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Hemisphere fingerprinting: Identifying left and right hemispheres using functional connectivity.

Code ↔ Paper

10 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 10 matches
  1. [1] § Analysis 1: Hemispheric Classification in Single-Handedness Groups › Methods › Atlas selection ↔ code/LDA/LD03-node_centrality.R, lines 52 to 108 · score 0.88 · ventral multimodal, orbito affective, AudN, visual networks, Cole Anticevic, posterior · Report a mismatch
  2. [2] § Analysis 1: Hemispheric Classification in Single-Handedness Groups › Methods › Atlas selection ↔ code/LDA/LD03-node_centrality_coef.R, lines 175 to 236 · score 0.88 · ventral multimodal, orbito affective, AudN, visual networks, Cole Anticevic, posterior · Report a mismatch
  3. [3] § Analysis 1: Hemispheric Classification in Single-Handedness Groups › Methods ↔ code/modeling_new/03-right_handed/run_model_kfold.py, lines 169 to 208 · score 0.69 · support vector classification, linear discriminant, neural, SVC, weight, NN · Report a mismatch
  4. [4] § Analysis 1: Hemispheric Classification in Single-Handedness Groups › Methods › Modeling ↔ code/modeling_new/03-right_handed/run_model_kfold.py, lines 169 to 208 · score 0.69 · LinearDiscriminantAnalysis, neural network, scikit-learn, svm, svc, Modeling · Report a mismatch
  5. [5] § Analysis 1: Hemispheric Classification in Single-Handedness Groups › Methods › Modeling ↔ code/modeling/run_model.py, lines 129 to 156 · score 0.68 · LinearDiscriminantAnalysis, neural network, scikit-learn, svm, svc, Modeling · Report a mismatch
  6. [6] § Analysis 1: Hemispheric Classification in Single-Handedness Groups › Methods › Fold creation ↔ code/modeling_new/01-create_balanced_groups.R, lines 424 to 505 · score 0.58 · dextral matched, dextral random, male, fold, gender, age · Report a mismatch
  7. [7] § Analysis 1: Hemispheric Classification in Single-Handedness Groups › Methods › Atlas selection ↔ code/modeling_new/02-create_input_data.R, lines 1 to 61 · score 0.53 · Glasser RR, Glasser LL, parcels, hemisphere · Report a mismatch
  8. [8] § Analysis 1: Hemispheric Classification in Single-Handedness Groups › Results › Parcel symmetrization robustness test ↔ code/modeling_new/02-create_input_data.R, lines 1 to 61 · score 0.53 · Glasser RR, Glasser LL, parcels, Hemisphere · Report a mismatch
  9. [9] § Analysis 1: Hemispheric Classification in Single-Handedness Groups › Methods › Atlas selection ↔ 01b-create_LLRR_glasser.sh, lines 50 to 96 · score 0.53 · Glasser RR, Glasser LL · Report a mismatch
  10. [10] § Analysis 1: Hemispheric Classification in Single-Handedness Groups › Results › Parcel symmetrization robustness test ↔ 01b-create_LLRR_glasser.sh, lines 50 to 96 · score 0.52 · Glasser RR, Glasser LL · Report a mismatch

Paper

Loaded from Europe PMC by your browser; OSCR does not store it: doi.org · Europe PMC

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The authors' code

Python · 375 lines · 13 KB · no license · 2 matches

  2. from matplotlib.pyplot import axis
  3. from IPython.core import error
  4. import pandas as pd
  5. import numpy as np
  6. import matplotlib.pyplot as plt
  7. import copy
  8. import sklearn
  9. import os
  10. import time
  12. from sklearn.metrics import matthews_corrcoef
  13. from pathlib import Path
  15. def bootstrap_mcc_value(y_true, y_pred, n_reps=500):
  17. df_orig = pd.DataFrame({"y_true": y_true, "y_pred": y_pred})
  19. # Sample n reps
  20. mccs = list()
  21. for i in range(n_reps):
  22. df_new = df_orig.sample(frac = 1, replace=True)
  23. mcc_new = matthews_corrcoef(y_true=df_new["y_true"],
  24. y_pred=df_new["y_pred"])
  25. mccs += [mcc_new]
  27. mean_mcc = np.mean(mccs)
  28. mcc_quantiles = np.quantile(mccs, [0.025, 0.975])
  30. # Plot
  31. # fig, ax = plt.subplots()
  33. # ax.hist(mccs, bins=8, linewidth=0.5, edgecolor="white")
  34. # ax.set(xlim=(0, 1))
  35. # plt.show()
  37. return((mean_mcc, mcc_quantiles))
  39. def run_my_kfold(data_tuple, outcome_cols, id_cols, classifier,
  40. output_dir, group_col="group", bootstrap_mcc=False,
  41. shuffle_train_labels = True,
  42. collapse_mcc=True, dump_scalings=False):
  44. """
  45. Run k-fold prediction on a data-frame.
  47. Args:
  49. data_tuple (tuple): A 2-tuple that contains the label and a data frame
  50. with at least the following columns.
  52. outcome_cols (string): A string or list of strings containing the
  53. columns to predict.
  55. id_cols (string): A list of strings containing which columns are
  56. identifiers and not predictors.
  58. classifier (string): lda, nn, or svc
  60. output_dir (Path object): Where to write out the files.
  62. group_col (string): Default: "group", the column to base the folds on.
  63. If None, does all-to-all prediction.
  65. bootstrap_mcc (logical): If False, the raw MCC is returned without
  66. a bootstrapped 95% CI.
  68. collapse_mcc (logical): If True, only overall MCC is returned, and not
  69. on a per-group_col basis.
  71. dump_scalings (logical): If True, the scalings (per feature are saved
  72. to a CSV).
  74. Returns:
  76. A 3-list containing a data frame of predicted outcomes, per-group MCCs
  77. and the overall MCC, and 95% CIs, if requested.
  80. """
  82. # Split data name and the actual data from the tuple
  83. dt = copy.deepcopy(data_tuple)
  84. data_name, data = dt
  86. print(f"\nStarting work on: {data_name}")
  88. # Replace a string with one a one-item list
  89. if isinstance(outcome_cols, str):
  90. outcome_cols = [outcome_cols]
  92. print(f" Dump scalings: {dump_scalings}")
  94. # Remove the control columns from the ID columns
  95. id_cols_local = list(id_cols)
  96. ctrl_cols = outcome_cols + [group_col]
  98. removed_cols = list(set(ctrl_cols) - set(id_cols_local))
  99. id_cols_local = [x for x in id_cols_local if x not in ctrl_cols]
  101. if len(removed_cols) > 0:
  102. print(f" Removed control columns {removed_cols} from ID columns")
  104. removed_cols2 = list(set(id_cols_local) - set(data.columns))
  105. id_cols_local = [x for x in id_cols_local if x in data.columns]
  107. if len(removed_cols2) > 0:
  108. print(f" Removed columns {removed_cols2} from ID columns")
  110. print(f" Final ID columns: {id_cols_local}")
  113. # Create new outcome column by joining names
  114. all_outcomes = '.'.join(outcome_cols)
  115. # Create an outcome column for crossed vars if necessary
  116. if len(outcome_cols) > 1:
  117. outcome_df = data[outcome_cols]
  118. data[all_outcomes] = outcome_df.apply('.'.join, axis=1)
  120. print(f" Predicting groups: {set(data[all_outcomes])}")
  122. # Create empty dataframe to store results. We're just gonna inefficently
  123. # append.
  124. results_df = pd.DataFrame(columns=id_cols_local + ["predicted"])
  125. group_mccs = pd.DataFrame(columns=["group", "mcc", "mcc_ci_lo",
  126. "mcc_ci_hi"])
  129. # Save results
  130. if output_dir is not None:
  132. if not os.path.exists(Path(output_dir)):
  133. os.mkdir(Path(output_dir))
  134. print(f" ! Created directory {output_dir}")
  136. output_name = Path(output_dir) / \
  137. f"data-{data_name}_clf-{classifier}_outcome-{all_outcomes}_results.csv"
  139. scalings_name = Path(output_dir) / \
  140. f"data-{data_name}_clf-{classifier}_outcome-{all_outcomes}_scalings.csv"
  142. if os.path.exists(output_name):
  143. print(f"Requested output file {output_name} already exists!")
  144. return()
  146. # Get the groups from the group column
  147. if group_col is not None:
  148. groups = list(set(data[group_col]))
  149. groups.sort()
  150. else:
  151. groups = [None]
  153. print(" Running hold outs ...")
  154. for hold_out in groups:
  156. print(f" {hold_out} ", end="")
  158. if hold_out is not None:
  159. # Filter data down to non-held-out group or only the held-out group
  160. # and then drop the grouping column from both train and test
  161. train = data[data[group_col] != hold_out].drop([group_col], axis=1,
  162. errors="ignore")
  163. test = data[data[group_col] == hold_out].drop([group_col], axis=1,
  164. errors="ignore")
  165. else:
  166. # If doing 'None', it's test/train within
  167. train = test = data
  169. # Extract the ground truths
  170. train_y = train[all_outcomes].tolist()
  171. test_y = test[all_outcomes].reset_index(drop=True)
  173. if shuffle_train_labels:
  174. train_y = np.random.permutation(train_y)
  176. # Extract the columns to re-add to the output
  177. # train_id = train[id_cols_local]
  178. # CURRENT PROBLEM: Can't subset columns that don't exist
  179. test_id = test[id_cols_local].reset_index(drop=True)
  181. # Remove the outcome and ID columns from the training data
  182. train_x = train.drop([all_outcomes] + outcome_cols + id_cols_local,
  183. axis=1, errors="ignore")
  184. test_x = test.drop([all_outcomes] + outcome_cols + id_cols_local,
  185. axis=1, errors="ignore")
  187. # Set up and run the model
  189. if classifier == "lda":
  190. clf = sklearn.discriminant_analysis.LinearDiscriminantAnalysis()
  191. elif classifier == "nn":
  192. # Neural Net
  193. clf = sklearn.neural_network.MLPClassifier(random_state=1,
  194. max_iter=300)
  195. elif classifier == "svc":
  196. # Support Vector CLASSIFIER
  197. clf = sklearn.svm.SVC(kernel="linear", class_weight="balanced")
  198. else:
  199. raise ValueError(f"Unknown classifier requested: {classifier}")
  201. # Actually fit the model and do the prediction
  202. model = clf.fit(train_x, train_y)
  203. y_hat = model.predict(test_x)
  205. # Get the LD values and add it to the results
  206. if classifier == "lda":
  207. print(test_x)
  208. y_transform = pd.DataFrame(model.transform(test_x))
  210. # Give the columns better names, starting with LD1, not LD0
  211. y_transform.columns = [f"LD{x}" for x
  212. in range(1, y_transform.shape[1] + 1)]
  214. # print(f"Test data shape: {test_x.shape}")
  215. # print(f"Transform shape: {y_transform.shape}")
  217. # Put the predicted column back together with the ID cols
  218. results = pd.concat([test_id, test_y], axis=1)
  219. results["predicted"] = y_hat
  221. if classifier == "lda":
  222. print(y_transform)
  223. results = pd.concat([results, y_transform], axis=1)
  225. print(results)
  227. if not collapse_mcc:
  228. if not bootstrap_mcc:
  229. mcc1 = matthews_corrcoef(y_true=results[all_outcomes],
  230. y_pred=results["predicted"])
  231. print(f"MCC: {mcc1:.3f}")
  232. mcc_ci1 = [None, None]
  233. else:
  234. mcc1, mcc_ci1 = bootstrap_mcc_value(y_true=results[all_outcomes],
  235. y_pred=results["predicted"])
  237. # Create a vector of values
  238. group_row = pd.DataFrame({"group": hold_out, "mcc": float(mcc1),
  239. "mcc_ci_lo": float(mcc_ci1[0]),
  240. "mcc_ci_hi": float(mcc_ci1[1])},
  241. index=[0])
  243. group_mccs = pd.concat([group_mccs, group_row])
  245. # If requested, save the scalings to a CSV
  246. if classifier == "lda":
  248. scalings = pd.DataFrame(model.scalings_)
  249. scalings.columns = ["scaling"]
  250. scalings["feature"] = model.feature_names_in_
  252. if output_dir is not None and dump_scalings:
  253. n_cols = scalings.shape[1]
  254. scalings.to_csv(scalings_name, index=False,
  255. header=[f"LD{x}" for x in range(1, n_cols + 1)])
  257. results_df = pd.concat([results_df, results])
  259. # Show final results
  260. print(results_df)
  261. tab = pd.crosstab(results_df[all_outcomes], results_df["predicted"])
  263. # Display purposes only
  264. print("\nConfusion matrix:")
  265. print(tab)
  266. print()
  268. # Display basic accuracy
  269. acc = np.diag(tab).sum() / tab.to_numpy().sum()
  270. acc_cirange = (acc * (1 - acc) / tab.to_numpy().sum()) ** 0.5 * 1.96
  271. acc_ci = [acc - acc_cirange, acc + acc_cirange]
  272. print(f"Overall accuracy: {acc:.3f} [{acc_ci[0]:.3f}, {acc_ci[1]:.3f}]")
  274. if not bootstrap_mcc:
  275. # If not bootstrapping MCC, return missing values for the CI
  276. mcc = matthews_corrcoef(y_true=results_df[all_outcomes],
  277. y_pred=results_df["predicted"])
  278. print(f"MCC: {mcc:.3f}")
  279. mcc_CI = [None, None]
  280. else:
  281. mcc, mcc_CI = bootstrap_mcc_value(y_true=results_df[all_outcomes],
  282. y_pred=results_df["predicted"])
  284. print(f"Overall MCC: {mcc:.3f} [{mcc_CI[0]:.3f}, {mcc_CI[1]:.3f}]")
  286. # if len(outcome_cols) > 1:
  287. # print(f"n\nStarting per-outcome calculations: {outcome_cols}")
  289. # outcome = results_df["predicted"]
  290. # outcome2 = outcome.str.split(".", expand=True)
  291. # outcome2.columns = outcome_cols
  293. # for o in outcome_cols:
  295. # # print(f"\n{o}")
  296. # print()
  297. # tab = pd.crosstab(outcome2[o], data[o])
  298. # print(tab)
  300. # o_mcc, o_mcc_CI = bootstrap_mcc_value(y_true=data[o],
  301. # y_pred=outcome2[o])
  303. # print(f"MCC ({o}): {o_mcc:.3f} [{o_mcc_CI[0]:.3f}, ",
  304. # f"{o_mcc_CI[1]:.3f}]")
  306. if output_dir is not None:
  307. results_df.to_csv(output_name, index=False)
  308. print(f"Saved to {output_name}!")
  310. # Put the overall MCC into an easily readable dataframe for later
  311. if bootstrap_mcc:
  312. overall_mcc = pd.DataFrame({"mcc": float(mcc),
  313. "mcc_ci_lo": float(mcc_CI[0]),
  314. "mcc_ci_hi": float(mcc_CI[1])},
  315. index=[0])
  316. else:
  317. overall_mcc = float(mcc)
  319. result = [results_df, group_mccs, overall_mcc]
  320. if classifier == "lda":
  321. result += [scalings]
  323. return(result)
  326. def create_null_distribution(data_tuple, cols_to_shuffle, id_cols,
  327. output_dir, n=10000):
  329. # Unpack data
  330. dt = copy.deepcopy(data_tuple)
  331. label, data = dt
  332. label = f"{label}.null"
  334. print(f"Shuffling columns: {cols_to_shuffle}")
  335. cols = '.'.join(cols_to_shuffle)
  337. results = [None] * n
  339. for i in range(0, n):
  341. start_time = time.perf_counter()
  343. new_label = f"{label}_bs-{i}"
  344. new_data = (new_label, data)
  346. _, _, _, temp_result = \
  347. run_my_kfold(new_data,
  348. outcome_cols=cols_to_shuffle, id_cols=id_cols,
  349. output_dir=None,
  350. shuffle_train_labels=True,
  351. group_col=None, classifier="lda", dump_scalings=True)
  353. temp_result["bs"] = i
  355. results[i] = temp_result
  357. end_time = time.perf_counter()
  358. execution_time = end_time - start_time
  360. if i == 0:
  362. total_sec = n*execution_time
  363. total_min = total_sec / 60
  365. print("\nTiming info:")
  366. print(f"Execution time: {execution_time:.2f} sec")
  367. print(f"Total estimated time: {total_sec:.2f} sec " + \
  368. f"({total_min:.1f}) min")
  369. input("Press enter to execute")
  371. final_result = pd.concat(results)
  373. final_result.to_csv(Path(output_dir) /
  374. f"data-{label}_shuffle-{cols}_n-{n}_bsscalings.csv",
  375. index=False)

run_model_kfold.py at commit d87f355, no license · at the source

Overview

Authors: Trevor KM Day1, Peter E Turkeltaub1,2, Elissa L Newport1,2, Andrew T DeMarco1
ORCID iDs: Trevor KM Day, Peter E Turkeltaub, Elissa L Newport, Andrew T DeMarco
  1. Center for Brain Plasticity and Recovery, Georgetown University, Washington, DC, United States
  2. MedStar National Rehabilitation Hospital, Washington, DC, United States
Institutions: Georgetown University (United States); Center for Brain Plasticity and Recovery (United States); MedStar National Rehabilitation Hospital (United States)
Journal: Imaging neuroscience (Cambridge, Mass.), volume 4, article IMAG.a.1397
Dates: received 12 January 2026; accepted 11 September 2026; published online 27 October 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1162/imag.a.1397 · PMID 42953869 · PMCID PMC13716284 · OpenAlex W7214161779
Open access: diamond, a free copy (OpenAlex)
Status: code verified (how the code is found and verified)
Categories: human (organism), systems (subfield)
Methods: Connectivity, Statistics, Machine learning, fMRI & imaging
Keywords: hemispheric organization, handedness, right handedness, left handedness, connectome
MeSH: Brain*, Connectome*, Functional Laterality*, Adult, Female, Humans, Magnetic Resonance Imaging, Male, Neural Pathways, Young Adult (* major topic)
Topic: Hemispheric Asymmetry in Neuroscience (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: National Science Foundation; National Institute on Deafness and Other Communication Disorders
Citations: not cited yet (Europe PMC); 116 references in the paper

Abstract

Many studies have analyzed what organizational features distinguish the left and right hemispheres of the human brain, with differences typically being found in language areas and in fine motor control (i.e., handedness). In this analysis, we test whether supervised learning can categorize (“fingerprint”) an unseen hemisphere as right or left based on functional connectivity. Using data from the Human Connectome Project, we find success to be extremely high (accuracies > .90) in models trained on right-handed participants (Edinburgh Handedness Inventory [EHI] > 0) and in models trained on left-handed participants (EHI ≤ 0). In a second analysis, we test whether the same can be done to identify handedness alongside hemisphere left/right sidedness. While individuals’ hemispheres are less distinct the more left handed they are, hemiconnectomes cannot be reliably classified as belonging to a left- or right-handed person. Our approach can inform developmental and post-injury work on hemispheric organization and reorganization.

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

Repositories

Its files are read in the Code ↔ Paper reader above, with 10 matches between paragraphs and lines of code.

TrevorKMDay/hemidentification

License: none: the authors keep all their rights
State: the link answers, verified on 30 October 2026
Evidence: files inventoried
Commit: d87f355f93b03181be473830286aaff8fa93dad1, 1 July 2026
Languages: R (50), Python (23), Shell (18)
Size: 194 files, 91 scripts
Software Heritage: archived
Found in: “Data and Code Availability”
Holds: README, CITATION.cff
Not found: license file, environment file, tests, continuous integration, documentation
Tools: tidyverse (45 files), pandas (16 files), scikit-learn (12 files), patchwork (9 files), reticulate (8 files), igraph (7 files), NumPy (7 files), Matplotlib (6 files), Connectome Workbench (4 files), easystats (2 files), broom (1 file), data.table (1 file), imbalanced-learn (1 file), lme4 (1 file), lmerTest (1 file), multcomp (1 file), rstatix (1 file)
Availability: 1 check, the latest on 30 October 2026: the link answers
  • 30 October 2026: the link answers
92 files
At the source: github.com/TrevorKMDay/hemidentification

TrevorKMDay/glasser_LR_overlap

License: none: the authors keep all their rights
State: the link answers, verified on 30 October 2026
Evidence: files inventoried
Commit: 8af725ddcbd38e63fddd3c15d8c544956e42078c, 24 June 2026
Languages: Shell (3), R (1)
Size: 21 files, 4 scripts
Software Heritage: not archived
Found in: the references
Holds: README, CITATION.cff
Not found: license file, environment file, tests, continuous integration, documentation
Tools: Connectome Workbench (3 files), tidyverse (1 file)
Availability: 1 check, the latest on 30 October 2026: the link answers
  • 30 October 2026: the link answers
5 files
At the source: github.com/TrevorKMDay/glasser_LR_overlap

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

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:

  • 2 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 95 scripts, each with its path and the digest of its content;
  • 10 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 and Code Availability

All data are publicly available through the Human Connectome Project or Zenodo (for the parcel-to-network assignments). Analysis code is available at https://github.com/TrevorKMDay/hemidentification.

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

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 1, 30 October 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 4 authors, 5 keywords, 10 MeSH terms, 2 funders, 114 references.

Cite

This paper

Day, T. K., Turkeltaub, P. E., Newport, E. L., & DeMarco, A. T. (2026). Hemisphere fingerprinting: Identifying left and right hemispheres using functional connectivity. Imaging neuroscience (Cambridge, Mass.), 4, IMAG.a.1397. https://doi.org/10.1162/imag.a.1397

BibTeX

@article{day2026hemisphere,
author = {Day, Trevor KM and Turkeltaub, Peter E and Newport, Elissa L and DeMarco, Andrew T},
title = {{Hemisphere fingerprinting: Identifying left and right hemispheres using functional connectivity}},
journal = {Imaging neuroscience (Cambridge, Mass.)},
year = {2026},
month = oct,
volume = {4},
pages = {IMAG.a.1397},
publisher = {MIT Press},
issn = {2837-6056},
doi = {10.1162/imag.a.1397},
url = {https://doi.org/10.1162/imag.a.1397},
pmid = {42953869},
pmcid = {PMC13716284}
}

RIS

TY - JOUR
AU - Day, Trevor KM
AU - Turkeltaub, Peter E
AU - Newport, Elissa L
AU - DeMarco, Andrew T
TI - Hemisphere fingerprinting: Identifying left and right hemispheres using functional connectivity
T2 - Imaging neuroscience (Cambridge, Mass.)
J2 - Imaging Neurosci (Camb)
PY - 2026
DA - 2026/10/27
VL - 4
SP - IMAG.a.1397
SN - 2837-6056
PB - MIT Press
DO - 10.1162/imag.a.1397
UR - https://doi.org/10.1162/imag.a.1397
LA - en
ER -

CSL-JSON

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}
}

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