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
5.47 KB
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Source_Data.xlsx
2.16 MB
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.
README: A dual-pathway architecture for stress to disrupt agency and promote habit
Description of the data and file structure
Fiber photometry data was analyzed using custom-written Matlab codes. Source data for each figure is included in SourceData.xlsx.
Source Data File
All data for main and extended data figures is contained in the Source Data file. Data is organized by figure
Code Files
NPM_new_gcamp_minus_isos_pipeline_V4.m – for processing the data, subtracting the isosbestic channel, and calculating dF/F
NPM_new_to_old_format_v4.m – for converting data files from old Neurophotometrics format (v1) to new Neurophotometrics format (v2)
align_df_F_zBL_medPC.m – calculates dF with a z-scored baseline aligned to TTLs
align_gcamp_medPC.m – calculates dF aligned to TTLs
align_gcampRaw_isosFit_medPC.m – calculates dF with fit isosbestic channel
align_z_df_F_medPC.m – calculates z-scored df/F
loadPhotometry_npm_temp.m – loads photometry files
**NPM_new_gcamp_minus_isos_pipeline_RewardMagSession.m – **processes data for a magazine session only
Photometry_gcamp_minus_isos_pipeline.m – subtracts isosbestic signal from gcamp signal
Photometry_separate_gcamp_isos_pipeline.m – exports separate gcamp and isosbestic signals
ts_tsdNotes.m – packages data into tsd
tsdPETH_amw.m – modified version of tsd packaging
tsdPETH_AS_mice.m – fixes a bug that caused TTL alignments to end too soon
data.m – code necessary to run processing pipeline
keep.m – code necessary to run processing pipeline
restrict.m – code necessary to run processing pipeline
smooth.m – code necessary to run processing pipeline
ts.m – code necessary to run processing pipeline
cat.m – code necessary to run processing pipeline
mask.m – code necessary to run processing pipeline
merge.m – code necessary to run processing pipeline
**removeNaNs.m – **code necessary to run processing pipeline
tsd.m – code necessary to run processing pipeline
dxdt.m – code necessary to run processing pipeline
nanstderr.m – code necessary to run processing pipeline
robustZ.m – code necessary to run processing pipeline
smooth2a.m – code necessary to run processing pipeline
extract_varargin.m – code necessary to run processing pipeline
findAlignment_amw.m – code necessary to run processing pipeline
findFile.m – code necessary to run processing pipeline
fndFiles.m – code necessary to run processing pipeline
Popdir.m – code necessary to run processing pipeline
process_varargin.m – code necessary to run processing pipeline
pushdir.m – code necessary to run processing pipeline
selectalongfirstdimension.m – code necessary to run processing pipeline
streq.m – code necessary to run processing pipeline
ttledges.m – code necessary to run processing pipeline
Variables
Aligned_gcamp_minus_isos – gcamp df/f after isosbestic subtraction for each TTL
Aligned_gcamp_minus_isos_zBL – gcamp df/f after isosbestic subtraction and z-score for each TTL
Aligned_gcamp_raw – raw gcamp signal for each TTL
Aligned_isos_fit – fit isosbestic signal for each TTL
Aligned_whole_session_z_df_F – gcamp df/f after whole session z-score
BehaviorData_raw – TTL pixel intensities from cameras
Df_F – processed gcamp signal
Gamp_minus_isos_df_F – gcamp df/f after isosbestic subtraction
Gcamp_raw – raw gcamp signal
gcampData – starting gcamp signal
input1 – input 1 for aligning gcamp signal with fitted isosbestic signal
input2 – input 2 for aligning gcamp signal with fitted isosbestic signal
input3 – input for aligning baseline z-scored gcamp signal
input4 – input for aligning whole session z-scored gcamp signal
isos_fit – fit isosbestic signal
isos_raw – raw isosbestic signal
isosData – starting isosbestic signal
iV – number of medVars +1
medVars – timestamps of each TTL onset
nVars – number of TTLs
signal – TSD format of signal
temp_fit – temporary variable
tFall – TTL offset
titles – titles of TTLs
tRise – TTL onset
z_df_F – z-scored df/F
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.
Methods
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.