Initial E/I Analysis of V1 Ecephys Data (sub-820459)

[anoushka-ch]

Hi everyone,
Following Dr. Lecoq's suggestion to start with the V1 electrophysiology recordings, here are my initial findings from exploring session sub-820459 (2025-11-12).

Methods: I filtered to 1025 SUA units and classified putative excitatory vs inhibitory neurons using a 0.4ms peak-to-valley waveform width threshold — a standard approach for Neuropixels data.

Results:
719 putative excitatory, 306 putative inhibitory neurons (E/I ratio ~2.4:1)
Inhibitory cells fire ~2x faster on average (6.8 Hz vs 3.2 Hz)
E/I firing rates are strongly correlated over the session (r = 0.918), consistent with inhibitory stabilization of the excitatory population

Currently working on the PSTH for standard vs omission trials. Happy to receive any feedback or pointers on next steps!

[jeromelecoq]

I would consider selecting E and I cells that are likely very close to one another to make sure you are looking at a local network. The probes are very long!

[anoushka-ch]

@maierav Thank you for these references! I think that the monosynaptic pair approach makes a lot of sense as a stronger measure of local E/I interaction. The cross-correlation method for identifying connected pairs with ~8ms transmission delays is very interesting.

I'll be looking into this further. As you said, the sample size limitation is a tradeoff, but I think it would be quite interesting to compute E/I across specific experimental conditions. I'll take a look! Thank you!

[anoushka-ch]

These are the results comparing block onset (0-30s) versus mid-block (60-300s) for two depth zones:

The results are interesting. The low coupling zone (~1000µm) shows r≈0.32 at block onset, dropping sharply to r≈0.03 mid-block. Motor cortex (~2100µm) stays much more stable (r≈0.67 onset, r≈0.60 mid-block).

Combined with the bin size analysis showing that the coupling metric is stable across 10-200ms timescales, I think this could suggest that the population-level metric marks slow contextual dynamics rather than fast local circuit interactions. The monosynaptic cross-correlational approach as was suggested could potentially be used to access faster local circuit interactions

[jeromelecoq]

The change in coupling strength across a session block is definitely interesting. A couple clarification questions :
1/ Assuming the cortical column is about 800um thick, 1000um is the bin just underneath the cortex? Is that right?
2/ Do you see the same change in E/I correlation in the middle of the visual cortex between onset and middle of a presentation block?
3/ I am confused why the plots says Motor Cortex (~2100um) ? We have specific probes in the motor cortex. In principle the cortex is the top of the probe (or the furthest from the tip). If this 2.1mm is deep inside the tissue, it might be below the cortex.

[anoushka-ch]

1. yes, based on your ~800µm cortical thickness estimate, 1000µm would put us at or just below the cortical boundary, this is with my CCF validation showing the hippocampal labels (CA3, DG) at that depth zone.
2. I don't currently have a visual cortex epoch comparison because the CCF labels for sub-820459 showed mostly hippocampal and what I labeled as motor cortex, not really any clear VISp zones with enough neurons. I'll check whether other sessions have clearer VISp coverage and rerun the analysis there.
3. I took the CCF location labels from the electrodes table directly, but if 2100µm is deep in the tissue rather than near the surface, "MOp" there may be a misalignment potentially. I'll check the probe targeting information and take a look at whether the CCF labels are aligning with the probe's insertion point and trajectory.

I'll post an update once I've verified the probe targeting and region labels more carefully.

[Sarruedi]

The tip of the probe is called depth 0 which is a bit unintuitive, so 2100um can actually be motor cortex.

There are some comments here that may be of interest to you: #170 (comment)

[maierav]

The bimodal distribution looks promising!

How about we try digging even deeper with:
https://pmc.ncbi.nlm.nih.gov/articles/PMC10220268/

[anoushka-ch]

Thank you for the suggestion! I'll take a look at WaveMAP. using just the peak-to-valley threshold is definitely a simplification, and I can see how a more nuanced clustering approach would help separate cell types that don't fit cleanly into the binary classification. I'll explore this as a next step.

[Sarruedi]

We actually implemented wavemap at this stage and are in the process of cleaning up the code, so we can share it. I highlighted the current state of this analysis in this week's group meeting, which may be of interest to you.