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Intravenous midazolam alters short-interval paired-pulse TMS responses differently in younger and older adults.

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  1. [1] § 2 Methods › 2.7 Statistical comparisons ↔ GABAM_Zenodo_Data_Package/analysis_code.py, lines 1 to 55 · score 0.78 · Shapiro Wilk, Benjamini Hochberg, Single pulse MEP, paired pulse MEP, mixed, ANOVA · Report a mismatch

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The authors' code

Python · 455 lines · 17 KB · CC-BY-4.0 · 1 match

  1. #!/usr/bin/env python3
  2. """
  3. Analysis code for:
  4. Intravenous midazolam alters short-interval paired-pulse TMS responses differently
  5. in younger and older adults
  7. Purpose
  8. -------
  9. This script reproduces the article-style statistical analyses for the de-identified
  10. subject-level TMS dataset. It performs descriptive summaries, Shapiro-Wilk normality
  11. checks, independent-samples t-tests, paired-samples t-tests, Benjamini-Hochberg p-value
  12. adjustment, and ANOVA-style models for single-pulse and paired-pulse MEP outcomes.
  14. Input
  15. -----
  16. subject_level_data.csv
  18. Expected location
  19. -----------------
  20. Place this script in the same folder as subject_level_data.csv, or update DATA_FILE below.
  22. Outputs
  23. -------
  24. The script writes CSV output files to an analysis_outputs/ subfolder:
  25. - descriptive_statistics.csv
  26. - normality_tests.csv
  27. - article_style_ttests.csv
  28. - one_way_anovas.csv
  29. - repeated_measures_anovas_within_age_group.csv
  30. - mixed_effects_wald_tests_mep.csv
  31. - analysis_notes.txt
  33. Notes
  34. -----
  35. The primary manuscript analyses are the t-tests. ANOVA and mixed-model outputs are
  36. included as supplementary reproducibility checks. The default paired-pulse ratio uses
  37. the source ratio variables from the workbook because those are the values preserved
  38. from the manuscript source file. To instead analyze calculated PP/SP ratios, change
  39. RATIO_MODE from "source" to "calculated" below.
  40. """
  42. from pathlib import Path
  43. import warnings
  45. import numpy as np
  46. import pandas as pd
  47. from scipy import stats
  49. try:
  50. import statsmodels.formula.api as smf
  51. from statsmodels.stats.anova import anova_lm, AnovaRM
  52. from statsmodels.stats.multitest import multipletests
  53. STATSMODELS_AVAILABLE = True
  54. except ImportError:
  55. STATSMODELS_AVAILABLE = False
  58. # -----------------------------------------------------------------------------
  59. # User settings
  60. # -----------------------------------------------------------------------------
  61. DATA_FILE = Path("subject_level_data.csv")
  62. OUTPUT_DIR = Path("analysis_outputs")
  63. RATIO_MODE = "source" # options: "source" or "calculated"
  64. EQUAL_VAR_TTESTS = True # True matches conventional Student independent-samples t-tests
  65. ALPHA = 0.05
  68. # -----------------------------------------------------------------------------
  69. # Helper functions
  70. # -----------------------------------------------------------------------------
  71. def as_numeric(series):
  72. return pd.to_numeric(series, errors="coerce")
  75. def fmt_float(x):
  76. if pd.isna(x):
  77. return np.nan
  78. return float(x)
  81. def ensure_output_dir():
  82. OUTPUT_DIR.mkdir(exist_ok=True)
  85. def load_data():
  86. if not DATA_FILE.exists():
  87. raise FileNotFoundError(
  88. f"Could not find {DATA_FILE}. Place analysis_code.py in the same folder as "
  89. "subject_level_data.csv or edit DATA_FILE."
  90. )
  91. df = pd.read_csv(DATA_FILE)
  93. # Coerce expected numeric columns.
  94. numeric_cols = [
  95. "sp_baseline_mep_uv", "sp_midazolam_mep_uv", "sp_washout_mep_uv",
  96. "pp_baseline_mep_uv", "pp_midazolam_mep_uv", "pp_washout_mep_uv",
  97. "baseline_pp_sp_ratio_source", "midazolam_pp_sp_ratio_source",
  98. "baseline_pp_sp_ratio_calculated", "midazolam_pp_sp_ratio_calculated",
  99. ]
  100. for col in numeric_cols:
  101. if col in df.columns:
  102. df[col] = as_numeric(df[col])
  104. if RATIO_MODE == "source":
  105. df["baseline_pp_sp_ratio"] = df["baseline_pp_sp_ratio_source"]
  106. df["midazolam_pp_sp_ratio"] = df["midazolam_pp_sp_ratio_source"]
  107. elif RATIO_MODE == "calculated":
  108. df["baseline_pp_sp_ratio"] = df["baseline_pp_sp_ratio_calculated"]
  109. df["midazolam_pp_sp_ratio"] = df["midazolam_pp_sp_ratio_calculated"]
  110. else:
  111. raise ValueError('RATIO_MODE must be either "source" or "calculated".')
  113. # Categorical ordering for readable output.
  114. df["age_group"] = pd.Categorical(df["age_group"], categories=["younger", "older"], ordered=True)
  115. return df
  118. def descriptive_statistics(df):
  119. variables = [
  120. "sp_baseline_mep_uv", "sp_midazolam_mep_uv",
  121. "pp_baseline_mep_uv", "pp_midazolam_mep_uv",
  122. "baseline_pp_sp_ratio", "midazolam_pp_sp_ratio",
  123. ]
  124. rows = []
  125. for variable in variables:
  126. for age_group, sub in df.groupby("age_group", observed=False):
  127. values = sub[variable].dropna()
  128. rows.append({
  129. "variable": variable,
  130. "age_group": age_group,
  131. "n": int(values.shape[0]),
  132. "mean": fmt_float(values.mean()),
  133. "sd": fmt_float(values.std(ddof=1)),
  134. "median": fmt_float(values.median()),
  135. "min": fmt_float(values.min()),
  136. "max": fmt_float(values.max()),
  137. })
  138. return pd.DataFrame(rows)
  141. def shapiro_normality_tests(df):
  142. variables = [
  143. "sp_baseline_mep_uv", "sp_midazolam_mep_uv",
  144. "pp_baseline_mep_uv", "pp_midazolam_mep_uv",
  145. "baseline_pp_sp_ratio", "midazolam_pp_sp_ratio",
  146. ]
  147. rows = []
  148. for variable in variables:
  149. for age_group, sub in df.groupby("age_group", observed=False):
  150. values = sub[variable].dropna()
  151. if len(values) >= 3:
  152. stat, p_value = stats.shapiro(values)
  153. else:
  154. stat, p_value = np.nan, np.nan
  155. rows.append({
  156. "variable": variable,
  157. "age_group": age_group,
  158. "n": int(len(values)),
  159. "shapiro_w": fmt_float(stat),
  160. "p_value": fmt_float(p_value),
  161. })
  162. return pd.DataFrame(rows)
  165. def independent_ttest(df, label, variable, group_col="age_group", group_a="younger", group_b="older"):
  166. a = df.loc[df[group_col] == group_a, variable].dropna()
  167. b = df.loc[df[group_col] == group_b, variable].dropna()
  168. stat, p_value = stats.ttest_ind(a, b, equal_var=EQUAL_VAR_TTESTS)
  169. return {
  170. "test_family": "independent_samples_ttest",
  171. "comparison": label,
  172. "variable": variable,
  173. "group_a": group_a,
  174. "group_b": group_b,
  175. "n_a": int(len(a)),
  176. "mean_a": fmt_float(a.mean()),
  177. "sd_a": fmt_float(a.std(ddof=1)),
  178. "n_b": int(len(b)),
  179. "mean_b": fmt_float(b.mean()),
  180. "sd_b": fmt_float(b.std(ddof=1)),
  181. "t_statistic": fmt_float(stat),
  182. "df": int(len(a) + len(b) - 2) if EQUAL_VAR_TTESTS else np.nan,
  183. "p_value_raw": fmt_float(p_value),
  184. "equal_variance_assumed": EQUAL_VAR_TTESTS,
  185. }
  188. def paired_ttest(df, label, variable_a, variable_b, age_group=None):
  189. sub = df.copy()
  190. if age_group is not None:
  191. sub = sub.loc[sub["age_group"] == age_group].copy()
  192. paired = sub[["participant_id", "age_group", variable_a, variable_b]].dropna()
  193. a = paired[variable_a]
  194. b = paired[variable_b]
  195. if len(paired) >= 2:
  196. stat, p_value = stats.ttest_rel(a, b)
  197. else:
  198. stat, p_value = np.nan, np.nan
  199. return {
  200. "test_family": "paired_samples_ttest",
  201. "comparison": label,
  202. "variable": f"{variable_a} vs {variable_b}",
  203. "group_a": age_group if age_group is not None else "all",
  204. "group_b": "within_subject",
  205. "n_a": int(len(paired)),
  206. "mean_a": fmt_float(a.mean()),
  207. "sd_a": fmt_float(a.std(ddof=1)),
  208. "n_b": int(len(paired)),
  209. "mean_b": fmt_float(b.mean()),
  210. "sd_b": fmt_float(b.std(ddof=1)),
  211. "t_statistic": fmt_float(stat),
  212. "df": int(len(paired) - 1) if len(paired) >= 2 else np.nan,
  213. "p_value_raw": fmt_float(p_value),
  214. "equal_variance_assumed": np.nan,
  215. }
  218. def article_style_ttests(df):
  219. """Primary tests corresponding to the article narrative."""
  220. tests = []
  222. # Age-group comparisons.
  223. tests.append(independent_ttest(
  224. df, "Baseline single-pulse MEP amplitude: younger vs older",
  225. "sp_baseline_mep_uv"
  226. ))
  227. tests.append(independent_ttest(
  228. df, "Baseline paired-pulse/single-pulse ratio: younger vs older",
  229. "baseline_pp_sp_ratio"
  230. ))
  231. tests.append(independent_ttest(
  232. df, "Midazolam paired-pulse/single-pulse ratio: younger vs older",
  233. "midazolam_pp_sp_ratio"
  234. ))
  236. # Within-group single-pulse versus paired-pulse comparisons at baseline.
  237. for group in ["younger", "older"]:
  238. tests.append(paired_ttest(
  239. df, f"Baseline SP versus PP MEP amplitude within {group}",
  240. "sp_baseline_mep_uv", "pp_baseline_mep_uv", age_group=group
  241. ))
  243. # Midazolam effect on single-pulse MEP amplitude within each group.
  244. for group in ["younger", "older"]:
  245. tests.append(paired_ttest(
  246. df, f"Single-pulse baseline versus midazolam MEP amplitude within {group}",
  247. "sp_baseline_mep_uv", "sp_midazolam_mep_uv", age_group=group
  248. ))
  250. # Within-group single-pulse versus paired-pulse comparisons during midazolam.
  251. for group in ["younger", "older"]:
  252. tests.append(paired_ttest(
  253. df, f"Midazolam SP versus PP MEP amplitude within {group}",
  254. "sp_midazolam_mep_uv", "pp_midazolam_mep_uv", age_group=group
  255. ))
  257. # Baseline versus midazolam paired-pulse MEP amplitudes within each group.
  258. for group in ["younger", "older"]:
  259. tests.append(paired_ttest(
  260. df, f"Paired-pulse baseline versus midazolam MEP amplitude within {group}",
  261. "pp_baseline_mep_uv", "pp_midazolam_mep_uv", age_group=group
  262. ))
  264. out = pd.DataFrame(tests)
  265. pvals = out["p_value_raw"].astype(float).values
  266. valid = ~np.isnan(pvals)
  267. out["p_value_bh_adjusted"] = np.nan
  268. out["reject_bh_0_05"] = False
  269. if valid.sum() > 0 and STATSMODELS_AVAILABLE:
  270. reject, p_adj, _, _ = multipletests(pvals[valid], alpha=ALPHA, method="fdr_bh")
  271. out.loc[valid, "p_value_bh_adjusted"] = p_adj
  272. out.loc[valid, "reject_bh_0_05"] = reject
  273. return out
  276. def one_way_anovas(df):
  277. if not STATSMODELS_AVAILABLE:
  278. return pd.DataFrame({"note": ["statsmodels not installed; ANOVAs not run."]})
  280. variables = [
  281. "sp_baseline_mep_uv",
  282. "baseline_pp_sp_ratio",
  283. "sp_midazolam_mep_uv",
  284. "midazolam_pp_sp_ratio",
  285. ]
  286. rows = []
  287. for variable in variables:
  288. sub = df[["age_group", variable]].dropna().copy()
  289. if sub["age_group"].nunique() < 2:
  290. continue
  291. model = smf.ols(f"{variable} ~ C(age_group)", data=sub).fit()
  292. aov = anova_lm(model, typ=2).reset_index().rename(columns={"index": "term"})
  293. for _, r in aov.iterrows():
  294. rows.append({
  295. "model": f"{variable} ~ age_group",
  296. "term": r["term"],
  297. "sum_sq": fmt_float(r.get("sum_sq")),
  298. "df": fmt_float(r.get("df")),
  299. "F": fmt_float(r.get("F")),
  300. "p_value": fmt_float(r.get("PR(>F)")),
  301. })
  302. return pd.DataFrame(rows)
  305. def make_mep_long(df):
  306. rows = []
  307. for _, r in df.iterrows():
  308. for drug_condition, sp_col, pp_col in [
  309. ("baseline", "sp_baseline_mep_uv", "pp_baseline_mep_uv"),
  310. ("midazolam", "sp_midazolam_mep_uv", "pp_midazolam_mep_uv"),
  311. ]:
  312. for pulse_condition, col in [("single_pulse", sp_col), ("paired_pulse", pp_col)]:
  313. value = r[col]
  314. if pd.notna(value):
  315. rows.append({
  316. "participant_id": r["participant_id"],
  317. "age_group": r["age_group"],
  318. "drug_condition": drug_condition,
  319. "pulse_condition": pulse_condition,
  320. "mep_uv": value,
  321. })
  322. return pd.DataFrame(rows)
  325. def repeated_measures_anovas_within_age_group(df):
  326. """Within-age-group repeated-measures ANOVA for drug condition x pulse condition."""
  327. if not STATSMODELS_AVAILABLE:
  328. return pd.DataFrame({"note": ["statsmodels not installed; repeated-measures ANOVAs not run."]})
  330. long = make_mep_long(df)
  331. rows = []
  332. for group in ["younger", "older"]:
  333. sub = long.loc[long["age_group"] == group].copy()
  334. # AnovaRM requires complete balanced data for within-subject factors.
  335. counts = sub.groupby("participant_id").size()
  336. complete_ids = counts[counts == 4].index
  337. sub = sub.loc[sub["participant_id"].isin(complete_ids)].copy()
  338. if sub["participant_id"].nunique() < 2:
  339. rows.append({
  340. "age_group": group,
  341. "term": "not_run",
  342. "F Value": np.nan,
  343. "Num DF": np.nan,
  344. "Den DF": np.nan,
  345. "Pr > F": np.nan,
  346. "note": "Insufficient complete cases for repeated-measures ANOVA.",
  347. })
  348. continue
  349. try:
  350. aov = AnovaRM(
  351. data=sub,
  352. depvar="mep_uv",
  353. subject="participant_id",
  354. within=["drug_condition", "pulse_condition"],
  355. ).fit().anova_table.reset_index().rename(columns={"index": "term"})
  356. for _, r in aov.iterrows():
  357. rows.append({
  358. "age_group": group,
  359. "term": r["term"],
  360. "F Value": fmt_float(r.get("F Value")),
  361. "Num DF": fmt_float(r.get("Num DF")),
  362. "Den DF": fmt_float(r.get("Den DF")),
  363. "Pr > F": fmt_float(r.get("Pr > F")),
  364. "note": "Complete-case within-age-group repeated-measures ANOVA.",
  365. })
  366. except Exception as exc:
  367. rows.append({
  368. "age_group": group,
  369. "term": "model_error",
  370. "F Value": np.nan,
  371. "Num DF": np.nan,
  372. "Den DF": np.nan,
  373. "Pr > F": np.nan,
  374. "note": str(exc),
  375. })
  376. return pd.DataFrame(rows)
  379. def mixed_effects_wald_tests_mep(df):
  380. """Mixed-effects model for MEP amplitude with random participant intercepts."""
  381. if not STATSMODELS_AVAILABLE:
  382. return pd.DataFrame({"note": ["statsmodels not installed; mixed model not run."]})
  384. long = make_mep_long(df)
  385. try:
  386. with warnings.catch_warnings():
  387. warnings.simplefilter("ignore")
  388. model = smf.mixedlm(
  389. "mep_uv ~ C(age_group) * C(drug_condition) * C(pulse_condition)",
  390. data=long,
  391. groups=long["participant_id"],
  392. )
  393. fit = model.fit(reml=False, method="lbfgs")
  394. table = fit.wald_test_terms(scalar=True).table.reset_index().rename(columns={"index": "term"})
  395. table["model"] = "MixedLM: mep_uv ~ age_group * drug_condition * pulse_condition + random participant intercept"
  396. table = table.rename(columns={"chi2": "wald_chi_square", "P>chi2": "p_value"})
  397. cols = ["model", "term"] + [c for c in table.columns if c not in ["model", "term"]]
  398. return table[cols]
  399. except Exception as exc:
  400. return pd.DataFrame({
  401. "model": ["MixedLM: mep_uv ~ age_group * drug_condition * pulse_condition + random participant intercept"],
  402. "term": ["model_error"],
  403. "note": [str(exc)],
  404. })
  407. def write_analysis_notes(df):
  408. notes = []
  409. notes.append("Analysis notes")
  410. notes.append("==============")
  411. notes.append("")
  412. notes.append(f"Input file: {DATA_FILE}")
  413. notes.append(f"Ratio mode: {RATIO_MODE}")
  414. notes.append("Primary article-style tests use Student independent-samples t-tests for between-age-group comparisons and paired-samples t-tests for within-subject comparisons.")
  415. notes.append("Benjamini-Hochberg adjustment is applied across the article_style_ttests.csv p_value_raw column.")
  416. notes.append("One-way ANOVA outputs are included because a two-group one-way ANOVA is equivalent to the corresponding independent-samples t-test.")
  417. notes.append("Within-age-group repeated-measures ANOVAs are included for drug condition x pulse condition MEP amplitude as supplementary checks.")
  418. notes.append("Mixed-effects Wald tests are included as an additional exploratory model because the dataset contains incomplete midazolam data for some older participants.")
  419. notes.append("")
  420. notes.append("Important: The manuscript interprets paired-pulse responses as net physiological responses under the stimulation parameters used, not as a direct receptor-specific assay.")
  421. notes.append("")
  422. notes.append("Participant counts by age group:")
  423. counts = df.groupby("age_group", observed=False)["participant_id"].nunique()
  424. for k, v in counts.items():
  425. notes.append(f" {k}: {v}")
  426. notes.append("")
  427. notes.append("Missingness summary for primary variables:")
  428. for col in [
  429. "sp_baseline_mep_uv", "sp_midazolam_mep_uv",
  430. "pp_baseline_mep_uv", "pp_midazolam_mep_uv",
  431. "baseline_pp_sp_ratio", "midazolam_pp_sp_ratio",
  432. ]:
  433. notes.append(f" {col}: {int(df[col].isna().sum())} missing")
  434. (OUTPUT_DIR / "analysis_notes.txt").write_text("\n".join(notes), encoding="utf-8")
  437. def main():
  438. ensure_output_dir()
  439. df = load_data()
  441. descriptive_statistics(df).to_csv(OUTPUT_DIR / "descriptive_statistics.csv", index=False)
  442. shapiro_normality_tests(df).to_csv(OUTPUT_DIR / "normality_tests.csv", index=False)
  443. article_style_ttests(df).to_csv(OUTPUT_DIR / "article_style_ttests.csv", index=False)
  444. one_way_anovas(df).to_csv(OUTPUT_DIR / "one_way_anovas.csv", index=False)
  445. repeated_measures_anovas_within_age_group(df).to_csv(
  446. OUTPUT_DIR / "repeated_measures_anovas_within_age_group.csv", index=False
  447. )
  448. mixed_effects_wald_tests_mep(df).to_csv(OUTPUT_DIR / "mixed_effects_wald_tests_mep.csv", index=False)
  449. write_analysis_notes(df)
  451. print(f"Analysis complete. Output files written to: {OUTPUT_DIR.resolve()}")
  454. if __name__ == "__main__":
  455. main()

analysis_code.py, under CC-BY-4.0 · at the source

Overview

Authors: Keith M McGregor1, Seyed A Safavynia2, Thomas Novak3,4, Ashton M Weber1, Jessica Wang5, Joe R Nocera3,4,6, Anna Woodbury3,7,8, Bruce Crosson4,9, Paul S García10
ORCID iDs: Keith M McGregor, Seyed A Safavynia, Jessica Wang
  1. Department of Clinical and Diagnostic Sciences, School of Health Professions, University of Alabama at Birmingham, Birmingham, Alabama, United States of America
  2. Department of Anesthesiology, Division of Neuroanesthesiology, Weill Cornell Medicine, New York-Presbyterian Hospital, New York, New York, United States of America
  3. VA Rehabilitation R&D Center for Visual and Neurocognitive Rehabilitation, Atlanta VA Health Care System, Atlanta, Georgia, United States of America
  4. Department of Neurology, Emory University School of Medicine, Atlanta, Georgia, United States of America
  5. Wayne State University School of Medicine, Detroit, Michigan, United States of America
  6. Department of Rehabilitation Medicine, Emory University School of Medicine, Atlanta, Georgia, United States of America
  7. Department of Anesthesiology, Emory University School of Medicine, Atlanta, Georgia, United States of America
  8. Department of Geriatrics & Gerontology, Emory University School of Medicine, Atlanta, Georgia, United States of America
  9. Department of Radiology, Emory University School of Medicine, Atlanta, Georgia, United States of America
  10. Department of Anesthesiology, Neuroanesthesia Division, Columbia University Medical Center, New York-Presbyterian Hospital, New York, New York, United States of America
Institutions: University of Alabama at Birmingham (United States); NewYork–Presbyterian Hospital (United States); Emory University (United States); Atlanta VA Health Care System (United States); Wayne State University (United States); Columbia University Irving Medical Center (United States)
Journal: PloS one, volume 21, issue 11, article e0353088
Dates: received 27 June 2026; accepted 23 October 2026; published online 6 November 2026
Type: Research article · Language: English
License: none stated
Identifiers: DOI 10.1371/journal.pone.0353088 · PMID 42993790 · PMCID PMC13743024 · OpenAlex W7165649810
Open access: gold, a free copy (OpenAlex)
Preprint: doi:10.64898/2026.06.20.733493
Status: code verified (how the code is found and verified)
Categories: other (modality), human (organism), systems (subfield)
Methods: Spectral & time-frequency, Statistics, Preprocessing, Evoked potentials, Physiology & signal measures
MeSH: Evoked Potentials, Motor*, Midazolam*, Motor Cortex*, Transcranial Magnetic Stimulation*, Administration, Intravenous, Adolescent, Adult, Age Factors, Aged, Aging, Female, Humans, Male, Middle Aged, Young Adult (* major topic)
Topic: Transcranial Magnetic Stimulation Studies (Neurology, Neuroscience), according to OpenAlex
Funding: U.S. Department of Veterans Affairs
Citations: not cited yet (Europe PMC); 32 references in the paper

Abstract

The abstract is not reproduced here: the paper's license (none stated) does not allow it. Read it in the paper, at the publisher or on Europe PMC.

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Zenodo 20734853

License: CC-BY-4.0
State: the link answers, verified on 9 November 2026
Evidence: files inventoried
Size: 1 file
Software Heritage: not checked
Found in: “Data Availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: NumPy (1 file), pandas (1 file), SciPy (1 file), statsmodels (1 file)
Availability: 1 check, the latest on 9 November 2026: the link answers (HTTP 200)
  • 9 November 2026: the link answers (HTTP 200)
1 file
At the source: doi.org/10.5281/zenodo.20734853

The paper's code and data availability statement is in the Data section.

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Data

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Version 1, 9 November 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 9 authors, 15 MeSH terms, 1 funder, 32 references.

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This paper

McGregor, K. M., Safavynia, S. A., Novak, T., Weber, A. M., Wang, J., Nocera, J. R., Woodbury, A., Crosson, B., & García, P. S. (2026). Intravenous midazolam alters short-interval paired-pulse TMS responses differently in younger and older adults. PloS one, 21(11), e0353088. https://doi.org/10.1371/journal.pone.0353088

BibTeX

@article{mcgregor2026intravenous,
author = {McGregor, Keith M and Safavynia, Seyed A and Novak, Thomas and Weber, Ashton M and Wang, Jessica and Nocera, Joe R and Woodbury, Anna and Crosson, Bruce and García, Paul S},
title = {{Intravenous midazolam alters short-interval paired-pulse TMS responses differently in younger and older adults}},
journal = {PloS one},
year = {2026},
month = nov,
volume = {21},
number = {11},
pages = {e0353088},
publisher = {PLOS},
issn = {1932-6203},
doi = {10.1371/journal.pone.0353088},
url = {https://doi.org/10.1371/journal.pone.0353088},
pmid = {42993790},
pmcid = {PMC13743024}
}

RIS

TY - JOUR
AU - McGregor, Keith M
AU - Safavynia, Seyed A
AU - Novak, Thomas
AU - Weber, Ashton M
AU - Wang, Jessica
AU - Nocera, Joe R
AU - Woodbury, Anna
AU - Crosson, Bruce
AU - García, Paul S
TI - Intravenous midazolam alters short-interval paired-pulse TMS responses differently in younger and older adults
T2 - PloS one
J2 - PLoS One
PY - 2026
DA - 2026/11/06
VL - 21
IS - 11
SP - e0353088
SN - 1932-6203
PB - PLOS
DO - 10.1371/journal.pone.0353088
UR - https://doi.org/10.1371/journal.pone.0353088
LA - en
ER -

CSL-JSON

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"author": [
{
"family": "McGregor",
"given": "Keith M"
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{
"family": "Safavynia",
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{
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},
{
"family": "Weber",
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},
{
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},
{
"family": "Nocera",
"given": "Joe R"
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{
"family": "Woodbury",
"given": "Anna"
},
{
"family": "Crosson",
"given": "Bruce"
},
{
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"given": "Paul S"
}
],
"container-title-short": "PLoS One",
"volume": "21",
"issue": "11",
"page": "e0353088",
"DOI": "10.1371/journal.pone.0353088",
"PMID": "42993790",
"PMCID": "PMC13743024",
"ISSN": "1932-6203",
"publisher": "PLOS",
"URL": "https://doi.org/10.1371/journal.pone.0353088",
"language": "en",
"issued": {
"date-parts": [
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2026,
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6
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]
}
}

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