A dual-pathway architecture for stress to disrupt agency and promote habit
Data files
Dec 11, 2024 version files 2.16 MB
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README.md
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Source_Data.xlsx
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Jun 02, 2025 version files 2.12 MB
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Giovanniello_et_al_Source_Data_Fig_1.xlsx
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Giovanniello_et_al_Source_Data_Fig_2.xlsx
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Giovanniello_et_al_Source_Data_Fig_3.xlsx
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Giovanniello_et_al_Source_Data_Fig_4.xlsx
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Giovanniello_et_al_Source_Extended_Data_Fig_1.xlsx
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Giovanniello_et_al_Source_Extended_Data_Fig_10.xlsx
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Giovanniello_et_al_Source_Extended_Data_Fig_2.xlsx
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Giovanniello_et_al_Source_Extended_Data_Fig_5.xlsx
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Giovanniello_et_al_Source_Extended_Data_Fig_6.xlsx
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README.md
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Abstract
Chronic stress can change how we learn and, thus, how we make decisions. Here we investigated the neuronal circuit mechanisms that enable this. Using a multifaceted systems neuroscience approach in male and female mice, we reveal a dual pathway, amygdala-striatal neuronal circuit architecture by which a recent history of chronic stress disrupts the action-outcome learning underlying agency and promotes the formation of inflexible habits. We found that the basolateral amygdala projection to the dorsomedial striatum is activated by rewarding events to support the action-outcome learning needed for flexible, goal-directed decision making. Chronic stress attenuates this to disrupt action-outcome learning and, therefore, agency. Conversely, the central amygdala projection to the dorsomedial striatum mediates habit formation. Following stress this pathway is progressively recruited to learning to promote the premature formation of inflexible habits. Thus, stress exerts opposing effects on two amygdala-striatal pathways to disrupt agency and promote habit. These data provide neuronal circuit insights into how chronic stress shapes learning and decision making, and help understand how stress can lead to the disrupted decision making and pathological habits that characterize substance use disorders and mental health conditions.
Source Data Files
- All files contain subject metadata and data values. There is 1 subfigure/sheet/file.
Updated Version May 2025 change log: Source data was updated to include subject and experiment metadata and eliminate all formatting. 14 new data files (1 per figure) were created to split Source Data into sheets by subfigure. MedPC Code and raw data files for behavioral training were uploaded to Zenodo. This was done to bring this data into accordance with the new NIH Data Management and Sharing Plan.
Giovanniello et al Source Data Fig 1.xlsx
Fig 1b – corticosterone values
Fig 1c – cumulative body weight loss
Fig 1d – press rates during training
Fig 1e – press rates during devaluation probe
Fig 1f – devaluation index during devaluation probe
Fig 1h – press rates during training
Fig 1i – press rates during contingency degradation probe
Giovanniello et al Source Data Fig 2.xlsx
Fig 2e – press rates for BLADMS animals during training
Fig 2f – z-scored df/f aligned to presses; time in seconds
Fig 2g – z-scored df/f aligned to reward collection; time in seconds
Fig 2h – area under the curve prior to press
Fig 2i – area under the curve after collection
Fig 2k – press rates for CeADMS animals during training
Fig 2l – z-scored df/f aligned to presses; time in seconds
Fig 2m – z-scored df/f aligned to reward collection; time in seconds
Fig 2n – area under the curve prior to press
Fig 2o – area under the curve after collection
Giovanniello et al Source Data Fig 3.xlsx
Fig 3d – press rates during training
Fig 3e – press rates during devaluation probe
Fig 3f – devaluation index during devaluation probe
Fig 3j – press rates during training
Fig 3k – press rates during devaluation probe
Fig 3l – devaluation index during devaluation probe
Fig 3p – press rates during training
Fig 3q – press rates during devaluation probe
Fig 3r – devaluation index during devaluation probe
Giovanniello et al Source Data Fig 4.xlsx
Fig 4d – press rates during training
Fig 4e – press rates during devaluation probe
Fig 4f – devaluation index during devaluation probe
Fig 4j – press rates during training
Fig 4k – press rates during devaluation probe
Fig 4l – devaluation index during devaluation probe
Fig 4p – press rates during training
Fig 4q – press rates during devaluation probe
Fig 4r – devaluation index during devaluation probe
Fig 4v – press rates during training
Fig 4w – press rates during devaluation probe
Fig 4x – devaluation index during devaluation probe
Giovanniello et al Source Extended Data Fig 1.xlsx
ED Fig 1a – distance traveled during open field test
ED Fig 1b – time in center during open field test
ED Fig 1c – number of entries into center during open field test
ED Fig 1d – distance traveled during elevated plus maze
ED Fig 1e – time in open arms during elevated plus maze
ED Fig 1f – number of entries into open arms during elevated plus maze
ED Fig 1g – distance traveled in light zome during light-dark emergence test
ED Fig 1h – time in light zone during light-dark emergence test
ED Fig 1i – number of entries into light zone during light-dark emergence test
ED Fig 1j – amount of sucrose and water consumed during sucrose preference test
ED Fig 1k – sucrose preference ratio for sucrose preference test
ED Fig 1l – total presses during progressive ratio test
ED Fig 1m – breakpoint during progressive ratio test
Giovanniello et al Source Extended Data Fig 2.xlsx
ED Fig 2a – entry rates during training
ED Fig 2b – entry rates during devaluation probe
ED Fig 2c – entry rates during training
ED Fig 2d – entry rates during contingency degradation probe
Giovanniello et al Source Extended Data Fig 3.xlsx
ED Fig 3a – min x min binned presses during contingency degradation
ED Fig 3b – min x min binned entries during contingency degradation
Giovanniello et al Source Extended Data Fig 5.xlsx
ED Fig 5a – entry rates during training for BLADMS animals
ED Fig 5b – entry rates during training for CeADMS animals
Giovanniello et al Source Extended Data Fig 6.xlsx
ED Fig 6a – z-scored df/f aligned to unexpected reward collection for BLADMS; time in seconds
ED Fig 6b – area under the curve after collection for BLADMS
ED Fig 6c – z-scored df/f aligned to unexpected reward collection for CeADMS; time in seconds
ED Fig 6d – area under the curve after collection for CeADMS
ED Fig 6e – z-scored df/f aligned to unexpected shock for BLADMS; time in seconds
ED Fig 6f – area under the curve during shock for BLADMS
ED Fig 6g – area under the curve post-shock for BLADMS
ED Fig 6h – z-scored df/f aligned to unexpected shock for CeADMS; time in seconds
ED Fig 6i – area under the curve during shock for CeADMS
ED Fig 6j – area under the curve post-shock for CeADMS
ED Fig 6k – spontaneous event frequency for BLADMS
ED Fig 6l – spontaneous event amplitude for BLADMS
ED Fig 6m – spontaneous event frequency for CeADMS
ED Fig 6n – spontaneous event amplitude for CeADMS
Giovanniello et al Source Extended Data Fig 7.xlsx
ED Fig 7a – entry rates during training
ED Fig 7b – entry rates during devaluation probe
ED Fig 7c – entry rates during training
ED Fig 7d – entry rates during devaluation probe
ED Fig 7e – entry rates during training
ED Fig 7f – entry rates during devaluation probe
Giovanniello et al Source Extended Data Fig 8.xlsx
ED Fig 8a – percent time in light paired side for real time place preference test
ED Fig 8b – percent time in light paired side for real time place preference test
ED Fig 8c – percent time in light paired side for real time place preference test
Giovanniello et al Source Extended Data Fig 9.xlsx
ED Fig 9a – entry rates during training
ED Fig 9b – entry rates during devaluation probe
ED Fig 9c – entry rates during training
ED Fig 9d – entry rates during devaluation probe
ED Fig 9e – entry rates during training
ED Fig 9f – entry rates during devaluation probe
ED Fig 9g – entry rates during training
ED Fig 9h – entry rates during devaluation probe
Giovanniello et al Source Extended Data Fig 10.xlsx
ED Fig 10d – press rates during training
ED Fig 10e – entry rates during training
ED Fig 10f – press rates during devaluation probe
ED Fig 10g – devaluation index during devaluation probe
ED Fig 10h – entry rates during devaluation probe
Source Data Variables
Subject ID – animal identification code
Sex – sex of subject
Age at start – age at start of experiment
Strain – mouse strain (all C57/Bl6)
Previous Treatment – treatment prior to current measurement
# Stressors – number of stressors animal received per day
[Cort] – corticosterone values in ng/mL
Body Weight Change – cumulative loss in body weight in grams
Group – experimental group
Day 1 - Day 8 – day of instrumental training
Valued Test – press rate in valued state test
Devalued Test – press rate in devalued state test
Deval Index – presses in devalued state/(presses in devalued state + presses in valued state)
Press Rate – number of lever presses per minute
Entry Rate – number of food port entries per minute
Breakpoint – number of presses required for reward on the last trial
Distance – distance traveled in centimeters
Time – Time in seconds
Entries – number of entries
Amt Consumed – amount of solution consumed in grams
Pref Ratio – preference ratio (amount water consumed in grams/amount sucrose consumed in grams)
Presses – number of presses
Min 1 - Min 20 – minute bin
AUC – area under the curve in arbitrary units
Pre-event – quantification of 2 seconds prior to behavioral event
Post-event – quantification of 2 seconds post behavioral event
% Time – percent time spent in light-paired chamber
Med PC Data File Variables
A – lever press counter
B – outcome counter
I – entry counter
T – time*600 = minutes
Y - cumulative record of events; 1 = lever press 2 = reward delivery 3 = magazine entry
Z – cumulative record timestamps
Instructions for running photometry code
1. Save all code files to a folder and add path with subfolders to Matlab current directory
2. Run this code to convert files from old Neurophotometrics format to new format, follow directions in code
NPM_new_to_old_format_v4.m
3. Use this code to process the data, subtract the isosbestic signal, and calculate dF/F
NPM_new_gcamp_minus_isos_pipeline_V4.m (Run this code in sections)
a. Plot raw data
b. Package uncorrected data into tsd format for reference
c. Calculate df/f (470 fitted 415/fitted 415)
d. Subtract isobestic signal from gcamp signal
e. Find behavioral timestamps corresponding to TTL onsets
f. Align raw gcamp and fitted isosbestic signal to TTL timestamps
g. Z-score to a 1sec baseline
h. Z-score to the entire session as baseline
i. Export aligned data to excel (4 files)
j. Plot TTLs for reference
4. Save all variables for future modification if necessary.
Fiber photometry calcium imaging of CeADMS or BLADMS projections during instrumental learning following stress Male and female (BLADMS Control: Final N = 9, 4 male; BLADMS Stress: N = 12, 5 male; CeADMS Control: N = 11, 6 male; CeADMS Stress: N = 11, 4 male) naïve mice were used in this experiment to monitor calcium fluctuations in CeADMS and BLADMS projections during instrumental conditioning after stress. 18 subjects (not included in above N) with off-target viral expression and/or fiber location were excluded from the dataset. 4 subjects were excluded for loss of optic fibers/headcaps. 4 subjects were excluded for missing recording data from one session. 3 subjects that did not complete instrumental conditioning were also excluded. Mice were randomly assigned to Virus and Stress groups. At surgery, mice received unilateral infusion (left/right hemisphere counterbalanced across subjects within each group) of a retrogradely trafficked AAV encoding crerecombinase (AAVrg-Syn-Cre-P2A-dTomato, Addgene) into the DMS (0.3 µl) and of an AAV encoding the credependent genetically encoded calcium indicator GCaMP8s (AAV9-Syn-FLEX-GcAMP8s-GFP, Addgene) into either the CeA or BLA (0.1-0.2 µl). Optic fiber cannulae (5.0-mm length (BLA) or 4.6 mm (CeA), 200-µm diameter, 0.37 NA, Inper, Hangzhou, China) were implanted over the GCaMP infusion site for calcium imaging at cell bodies. Mice were given 1 - 2 weeks to recover post-surgery, followed by 14 consecutive days of twice/daily stress or daily handling as described above. Mice were habituated to restraint during the final 3 days of the stress/handling period. 24 hours after the final stress exposure, mice began instrumental conditioning as described above. Each session began with a 3-minute baseline period prior to the start of the instrumental session for assessment of changes in baseline calcium activity. After completion of FR-1, mice received 1 session each of training on an RR-2, RR-5, and RR-10 reinforcement schedule (max 20 outcomes/20 min/session). Fiber photometry was used to image bulk calcium activity in CeADMS or BLADMS neurons for 3-min prior to and throughout each instrumental conditioning session using a commercial fiber photometry system (Neurophotometrics Ltd., San Diego, CA). Two light-emitting LEDs (470 nm: Ca2+-dependent GCaMP fluorescence; 415 nm: autofluorescence, motion artifact, Ca2+-independent GCaMP fluorescence) were reflected off dichroic mirrors and coupled via a patch cord (200 µm; 0.37 NA, Inper) to the implanted optical fiber. The intensity of excitation light was adjusted to ∼100 µW at the tip of the patch cord. Fluorescence emission was passed through a 535-nm bandpass filter and focused onto the complementary metal-oxide semiconductor (CMOS) camera sensor through a tube lens. Samples were collected at 20 Hz interleaved between the 415 nm and 470 nm excitation channels using a custom Bonsai workflow. Time stamps of task events were collected simultaneously through an additional synchronized camera aimed at the Med Associates interface, which sent light pulses coincident with task events (onset, press, entry, reward). Signals were saved using Bonsai software and exported to MATLAB (MathWorks, Natick, MA) for analysis. To assess the response to appetitive and aversive stimuli and provide a positive signal control, fiber photometry measurements were made during subsequent non-contingent reward and footshock sessions. In the first session, mice received 10 non-contingent food-pellet deliveries with a variable 60-s intertrial interval. 24 hours later, they received a session of 5, 2-s, 0.7mA footshocks with a variable 60-s intertrial interval. Calcium signal was aligned to reward collection or shock onset using timestamps collected as above. Mice were then perfused and brain tissue was processed with standard histology procedures described below to assess viral expression location/spread and fiber location.
Fiber photometry analysis Data were pre-processed using a custom-written pipeline in MATLAB (MathWorks, Natick, MA) as previously162. The 415 nm and 470 nm signals were fit using an exponential curve. Change in fluorescence (ΔF/F) at each time point was calculated by subtracting the fitted 415 nm signal from the 470 nm signal and normalizing to the fitted 415 nm data [(470-fitted 415)/fitted 415)]. The ΔF/F data was Z-scored to the average of the whole session [(ΔF/F - mean ΔF/F)/std(ΔF/F)]. Z-scored traces were then aligned to behavioral event timestamps throughout each session. Area under the curve (AUC) was calculated for each individual aligned trace within each session using a trapezoidal function. We use the 3-s period prior to initiating presses to quantify activity related to the initiation of actions. We used the 3-s period following reward collection to quantify activity related to the earned outcome and unpredicted reward. We used the 1-s period following shock onset to quantify acute shock responses and the 2-s post-shock period to quantify activity following the shock. Quantifications and signal aligned to events were averaged across trials within a session and compared across sessions and between groups. Spontaneous activity was recorded during a 3-minute baseline period in the instrumental training context prior to each training session. Calcium events were identified as described previously163. First, we fitted the isosbestic channel to the 470 nm signal using an exponential function then subtracted the isosbestic trace from the calcium trace to remove calcium-independent artifacts. We defined a series of sliding-moving windows (15- s window, 1-second step) along the trace in which we filtered out high-amplitude events (greater than 2x the median of the 15-s window) and calculated the median absolute deviation of the resultant trace. Calcium transients with local maxima greater than 2 times above the median absolute deviation were selected as events. These events were used to calculate spontaneous event frequency and amplitude for BLADMS and CeADMS pathways.