iMOSS: an integrated open-source tail suspension test platform for high-resolution immobility scoring and synchronization with neural activity.

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7 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

The 7 matches

[1] § Materials, equipment, and methods › Threshold optimization using machine learning ↔ Data_Analysis/Machine Learning/ZY_ML_V5.py, lines 84 to 118 · score 0.94 · Random Forest regression, RandomizedSearchCV, n_estimators, random_state, machine learning, absolute error · Report a mismatch
[2] § Materials, equipment, and methods › Code accessibility ↔ Data_Analysis/zy_install_libraries_version.py, lines 87 to 123 · score 0.72 · PyExcelerate, installation, OpenCV, Pandas, Pillow, libraries · Report a mismatch
[3] § Results › iMOSS-AS: a sensor-based automated immobility scoring platform ↔ Data_Analysis/iMOSS_AS/Archive_V1/zy_iMOSS_AS_20251119_clean.py, lines 311 to 369 · score 0.71 · band pass, separating pre, post start period, refined start, subtraction, raw · Report a mismatch
[4] § Results › iMOSS-AS: a sensor-based automated immobility scoring platform ↔ Data_Analysis/iMOSS_AS/Archive_V1/zy_iMOSS_AS_20251119_clean.py, lines 259 to 309 · score 0.68 · smallest rolling standard, post start period, deviation, baseline, filtering, window · Report a mismatch
[5] § Results › iMOSS-AS: a sensor-based automated immobility scoring platform ↔ Data_Analysis/iMOSS_AS/Archive_V1/zy_iMOSS_AS_20251119_clean.py, lines 371 to 443 · score 0.67 · smoothed signals, Provisional start, period threshold, post start period, raw, baseline · Report a mismatch
[6] § Results › iMOSS-AS: a sensor-based automated immobility scoring platform ↔ Data_Analysis/Machine Learning/ZY_ML_V5.py, lines 84 to 118 · score 0.62 · Random Forest regressor, absolute error, prediction, MAE, model, scoring · Report a mismatch
[7] § Results › iMOSS-AS: a sensor-based automated immobility scoring platform ↔ Data_Analysis/Machine Learning/ZY_ML_V5.py, lines 171 to 190 · score 0.56 · global best threshold, absolute error, Machine learning, MAE, mouse · Report a mismatch

Paper

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The authors' code

Python · 195 lines · 6.2 KB · MIT · 3 matches

# -*- coding: utf-8 -*-
"""
Spyder Editor
This is a temporary script file.
"""
import os
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split, GroupKFold, RandomizedSearchCV
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_absolute_error
from sklearn.base import clone
# ===============================
# 1. Load data
# ===============================
excel_file = r"C:\Users\yez4\Box\NIDA works\Projects\Manuscript for tail suspension system\202500328\ML_threshold\immobility_threshold_data.xlsx"
sheet_name = "Sheet1"
file_path = os.path.dirname(excel_file)
output_path = os.path.join(file_path,'Output')
df = pd.read_excel(excel_file, sheet_name=sheet_name)
# Features and target
X = df[["Threshold", "Auto_Time"]]
y = df["Manual_Time"]
groups = df["Mouse"]
# ===============================
# 2. Train-test split by mouse
# ===============================
mice = df["Mouse"].unique()
train_mice, test_mice = train_test_split(mice, test_size=0.25, random_state=42)
train_df = df[df["Mouse"].isin(train_mice)].copy()
test_df = df[df["Mouse"].isin(test_mice)].copy()
X_train = train_df[["Threshold", "Auto_Time"]]
y_train = train_df["Manual_Time"]
g_train = train_df["Mouse"]
X_test = test_df[["Threshold", "Auto_Time"]]
y_test = test_df["Manual_Time"]
print(f"Train mice: {len(train_mice)}, Test mice: {len(test_mice)}")
print(f"Train rows: {len(train_df)}, Test rows: {len(test_df)}")
# ===============================
# 3. Baseline model
# ===============================
baseline_model = RandomForestRegressor(
n_estimators=200,
random_state=42
)
baseline_model.fit(X_train, y_train)
baseline_test_pred = baseline_model.predict(X_test)
baseline_test_mae = mean_absolute_error(y_test, baseline_test_pred)
# Grouped CV on training mice only
n_splits = min(5, len(np.unique(g_train)))
if n_splits < 2:
raise ValueError("Not enough training mice for cross-validation.")
gkf = GroupKFold(n_splits=n_splits)
baseline_cv_maes = []
for tr_idx, val_idx in gkf.split(X_train, y_train, groups=g_train):
X_tr, X_val = X_train.iloc[tr_idx], X_train.iloc[val_idx]
y_tr, y_val = y_train.iloc[tr_idx], y_train.iloc[val_idx]
m = clone(baseline_model)
m.fit(X_tr, y_tr)
pred = m.predict(X_val)
baseline_cv_maes.append(mean_absolute_error(y_val, pred))
baseline_cv_mae = float(np.mean(baseline_cv_maes))
print(f"Baseline RF | CV MAE (train only): {baseline_cv_mae:.3f} | Test MAE: {baseline_test_mae:.3f}")
# ===============================
# 4. Tuned model
# Tune ONLY on training mice
# ===============================
param_dist = {
"n_estimators": [100, 150, 200, 250, 300, 400],
"max_depth": [None, 4, 6, 8, 10, 12, 16, 20],
"min_samples_split": [2, 4, 6, 8, 10],
"min_samples_leaf": [1, 2, 3, 4, 5],
"max_features": ["sqrt", "log2", 1.0, 0.7, 0.5],
"bootstrap": [True],
"max_samples": [0.7, 0.8, 0.9, 1.0],
}
search = RandomizedSearchCV(
estimator=RandomForestRegressor(random_state=42),
param_distributions=param_dist,
n_iter=25,
scoring="neg_mean_absolute_error",
cv=gkf,
random_state=42,
n_jobs=-1,
refit=True
)
search.fit(X_train, y_train, groups=g_train)
tuned_model = search.best_estimator_
tuned_cv_mae = -search.best_score_
tuned_test_pred = tuned_model.predict(X_test)
tuned_test_mae = mean_absolute_error(y_test, tuned_test_pred)
print(f"Tuned RF | CV MAE (train only): {tuned_cv_mae:.3f} | Test MAE: {tuned_test_mae:.3f}")
print("Best tuned params:", search.best_params_)
# ===============================
# 5. Choose final model using CV only
# Do NOT use test MAE for selection
# ===============================
if tuned_cv_mae < baseline_cv_mae:
best_model = tuned_model
best_name = "Tuned RF"
best_cv_mae = tuned_cv_mae
else:
best_model = baseline_model
best_name = "Baseline RF"
best_cv_mae = baseline_cv_mae
print(f"Selected model by training CV: {best_name} (CV MAE = {best_cv_mae:.3f})")
# ===============================
# 6. Final test evaluation once
# ===============================
final_pred = best_model.predict(X_test)
final_test_mae = mean_absolute_error(y_test, final_pred)
print(f"Final model: {best_name} | Final test MAE: {final_test_mae:.3f}")
# Optional: save chosen model
# import joblib
# joblib.dump(best_model, os.path.join(os.path.dirname(excel_file), "best_model.joblib"))
# ===============================
# 7. Individual best threshold
# ===============================
search_space = np.linspace(df['Threshold'].min(), df['Threshold'].max(), 1000)
results = []
for mouse in df['Mouse'].unique():
sub = df[df['Mouse'] == mouse]
manual = sub['Manual_Time'].iloc[0]
preds = []
for t in search_space:
auto_est = np.interp(t, sub['Threshold'], sub['Auto_Time'])
X_new = pd.DataFrame([[t, auto_est]], columns=['Threshold', 'Auto_Time'])
pred = best_model.predict(X_new)[0]
preds.append(pred)
preds = np.array(preds)
best_idx = np.argmin(np.abs(preds - manual))
best_thresh = search_space[best_idx]
best_pred = preds[best_idx]
results.append([mouse, manual, best_thresh, best_pred])
results_df = pd.DataFrame(results, columns=['Mouse', 'Manual', 'Best_Threshold', 'Predicted_Time'])
print("\nIndividual best thresholds:")
print(results_df)
# ===============================
# 8. Global best threshold
# ===============================
global_errors = []
for t in search_space:
preds, manuals = [], []
for mouse in df['Mouse'].unique():
sub = df[df['Mouse'] == mouse]
manual = sub['Manual_Time'].iloc[0]
auto_est = np.interp(t, sub['Threshold'], sub['Auto_Time'])
X_new = pd.DataFrame([[t, auto_est]], columns=['Threshold', 'Auto_Time'])
pred = best_model.predict(X_new)[0]
preds.append(pred)
manuals.append(manual)
error = mean_absolute_error(manuals, preds)
global_errors.append(error)
best_idx = np.argmin(global_errors)
global_best_thresh = search_space[best_idx]
print(f"\nGlobal best threshold: {global_best_thresh:.3f}, MAE={global_errors[best_idx]:.3f}")
# save results_df as csv
csv_name = 'ML_result.csv'
csv_path = os.path.join(output_path, csv_name)
results_df.to_csv(csv_path, index=False)

ZY_ML_V5.py at commit 317da51, under MIT · at the source

Overview

Authors: Zengyou Ye¹, Xia Min¹, Sarah T. Johnson¹, Xuehong Cao², Satoshi Ikemoto¹

Institutions: National Institutes of Health (United States); National Institute on Drug Abuse (United States); Tufts Medical Center (United States)

Journal: Frontiers in behavioral neuroscience, volume 20, article 1819512

Dates: received 27 February 2026; accepted 20 April 2026; published online 8 May 2026

Type: Methods article · Language: English

License: CC BY

Identifiers: DOI 10.3389/fnbeh.2026.1819512 · PMID 42182827 · PMCID PMC13194518 · OpenAlex W7160546135

Open access: gold, a free copy (OpenAlex)

Status: code verified (how the code is found and verified)

Categories: optical imaging (calcium, voltage, 2-photon) (modality), methods / tools (subfield)

Methods: Connectivity, Statistics, Machine learning, Preprocessing, Single-unit activity, calcium imaging

Keywords: behavioral neuroscience, fiber photometry, immobility scoring, open-source platform, tail suspension test

Topic: Neurotransmitter Receptor Influence on Behavior (Cellular and Molecular Neuroscience, Neuroscience), according to OpenAlex

Citations: not cited yet (Europe PMC); 8 references in the paper

Abstract

The tail suspension test (TST) is widely used to assess stress-coping behavior in rodents, characterized by alternating periods of active (struggling) and passive (immobile) responses. Immobility in the TST is interpreted as behavioral despair and serves as a key measure for screening antidepressant compounds. Traditional manual scoring is labor-intensive and temporally imprecise, while existing automated systems often misclassify behaviors and have not shown the capacity to integrate behavioral data with neural recording methods. Here, we improved upon the traditional TST with our new developed iMOSS (Immobility/Mobility Optimized Scoring System)—two open-source, low-cost, and scalable tools for high-resolution quantification of mobility and immobility: (1) iMOSS-MV, a video-based frame-by-frame manual-scoring software instrument designed to precisely annotate the exact onset frame for each binary event, and (2) iMOSS-AS, a sensor-based automated instrument detecting immobility/mobility bouts from the sensor-signal using a machine-learning optimized detection threshold. The output from iMOSS-AS closely matched that from iMOSS-MV and outperformed other publicly available tools. Moreover, both systems reliably detected changes in mobility and immobility induced by imipramine treatment, demonstrating sensitivity to pharmacological manipulation. Finally, both iMOSS tools readily integrated with neural data, as shown by simultaneous analysis of medial septal glutamatergic calcium activity via fiber photometry. Thus, either iMOSS-MV or iMOSS-AS alone offers an efficient, user-friendly, and bias-minimized platform for high-throughput behavioral analysis, enabling seamless integration of behavioral and neural data in systems neuroscience research.

Reproduced under the paper's license (CC BY), from the paper cited above.

Repository

Its files are read in the Code ↔ Paper reader above, with 7 matches between paragraphs and lines of code.

addy9908/iMOSS

License: MIT

State: the link answers, verified on 28 September 2026

Evidence: files inventoried

Commit: 317da515ee2a08f515d71e55039cec0213468c37, 10 June 2026

Languages: Python (13)

Size: 94 files, 13 scripts

Software Heritage: not archived

Found in: “Code accessibility”

Holds: README, license file

Not found: CITATION.cff, environment file, tests, continuous integration, documentation

Tools: NumPy (7 files), pandas (7 files), Pillow (5 files), Matplotlib (4 files), OpenCV (4 files), SciPy (2 files), scikit-learn (1 file)

Availability: 1 check, the latest on 28 September 2026: the link answers

28 September 2026: the link answers

15 files

Data_Analysis/Machine Learning/ZY_ML_V5.py, Python, 195 lines, 3 matches
Data_Analysis/iMOSS_AS/Archive_V1/zy_iMOSS_AS_20251119_clean.py, Python, 758 lines, 3 matches
Data_Analysis/iMOSS_AS/Archive_V1/zy_importer_lite_V4.py, Python, 248 lines
Data_Analysis/iMOSS_AS/Archive_V1/zy_preset_mpl_v2.py, Python, 25 lines
Data_Analysis/iMOSS_AS/V2/zy_iMOSS_AS_20260501_with_summary.py, Python, 734 lines
Data_Analysis/iMOSS_AS/V2/zy_importer_lite_V5.py, Python, 189 lines
Data_Analysis/iMOSS_AS/V2/zy_preset_mpl_v2.py, Python, 25 lines
Data_Analysis/iMOSS_MV/V1/zy_iMOSS_MV_20251119.py, Python, 1,045 lines
Data_Analysis/iMOSS_MV/V1/zy_iMOSS_MV_20260610_logofree.py, Python, 1,057 lines
Data_Analysis/iMOSS_MV/V2/zy_iMOSS_MV_withDuration.py, Python, 1,065 lines
Data_Analysis/iMOSS_MV/V3/zy_iMOSS_MV_20260501.py, Python, 881 lines
Data_Analysis/logo_to_base64.py, Python, 51 lines
Data_Analysis/zy_install_libraries_version.py, Python, 123 lines, 1 match
LICENSE, License, 21 lines
README.md, Text, 323 lines

At the source: github.com/addy9908/iMOSS

Code accessibility

The iMOSS-MV and iMOSS-AS were developed in Python and leverages widely used libraries including OpenCV, Tkinter, Numpy, Pandas, Pillow, Scipy, and PyExcelerate. All the codes will be freely available online at https://github.com/addy9908/iMOSS under an MIT license, along with user documentation detailing the installation process. Additionally, the full Python source code is provided in the Supplementary material. Future updates to the code will be available through the link provided above.

Reproduced under the paper's license (CC BY), from the paper cited above.

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
13 scripts, each with its path and the digest of its content;
7 matches between paragraphs of the paper and lines of the code (method lexical-v1);
neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

No dataset and no data link were found in the paper.

Data availability statement

The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found at: https://github.com/addy9908/iMOSS.

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

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Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 5 authors, 5 keywords, 8 references.

Cite

This paper

Ye, Z., Min, X., Johnson, S. T., Cao, X., & Ikemoto, S. (2026). iMOSS: an integrated open-source tail suspension test platform for high-resolution immobility scoring and synchronization with neural activity. Frontiers in behavioral neuroscience, 20, 1819512. https://doi.org/10.3389/fnbeh.2026.1819512

BibTeX

@article{ye2026imoss, author = {Ye, Zengyou and Min, Xia and Johnson, Sarah T. and Cao, Xuehong and Ikemoto, Satoshi}, title = {{iMOSS: an integrated open-source tail suspension test platform for high-resolution immobility scoring and synchronization with neural activity}}, journal = {Frontiers in behavioral neuroscience}, year = {2026}, month = may, volume = {20}, pages = {1819512}, publisher = {Frontiers Media SA}, issn = {1662-5153}, doi = {10.3389/fnbeh.2026.1819512}, url = {https://doi.org/10.3389/fnbeh.2026.1819512}, pmid = {42182827}, pmcid = {PMC13194518} }

RIS

TY - JOUR AU - Ye, Zengyou AU - Min, Xia AU - Johnson, Sarah T. AU - Cao, Xuehong AU - Ikemoto, Satoshi TI - iMOSS: an integrated open-source tail suspension test platform for high-resolution immobility scoring and synchronization with neural activity T2 - Frontiers in behavioral neuroscience J2 - Front Behav Neurosci PY - 2026 DA - 2026/05/08 VL - 20 SP - 1819512 SN - 1662-5153 PB - Frontiers Media SA DO - 10.3389/fnbeh.2026.1819512 UR - https://doi.org/10.3389/fnbeh.2026.1819512 LA - en ER -

CSL-JSON

{ "id": "10.3389/fnbeh.2026.1819512", "type": "article-journal", "title": "iMOSS: an integrated open-source tail suspension test platform for high-resolution immobility scoring and synchronization with neural activity", "container-title": "Frontiers in behavioral neuroscience", "author": [ { "family": "Ye", "given": "Zengyou" }, { "family": "Min", "given": "Xia" }, { "family": "Johnson", "given": "Sarah T." }, { "family": "Cao", "given": "Xuehong" }, { "family": "Ikemoto", "given": "Satoshi" } ], "container-title-short": "Front Behav Neurosci", "volume": "20", "page": "1819512", "DOI": "10.3389/fnbeh.2026.1819512", "PMID": "42182827", "PMCID": "PMC13194518", "ISSN": "1662-5153", "publisher": "Frontiers Media SA", "URL": "https://doi.org/10.3389/fnbeh.2026.1819512", "language": "en", "issued": { "date-parts": [ [ 2026, 5, 8 ] ] } }

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iMOSS: an integrated open-source tail suspension test platform for high-resolution immobility scoring and synchronization with neural activity.

Code ↔ Paper

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