name	iblatlas-connectivity-mesoscale
description	Load the Allen/Knox regularized-regression mesoscale mouse connectivity matrix and the Harris et al. cortico-thalamic hierarchy scores using iblatlas. Use when the user asks to load mesoscale connectivity, the mouse connectome, connection strength/density, or cortical/thalamic hierarchy scores.
allowed-tools	["Read"]

name: ea-load-mesoscale-connectivity

Load mesoscale mouse connectivity + cortico-thalamic hierarchy

from iblatlas.connectivity import mesoscale
df = mesoscale.load()                   # regionalized connectivity matrix
hierarchy = mesoscale.load_hierarchy()  # Harris et al. hierarchy scores

Outputs

Variable	Description
`df`	Tidy long dataframe (671_628, 9): one row per (source structure, hemisphere, target structure, metric)
`hierarchy`	Dataframe (122, 9): one row per (area, correction scheme), 61 cortical/thalamic areas

`df` columns

source_structure_id / source_acronym, target_structure_id / target_acronym: Allen CCF structure (291 "summary structures")
hemisphere: 'ipsi' or 'contra' (relative to the source)
metric: one of 'connection_strength', 'connection_density', 'normalized_connection_strength', 'normalized_connection_density'
value: the metric value for this (source, hemisphere, target) triple
source_volume_mm3 / target_volume_mm3: single-hemisphere structure volume (mm³), including descendants (e.g. layers)

`hierarchy` columns

correction: 'cre_conf' (Cre-line confidence weighted) or 'no_conf' (unweighted)
area: acronym — matches source_acronym / target_acronym in df directly, same Allen CCF acronyms
region_type: 'C' cortex or 'T' thalamus
cc_before / cc_iter, cctc_before / cctc_iter, cctcct_before / cctcct_iter: hierarchy score at increasing model stages (cortico-cortical only → + thalamocortical → + corticothalamic), before/after iterative refinement. Lower = more feedforward/earlier (e.g. VISp ≈ −0.33 to −0.42, bottom of the visual hierarchy); higher = more association-like. Use cctcct_iter with correction == 'cre_conf' for the paper's main/full result.

Key points

Only connection_strength is additive across a re-parcellation (it's proportional to integrated axon volume). To reaggregate onto a coarser parcellation (e.g. Cosmos), map source_structure_id / target_structure_id via iblatlas.regions.BrainRegions().id2id(ids, mapping='Cosmos'), then groupby([...])['value'].sum(). This is only exact because a proper Allen ontology grouping (Beryl, Cosmos) partitions the 291 summary structures with no overlap or gaps — verify that if using a custom grouping.
connection_density and the two normalized metrics are ratios — never sum or average them across a regrouping. Re-derive them from the aggregated connection_strength and source_volume_mm3 / target_volume_mm3 at the new grouping instead.
This is anterograde structural connectivity (fit via regularized regression across injection experiments to correct for injection-site overlap contamination) — it carries no timing/conduction-delay information, and doesn't fully correct for fibers of passage.
Empirically, mean outgoing connection_strength per area does not correlate significantly with the Harris hierarchy score (Spearman ρ≈0.14, p≈0.29, n=57 areas) — raw connectivity strength alone is a weak hierarchy proxy once injection-overlap/normalization artifacts are corrected for.
If relating connectivity to response latency, prefer the Harris hierarchy score (layer-resolved feedforward/feedback classification) over raw connection strength as the proxy — Siegle et al. (2021, Nature, "Survey of spiking in the mouse visual system reveals functional hierarchy") correlated this same hierarchy score against measured visual response latency, a good template for this kind of analysis.
Colour scatter/heatmap points or tick labels by Allen region: iblatlas.regions.BrainRegions().get(br.acronym2id(acronyms)).rgb gives per-region RGB (uint8, one row per acronym).
See examples/atlas_connectivity_mesoscale.ipynb in this repo for a worked example (hierarchy-vs-strength scatter + Cosmos-level reaggregated heatmap, both colour-coded by Allen region).

References

Oh SW et al. (2014) A mesoscale connectome of the mouse brain. Nature 508, 207–214. doi: 10.1038/nature13186
Knox JE et al. (2018) High-resolution data-driven model of the mouse connectome. Network Neuroscience 3, 217–236. doi: 10.1162/netn_a_00066
Harris JA et al. (2019) Hierarchical organization of cortical and thalamic connectivity. Nature 575, 195–202. doi: 10.1038/s41586-019-1716-z

Load mesoscale mouse connectivity + cortico-thalamic hierarchy

from iblatlas.connectivity import mesoscale df = mesoscale.load() # regionalized connectivity matrix hierarchy = mesoscale.load_hierarchy() # Harris et al. hierarchy scores

Outputs

Variable

Description

df

Tidy long dataframe (671_628, 9): one row per (source structure, hemisphere, target structure, metric)

hierarchy

Dataframe (122, 9): one row per (area, correction scheme), 61 cortical/thalamic areas

df columns

source_structure_id / source_acronym, target_structure_id / target_acronym: Allen CCF structure (291 "summary structures")

hemisphere: 'ipsi' or 'contra' (relative to the source)

metric: one of 'connection_strength', 'connection_density', 'normalized_connection_strength', 'normalized_connection_density'

value: the metric value for this (source, hemisphere, target) triple

source_volume_mm3 / target_volume_mm3: single-hemisphere structure volume (mm³), including descendants (e.g. layers)

hierarchy columns

correction: 'cre_conf' (Cre-line confidence weighted) or 'no_conf' (unweighted)

area: acronym — matches source_acronym / target_acronym in df directly, same Allen CCF acronyms

region_type: 'C' cortex or 'T' thalamus

cc_before / cc_iter, cctc_before / cctc_iter, cctcct_before / cctcct_iter: hierarchy score at increasing model stages (cortico-cortical only → + thalamocortical → + corticothalamic), before/after iterative refinement. Lower = more feedforward/earlier (e.g. VISp ≈ −0.33 to −0.42, bottom of the visual hierarchy); higher = more association-like. Use cctcct_iter with correction == 'cre_conf' for the paper's main/full result.

Key points

Only connection_strength is additive across a re-parcellation (it's proportional to integrated axon volume). To reaggregate onto a coarser parcellation (e.g. Cosmos), map source_structure_id / target_structure_id via iblatlas.regions.BrainRegions().id2id(ids, mapping='Cosmos'), then groupby([...])['value'].sum(). This is only exact because a proper Allen ontology grouping (Beryl, Cosmos) partitions the 291 summary structures with no overlap or gaps — verify that if using a custom grouping.

connection_density and the two normalized metrics are ratios — never sum or average them across a regrouping. Re-derive them from the aggregated connection_strength and source_volume_mm3 / target_volume_mm3 at the new grouping instead.

This is anterograde structural connectivity (fit via regularized regression across injection experiments to correct for injection-site overlap contamination) — it carries no timing/conduction-delay information, and doesn't fully correct for fibers of passage.

Empirically, mean outgoing connection_strength per area does not correlate significantly with the Harris hierarchy score (Spearman ρ≈0.14, p≈0.29, n=57 areas) — raw connectivity strength alone is a weak hierarchy proxy once injection-overlap/normalization artifacts are corrected for.

If relating connectivity to response latency, prefer the Harris hierarchy score (layer-resolved feedforward/feedback classification) over raw connection strength as the proxy — Siegle et al. (2021, Nature, "Survey of spiking in the mouse visual system reveals functional hierarchy") correlated this same hierarchy score against measured visual response latency, a good template for this kind of analysis.

Colour scatter/heatmap points or tick labels by Allen region: iblatlas.regions.BrainRegions().get(br.acronym2id(acronyms)).rgb gives per-region RGB (uint8, one row per acronym).

See examples/atlas_connectivity_mesoscale.ipynb in this repo for a worked example (hierarchy-vs-strength scatter + Cosmos-level reaggregated heatmap, both colour-coded by Allen region).

References

Oh SW et al. (2014) A mesoscale connectome of the mouse brain. Nature 508, 207–214. doi: 10.1038/nature13186

Knox JE et al. (2018) High-resolution data-driven model of the mouse connectome. Network Neuroscience 3, 217–236. doi: 10.1162/netn_a_00066

Harris JA et al. (2019) Hierarchical organization of cortical and thalamic connectivity. Nature 575, 195–202. doi: 10.1038/s41586-019-1716-z

iblatlas-connectivity-mesoscale

Load mesoscale mouse connectivity + cortico-thalamic hierarchy

Outputs

df columns

hierarchy columns

Key points

References

Mais deste repositório

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Load mesoscale mouse connectivity + cortico-thalamic hierarchy

Outputs

df columns

hierarchy columns

Key points

References

`df` columns

`hierarchy` columns

`df` columns

`hierarchy` columns