DOI: 10.5061/dryad.2jm63xt00
title: A dual-pathway architecture for stress to disrupt agency and promote habit

description: 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.
description: Fiber photometry calcium imaging of CeADMS or BLADMS
 projections during instrumental learning following stress Male and female
 (BLADMS Control: Final N = 9, 4 male; BLADMS Stress: N = 12, 5 male;
 CeADMS Control: N = 11, 6 male; CeADMS Stress: N = 11, 4 male) naïve
 mice were used in this experiment to monitor calcium fluctuations in
 CeADMS and BLADMS 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 CeADMS or BLADMS 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 BLADMS and
 CeADMS pathways.
description: # A dual-pathway architecture for stress to disrupt agency and promote
 habit   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 BLADMS 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
 CeADMS 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 BLADMS animals             ED Fig 5b –
 entry rates during training for CeADMS animals   Giovanniello et al
 Source Extended Data Fig 6.xlsx             ED Fig 6a – z-scored df/f
 aligned to unexpected reward collection for BLADMS; time in seconds
             ED Fig 6b – area under the curve after collection for BLADMS
             ED Fig 6c – z-scored df/f aligned to unexpected reward
 collection for CeADMS; time in seconds             ED Fig 6d – area under
 the curve after collection for CeADMS             ED Fig 6e – z-scored
 df/f aligned to unexpected shock for BLADMS; time in seconds            
 ED Fig 6f – area under the curve during shock for BLADMS             ED
 Fig 6g – area under the curve post-shock for BLADMS             ED Fig 6h
 – z-scored df/f aligned to unexpected shock for CeADMS; time in seconds
             ED Fig 6i – area under the curve during shock for CeADMS
             ED Fig 6j – area under the curve post-shock for CeADMS
             ED Fig 6k – spontaneous event frequency for BLADMS
             ED Fig 6l – spontaneous event amplitude for BLADMS
             ED Fig 6m – spontaneous event frequency for CeADMS          
   ED Fig 6n – spontaneous event amplitude for CeADMS   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.