CSDS-CaNet

Université Laval · JDCHS — Ca2+ imaging / CSDS

Network reorganization of single-neuron calcium dynamics during chronic social defeat stress

Single-neuron miniscope calcium imaging of six male mice (3 susceptible, 3 resilient) across chronic social defeat stress (CSDS), aligned with frame-by-frame behavioral annotation. Every result on this site is regenerated directly from the analysis code. Each study below states its question, full methodology, highlighted findings, figures and limitations.

Last updated: 2026-10-13T21:37:29+00:00 · 11 studies

Key findings

Highlights that reached significance (see each study for corrections, effect sizes and caveats).

significant

Session boundaries

65% of boundaries > 90th pct

Population activity changes abruptly every 2,000 frames (binomial vs 10% chance, p < 0.001).

S00 →

significant

Session order validated

pooled p < 0.001

Neural blocks are better explained by their own session's ethogram than by shuffled assignments (mean diagonal z = 0.96, 4 animals).

S00 →

significant

Modules persist across days

ARI = 0.337

Consecutive same-condition partitions agree above label-permutation null (-0.000), p = 0.016.

S05 →

significant

Hub persistence

Jaccard 0.20

vs 0.06 expected for random hub sets, p = 0.016.

S05 →

significant

Drift of defeat networks

-0.0249 per day

Mean per-animal change in FC similarity per day of separation between SD sessions; 6/6 animals negative, exact Wilcoxon p = 0.031, q = 0.047 (FDR over BL, SD, SS).

S06 →

significant

Decoding behavior (BL+SS)

45% (chance 27%)

6/6 animals above their own null; group p = 0.016.

S08 →

significant

Decoding behavior (SD)

26% (chance 14%)

4/6 animals above their own null; group p = 0.016.

S08 →

significant

Decoding context (BL/SD/SS, leave-one-day-out)

47% (chance 33%)

4/6 animals above their own null; group p = 0.031.

S08 →

Studies

Study S00 · 5 figures

Data provenance, quality control and session mapping

Recovering session boundaries from concatenated recordings and validating them against behavior.

Question. How are the calcium recordings organised in time, and can each imaging block be assigned to its behavioral session with evidence rather than assumption?

Cohort: 6 mice · 848 neuronsEpoch length: 2000.24–2000.62 framesSession boundaries: 65% of boundaries > 90th pctSession order validated: pooled p < 0.001Missing epochs: 6597 SS9, 6669 SIT11

Study S01 · 3 figures

Behavioral phenotyping across chronic social defeat

Time budgets, the evolution of defeat responses across days, and a social-interaction phenotype.

Question. How does behavior evolve across repeated defeat, and how does social-test behavior relate to the lab's susceptible/resilient classification?

Attack (SD): +0.97 %/daySubmissive (SD): +2.67 %/dayNon-physical interaction (BL): +2.80 %/dayLab phenotype: 3 susceptible · 3 resilientEthogram SI proxy vs lab phenotype: 4/6 agree

Study S02 · 3 figures

Neural activity across CSDS phases

Inferred firing, active-neuron fraction and behavior-locked population responses.

Question. Does the overall level of neural activity differ between baseline, defeat and post-defeat sensory contact, does it change across defeat days, and which behaviors drive population activity?

Firing during defeat: -0.224 Hz (SD − BL)Population response: Exploring: z = -0.05Population response: Standing: z = +0.03SIT CD-1 vs empty: +0.121 Hz

Study S03 · 3 figures

Behavior-tuned neurons and ensemble reactivation

Which neurons encode defeat behaviors, and are defeat-responsive neurons re-engaged in the SIT?

Question. What fraction of neurons is selectively excited or inhibited during defeat-related behaviors, and do neurons recruited during physical defeat overlap with neurons that respond to the CD-1 in the SIT?

Standing-excited neurons: 6.6%Freezing-excited neurons: 3.2%Defeat → SIT reactivation: ×0.00 vs nullNon-physical → SIT reactivation: ×3.03 vs null

Study S04 · 4 figures

Functional network topology across CSDS phases

Neuron-level co-activity graphs, null-model thresholding and graph-theoretic contrasts.

Question. Does the functional network formed by co-active neurons reorganise during defeat and post-defeat sensory contact relative to baseline, and does topology change as defeats accumulate?

modularity Q (SD vs BL): +0.0225clustering coefficient (SD vs BL): +0.0117Q − null Q (SD vs BL): +0.0352Robust to sensitivity analyses: 0 metric(s)

Study S05 · 4 figures

Communities, hubs and rich-club organisation

Ensemble (module) structure, connector hubs, rich clubs and their stability across days.

Question. Are neurons organised into stable co-active modules and hub neurons, and does defeat change hub roles, rich-club organisation or the day-to-day stability of modules?

Modules persist across days: ARI = 0.337Hub persistence: Jaccard 0.20hub participation P (SD vs BL): -0.0605Rich club: normalised φ = 1.33

Study S06 · 5 figures

Network reorganisation and drift

Similarity of neuron-level FC across all sessions: context-specific configurations vs slow drift.

Question. Does each CSDS context (baseline, defeat, sensory contact, SIT) evoke its own network configuration that recurs across days, and how quickly does the functional network drift over the 11 days?

Context-specific networks: Δsim = +0.005Drift of defeat networks: -0.0249 per daySIT network resembles…: no preference

Study S07 · 3 figures

Behavior-state-dependent networks

Functional networks computed separately within each annotated behavior, frame-count matched.

Question. Within the defeat context, does the network configuration depend on what the animal is doing (physical interaction, submission, freezing, escape vs exploration)?

Submissive: modularity vs exploring: -0.124States reconfigure the network: 0.57 vs 0.57

Study S08 · 2 figures

Population decoding of behavior and context

Cross-validated linear decoders with temporally structured null models.

Question. How much information about ongoing defeat behavior and about the CSDS context (baseline, defeat, sensory contact) is carried by the population activity of the imaged neurons?

Decoding behavior (BL+SS): 45% (chance 27%)Decoding behavior (SD): 26% (chance 14%)Decoding context (BL/SD/SS, leave-one-day-out): 47% (chance 33%)

Study S09 · 4 figures

Population dynamics: dimensionality and hidden states

Participation-ratio dimensionality and hidden Markov model population states across CSDS.

Question. Does defeat compress or expand the dimensionality of population activity, and does the population visit different discrete states in each context, states that track defensive behavior?

Dimensionality during defeat: -0.002 PR/NContext-specific population states: JS 0.532 vs 0.549States track defeat behavior: MI z = 0.9

Study S10 · 3 figures

Susceptible vs resilient: neural correlates of the CSDS phenotype

Which per-animal network, activity and coding measures separate the lab-classified susceptible and resilient mice?

Question. Do susceptible and resilient mice differ in how their neuronal activity, functional networks and population codes respond to defeat, and in how those responses evolve across days?

Phenotype groups: 3 susceptible · 3 resilientFeatures separating the groups: 2/25event rate (Hz) · SS − BL: g = -3.22event rate (Hz) · SD − BL: g = -1.90Power: n = 3 per group