module holds only ILA dataROIs, or (b) it holds all ILA dataROIs and most of its dataROIs are in ILA; the first part must hold under both, else the claim is blocked as methods_underspecified.
- fig4g-gao-category1
Across GaoROIs, enrichment in low-rate, regular-firing category 1 units correlates positively with the intra-PFC hierarchy score of Gao et al.
Enrichment in low-rate, regular-firing units (category 1) was positively correlated with intra-PFC hierarchy (Fig. 4g )
Figure 4g,h; Results, Toward an activity-defined map of the PFC, paragraph 3How it will be judged
Units assigned to the 60 GaoROIs (polygons from Gao et al.; Zenodo 14205018 flatmap_PFCrois.h5); E-scores per GaoROI as for dataROIs; GaoROIs with at least 20 deep ww units (Extended Data Fig. 7i; > 20 as in the code is a variant) and a hierarchy score (ROI_Gao_hierarchy_index.csv), n = 30 (Fig. 4g legend). Pearson, two-sided. The r and p are in the figure only. Reproduced when r > 0 with p < 0.05.
- extfig7n-gao-category6
Across GaoROIs, enrichment in high-rate, bursty category 6 units correlates negatively with intra-PFC hierarchy.
while enrichment in high-rate, bursty units (category 6) was negatively correlated to intra-PFC hierarchy (Extended Data Fig. 7n )
Extended Data Fig. 7n; Results, Toward an activity-defined map of the PFC, paragraph 3How it will be judged
As fig4g-gao-category1 for category 6. Reproduced when r < 0 with p < 0.05.
- fig4g-pl-orbl-extremes
GaoROIs 19, 20 and 41 in PL have exceptionally high hierarchy scores and the strongest category 1 enrichment, while GaoROI 23 in ventral ORBl has the lowest hierarchy score and the strongest category 1 depletion.
Specifically, GaoROIs (19, 20, 41) located in PL displayed exceptionally high hierarchy scores and the strongest enrichment in unit category 1, while the GaoROI (23) located in the ventral part of ORBl featured the lowest hierarchy score and most prominent depletion in unit category 1
Figure 4g; Results, Toward an activity-defined map of the PFC, paragraph 3How it will be judged
Among the GaoROIs of fig4g-gao-category1, GaoROI IDs as in Gao's table. Reproduced when GaoROIs 19, 20 and 41 are the three highest category 1 E-scores and GaoROI 23 the lowest; partially reproduced when at least two of 19, 20, 41 are in the top three or 23 is lowest. The hierarchy-score ranks are a property of the outside table, checked as a link.
- fig5b-tone-categories
Deep-layer PFC ww tone responses cluster into eight categories ordered by peak delay; most units have flat or negative responses (category 8), categories 1 to 4 have sharp peaks with consistent onsets, and categories 5 to 7 have variable onsets and persistent responses.
Most units displayed flat or negative response traces (category 8). Units belonging to tone response categories 1–4 exhibited well-defined peaks and consistent response peak onsets. In contrast, units belonging to tone response categories 5–7 had variable peak onset latencies and persistent responses
Figure 5b; Extended Data Fig. 8a to c; Results, Tone response maps do not align with cytoarchitecture, paragraph 1How it will be judged
PSTHs from tone response epochs (−2 to 0.7 s, dropping licking, sleep-like and saturation epochs), 0.5-ms bins, Gaussian kernel sd 10 ms, z-scored to the prestimulus window, units below 0.1 Hz overall or prestimulus dropped, 0 to 0.65 s trace; PCA over nw and ww units pooled, 8 components; SOM of 400 nodes on ww units (code somsettings_carlenResp.yml), Ward into 8 categories numbered by mean peak delay, the flat or negative category last (Methods). The units the SOM is trained on are open; readings (a) deep-layer ww units of all regions, n = 15,352 (Results, Tone response maps, paragraph 1); (b) all ww units of all regions and layers, as the code's runCresp*_brain settings (layerlist all). The claim is judged on the PFC deep-layer ww units with tone responses, n = 7,184 (Fig. 5 legend), under both readings. The code drops units beyond 5 sd on any component; shown as a variant. Reproduced when the category whose mean trace has no positive peak is the largest, and the across-unit spread (IQR) of ZETA onset times of significant units is smaller in each of categories 1 to 4 than in each of 5 to 7; partially reproduced when only the first holds. ZETA as in fig5e.
- fig5cd-tone-maps-cross-subregions
Tone-response enrichment maps of the PFC do not follow cytoarchitectural boundaries; the earliest-responding units (category 1) are enriched in anterior ACAd and ORBvl and parts of PL, and tone response modules reveal heterogeneity within subregions.
Units displaying the earliest responses (category 1) were enriched in anterior parts of ACAd and ORBvl and in parts of PL (Fig. 5c ). Neither this response map nor the maps of the other response types (categories 2–8) obeyed cytoarchitectural boundaries
Figure 5c,d; Extended Data Fig. 8d to f; Results, Tone response maps do not align with cytoarchitecture, paragraph 2How it will be judged
dataROI E-scores for tone categories, units as in fig5b, grouped by behavioral setting, reference population read both ways as in fig4cd-dataroi-enrichment. Ward into 4 tone modules (Extended Data Fig. 8e). Reproduced when at least one dataROI in each of ACAd, ORBvl and PL has a significant positive category 1 E-score, and the dataROIs of at least two subregions fall in more than one module. "Anterior" is not tested, as the flatmap's anterior axis is not given in the text.
- fig5e-tone-responsive-patches
Tone-responsive ww units (ZETA test) are enriched in spatial patches distributed across the PFC.
classifying units as either tone-responsive or nonresponsive (Zenith of event-based time-locked anomalies (ZETA) test 32 ; Methods ) confirmed that tone-responsive units were enriched in spatial patches distributed across the PFC
Figure 5e; Results, Tone response maps do not align with cytoarchitecture, paragraph 2How it will be judged
ZETA test on the 700 ms after tone onset, two-sided, responsive at P < 0.01 (Methods, ZETA test). The number of resamplings and the zetapy version are not given in the paper or the code (results are loaded from Zenodo); the zetapy default is a standard choice and the seed is fixed. dataROI E-scores for responsive against nonresponsive. Reproduced when significantly enriched dataROIs occur in at least three different subregions and at least one subregion holds both a significantly enriched and a significantly depleted dataROI.
- fig5g-tuned-unit-counts
In dataset IBL, of 16,148 PFC ww units, only 105 are tuned to visual stimulus side, 1,109 to choice and 2,868 to feedback.
Only 105 of 16,148 PFC units were tuned to the position of the visual stimulus ... Choice-tuned units ( n = 1,109 units) ... Feedback-tuned units ( n = 2,868 units)
Results, Mapping task variables during goal-directed behavior, paragraphs 2 and 3; Fig. 5gHow it will be judged
Windows 0 to 0.1 s after stimulus, 0.1 s before to first wheel movement, 0 to 0.2 s after feedback; trials with first movement 0.08 to 2 s after stimulus (Methods). Combined-condition Mann-Whitney U within conditions of block and stimulus side (choice) or block and choice (stimulus, feedback), 2,000 within-condition shuffles; tuned when P < 0.05 and the unconditioned Mann-Whitney P < 0.001 (Methods). The code also requires the same preferred side in both tests and a combined statistic of at least 0.52; shown as a variant.
- fig5g-tuning-maps
Across 73 IBL ROIs, choice-tuned units are enriched in a territory spanning central MOs, FRP and anterior ACAd, ORBvl and PL, and consistently depleted in ILA, ORBm, ORBl and ACAv; feedback-tuned units are enriched in AId, FRP and anterior MOs, ORBvl and PL, and depleted in ILA, ORBm and ACAd/ACAv; visual stimulus tuning is strongly enriched in a single IBL ROI in MOs.
with a single IBL ROI in the MOs showing strong enrichment (Fig. 5g ). Choice-tuned units ( n = 1,109 units) were enriched in a spatially cohesive territory spanning central MOs, the FRP and the anterior parts of ACAd, ORBvl and PL; ILA, ORBm, ORBl and ACAv were consistently depleted in choice-tuned units (Fig. 5g ). Feedback-tuned units ( n = 2,868 units) were enriched in AId, FRP and anterior parts of MOs, ORBvl and PL, while they were depleted in ILA, ORBm and ACAd/ACAv
Figure 5g; Extended Data Fig. 9c; Results, Mapping task variables during goal-directed behavior, paragraphs 2 and 3How it will be judged
IBL ROIs from the dataset IBL deep-layer PFC ww units, parcellated as in fig4b-dataroi-parcellation (Methods, Parcellation into ROIs of similar unit count). E-scores of tuned against untuned units per IBL ROI with at least 20 units, 1,000 shuffles (Methods); the grouping is not stated (readings as in fig5h-choice-gao-hierarchy; the code's 2,000 shuffles grouped by 'task', calc_statsdict_tuning.py, a variant). The text gives no rule for "enriched" or "depleted" in a territory; readings (a) a significant E-score of that sign, (b) an E-score of that sign. Enriched is judged per subregion as at least one IBL ROI of the subregion enriched; "consistently depleted" and "depleted" as every IBL ROI of the subregion with a negative E-score (under (a), none significantly positive and at least one significantly negative). Reproduced when every named subregion meets its part for choice and for feedback, and exactly one IBL ROI, in MOs, has a significant positive E-score for visual tuning, under both readings; partially reproduced when choice or feedback holds alone. "Anterior" and "central" are not tested, as the flatmap axes are not given in the text.
- fig5h-choice-gao-hierarchy
Across GaoROIs, enrichment in choice-tuned units correlates with the intra-PFC hierarchy score (R = 0.48, P = 0.002), while correlations are weak for visual tuning and absent for feedback tuning.
Enrichment in choice-tuned units correlated strongly with intra-PFC hierarchy score ( R = 0.48, P = 0.002; Fig. 5h and Extended Data Fig. 9d ), whereas correlations were weak for visual tuning and absent for feedback tuning
Figure 5h; Extended Data Fig. 9d,f,g; Results, Mapping task variables during goal-directed behavior, paragraph 4How it will be judged
Tuned units as in fig5g; E-scores per GaoROI with at least 20 units (Extended Data Fig. 9d) and a hierarchy score (n = 39), 1,000 shuffles as in Methods, Enrichment statistics (the grouping for dataset IBL is not stated; no grouping is one reading and grouping by recording a second; both shown). The code (calc_statsdict_gaoRois_tuning.py) uses 2,000 shuffles grouped by its 'task' label; shown as a variant. The R holds when within tolerance with P < 0.05 under a reading. Feedback "absent" is judged as a claim of absence, P >= 0.05 for the feedback correlation.
- extfig9e-tone-responsive-no-hierarchy
Enrichment in tone-responsive units does not correlate with intra-PFC hierarchy across GaoROIs.
whereas correlations were weak for visual tuning and absent for feedback tuning and tone responsiveness (Extended Data Fig. 9e–g )
Extended Data Fig. 9e; Results, Mapping task variables during goal-directed behavior, paragraph 4How it will be judged
A claim of absence. Pearson, two-sided, n = 29 GaoROIs (legend), E-scores of ZETA-responsive units (fig5e) per GaoROI. Reproduced when p >= 0.05; not reproduced when p < 0.05; r given.
- fig5i-map-similarity
No pair of PFC maps is strongly congruent, but the choice-tuning map is most similar to the spontaneous category 1 map, the feedback-tuning map to the category 6 map, and the tone-responsiveness map to the category 8 map.
the map of choice-tuned units (Fig. 5g ) was most similar to the map of low-rate, regular-firing units (spontaneous category 1; Fig. 4c ) ... The map of feedback-tuned units was most similar to the map of high-rate, bursty units (spontaneous category 6), while the map of overall tone responsiveness (Fig. 5e ) was most similar to the map of low-rate, bursty units (spontaneous category 8; Fig. 4d )
Figure 5i; Results, Relationship between spontaneous and evoked activity patterns at spatial and single-unit level, paragraphs 1 and 2How it will be judged
Each map rasterized on the PFC flatmap (311,563 pixels in the paper), each pixel taking the E-score of its ROI, cosine similarity between vectors (Results, Relationship between spontaneous and evoked activity patterns, paragraph 1). The code offers Gaussian blurring of the rasters; the unblurred map is the paper's text and is used. Pixels outside any analyzed ROI are left out. Reproduced for the dataset part when, among the eight spontaneous category maps, the tone-responsiveness map has its highest cosine with category 8; the choice and feedback parts, which use dataset IBL, are reported with this claim and hold when choice is highest with category 1 and feedback with category 6.
- fig5j-tone-responsive-high-rate
At the single-unit level, tone-responsive units (tone categories 1 to 7) are more often than expected high-rate spontaneous units (categories 4 to 6), and units without clear positive tone responses (category 8) tend to have low spontaneous rates (categories 1 to 3, 7 and 8).
Surprisingly, we found that tone-responsive units (tone response categories 1–7) were more likely than expected to have high spontaneous firing rates (spontaneous categories 4–6; Fig. 5j ). Conversely, units lacking clear positive tone responses (tone response category 8) tended to have low spontaneous firing rates
Figure 5j; Results, Relationship between spontaneous and evoked activity patterns at spatial and single-unit level, paragraph 2How it will be judged
Mutual enrichment with spontaneous category as population and tone category as attribute, 1,000 shuffles grouped by behavioral setting (Methods, Group-bias control); the code groups by region and setting, shown as a variant. Reproduced when the summed E-score of tone categories 1 to 7 is positive in each of spontaneous categories 4, 5 and 6, at least one significantly, and the tone category 8 E-score is positive in at least four of spontaneous categories 1 to 3, 7 and 8.
- fig5k-task-tuning-category4
Units with choice tuning and units with feedback tuning are associated with high-rate, Poissonian spontaneous firing (category 4).
Similarly, analysis of goal-directed task variables revealed that units with choice tuning, as well as units displaying feedback tuning, were associated with high-rate, Poissonian firing (spontaneous category 4; Fig. 5k )
Figure 5k; Results, Relationship between spontaneous and evoked activity patterns at spatial and single-unit level, paragraph 2How it will be judged
Mutual enrichment of spontaneous category (IBL Passive units on dataset KI's ww SOM) as population against tuning as attribute (Methods, Mutual enrichment), 1,000 shuffles (Methods, Enrichment statistics). The Methods name no grouping for Fig. 5k; readings (a) no grouping, (b) the code's grouping by its 'task' label and region (calc_statsdict_tuning.py). The code's 2,000 shuffles are a variant, as the Methods give 1,000. Reproduced when choice-tuned and feedback-tuned units each have a significant positive E-score in spontaneous category 4, under both readings; readings that disagree block the claim as methods_underspecified.
- methods-settings-no-difference
Firing metric distributions do not differ significantly across behavioral settings.
Importantly, although the mice were engaged in various behavioral tasks, firing metric distributions were not significantly different across behavioral settings
Methods, Habituation and behavioral settingsHow it will be judged
A claim of absence, but the paper names no test in the text read; the Supplementary Methods, which may give it, were not read. The claim is blocked as methods_underspecified unless a reader answers with the paper's test. As a variant only, a mixed model Metric ~ 1 + Setting + (1|Mouse) per metric for ww units, with the size of the setting effects.
Pipeline
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- done (1 of 1)
- Claims
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- Claims check
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- Attempt
- todo
- Questions
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