OSCR

The Brain Imaging and Neurophysiology Dataset of large-scale multimodal neural data.

Code ↔ Paper

4 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 4 matches
  1. [1] § Methods › Computational processing › Sequence names ↔ mri_sequence_identification.ipynb, lines 95–223 · score 0.99 · Echo Train Length, Flip Angle, fMRI, MR Acquisition, Sequence Variant, MRI sequence
  2. [2] § Methods › Computational processing › Clinical metadata extraction ↔ ask_llama_open.py, lines 153–233 · score 0.71 · mentioned pathologies, brain related, clinically important, acute, chronic, location
  3. [3] § Methods › Computational processing › Clinical metadata extraction ↔ ask_llama_category.py, lines 72–121 · score 0.64 · Bio Medical Llama, 3–8, asked, answer, Model, Clinical
  4. [4] § Methods › Computational processing › Clinical metadata extraction ↔ ask_llama_open.py, lines 102–151 · score 0.60 · Bio Medical Llama, 3–8, asked, Model

Paper

Loaded from Europe PMC by your browser, not stored by OSCR: doi.org · Europe PMC

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The authors' code

Python · 233 lines · 9.3 KB · CC-BY-NC-4.0 · 2 matches

  1. import os
  2. import json
  3. import torch
  4. import tarfile
  5. import argparse
  6. import numpy as np
  7. import pandas as pd
  8. from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
  10. def clean_json_string(json_str):
  11. # Try to load the string into a Python object
  12. try:
  13. # Attempt to load and parse the string, if valid JSON, return it
  14. data = json.loads(json_str)
  15. return data
  17. except json.JSONDecodeError:
  18. try:
  19. # If an error occurs, the string is likely incomplete, Remove the part after the last complete JSON object or array
  20. last_complete_json_end = json_str.rfind('}')
  21. if last_complete_json_end != -1:
  22. fixed_str = json_str[:last_complete_json_end + 1]
  23. return json.loads(fixed_str)
  24. except:
  25. # erroreous output
  26. new_str = '{"Name": "INCOMPLETE"}'
  27. return json.loads(new_str)
  30. def ask_llama (system_message,instruction_message, pipeline):
  31. messages = [{"role": "system", "content": system_message},
  32. {"role": "user",
  33. "content": instruction_message},
  34. ]
  35. prompt = pipeline.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
  36. terminators = [pipeline.tokenizer.eos_token_id, pipeline.tokenizer.convert_tokens_to_ids("<|eot_id|>")]
  38. with torch.no_grad():
  39. outputs = pipeline(prompt, max_new_tokens=2000, eos_token_id=terminators, do_sample=True, temperature=0.5,
  40. top_p=0.9)
  42. return outputs
  44. def read_from_tar(tar_path, file_name):
  45. with tarfile.open(tar_path, 'r') as tar:
  46. # Check if the file exists in the tar archive
  47. if file_name in tar.getnames():
  48. file = tar.extractfile(file_name)
  49. file_content = file.read()
  50. file_content = file_content.decode('utf-8')
  51. file_content = eval(file_content) # to dict
  52. else:
  53. print(f"File '{file_name}' not found in the tar archive.")
  54. file_content = None
  55. return file_content
  57. if __name__ == "__main__":
  58. parser = argparse.ArgumentParser(description="Run distance analysis")
  60. parser.add_argument(
  61. "-tar_data_dir",
  62. type=str,
  63. action="store",
  64. help="path to tar directory with data",
  65. )
  67. parser.add_argument(
  68. "-master_dir",
  69. action="store",
  70. default=False,
  71. help="Directory to csv with all patients",
  72. )
  74. parser.add_argument(
  75. "-output_dir",
  76. action="store",
  77. default=False,
  78. help="Directory to store results in",
  79. )
  81. parser.add_argument(
  82. "-model_dir",
  83. action="store",
  84. default=False,
  85. help="Directory to with models",
  86. )
  88. parser.add_argument(
  89. "-reverse",
  90. action="store",
  91. default=False,
  92. help="0 or 1 Start at the End of the Dataset to better parallelize",
  93. )
  95. parser.add_argument(
  96. "-split",
  97. action="store",
  98. default=False,
  99. help="between 0 to 80000, start index to calculate ten thousand samples.",
  100. )
  102. # set up device
  103. device = "cpu"
  104. if torch.cuda.is_available():
  105. device = "cuda"
  106. # if torch.backends.mps.is_available():
  107. # device = "mps"
  109. print(f'USING DEVICE {device}')
  111. # load data
  112. args = parser.parse_args()
  113. master = args.master_dir
  114. reverse = args.reverse
  115. REPORT_DIR = args.tar_data_dir
  116. OUT_DIR = args.output_dir
  117. model_dir = args.model_dir
  118. split = int(args.split)
  119. os.makedirs(OUT_DIR, exist_ok=True)
  120. master = pd.read_csv(master)
  122. # load llama
  123. model_name = 'ContactDoctor/Bio-Medical-Llama-3-8B'
  124. tokenizer = AutoTokenizer.from_pretrained(f'{model_dir}/tokenizers/{model_name}')
  125. model = AutoModelForCausalLM.from_pretrained(f'{model_dir}/models/{model_name}',device_map=device)
  126. pipel = pipeline("text-generation", model=model,tokenizer=tokenizer, model_kwargs={"torch_dtype": torch.bfloat16})
  127. # set model to eval mode
  128. model.eval()
  130. # if MGB
  131. if 'MGB' in REPORT_DIR:
  132. master['aws_file'] = master['aws_filename'].str.replace('/','_')
  134. if split:
  135. selection = master['aws_file'][split:split+10000]
  136. else:
  137. selection = master['aws_file']
  139. # start at the end if reverese = 1
  140. if int(reverse):
  141. selection = selection.iloc[::-1]
  143. for id_stan in selection:
  144. id_tmp = id_stan.split('.')[0]
  145. # fname = f"{REPORT_DIR}/Report_ID_{id_stan[:-4]}.json"
  146. out_file1 = f'{OUT_DIR}/LLAMA_out_{id_tmp}_scaninfo.json'
  147. out_file2 = f'{OUT_DIR}/LLAMA_out_{id_tmp}_findings.json'
  149. folder_name = REPORT_DIR.split('/')[-1][:-7]
  151. report = f'{folder_name}/Report_ID_{id_tmp}.json'
  153. # if one of the output does not exist yet:
  154. if not os.path.isfile(out_file1) or not os.path.isfile(out_file2):
  155. data_id = read_from_tar(REPORT_DIR, report)
  157. # if data_id not found in data
  158. if not data_id:
  159. data_id = 0 # do not process this one.
  160. # ID NOT FOUND
  161. with open(out_file1, "w") as out:
  162. json.dump("REPORT NOT FOUND", out)
  163. with open(out_file2, "w") as out:
  164. json.dump("REPORT NOT FOUND", out)
  166. # if data_id found in tar
  167. elif data_id:
  168. answers = dict()
  169. # set input for model
  170. question = dict()
  171. question['Brain']="Is this report about the brain? Answer 'Yes', 'No' or 'Unknown'."
  172. question['Abnormality']="Is this a pathological report? Answer 'Yes', 'No' or 'Unknown'."
  174. question['ALL-free']= "Make a list of all mentioned pathologies in the report, start with the most clinically important one. Do not repeat pathologies. Only include pathologies which are explicitly mentioned in the report. Return a list of pathologies in JSON format. Each finding should have a 'Name', 'General Name', 'Location', 'Brain-related', 'Magnitude','Acute/Chronic' and a 'Details' field."
  176. system_message = f"You are an expert trained on neurology and clinical domain. Do only report information which is explicitely metnioed in the report."
  177. report = data_id['report']
  179. answers['report'] = report
  181. for qu in ['Brain','Abnormality']:
  182. instruction_message = f'{report} {question[qu]}'
  183. answer_tmp = ask_llama(system_message,instruction_message, pipel)
  184. answer_short = answer_tmp[0]['generated_text'].split("<|eot_id|>\n\nAssistant: ")[-1]
  185. answers[qu] = answer_short
  186. print(f'Finished question {qu}. Response: {answer_short}')
  188. # save participant_dict with scan and brain info
  189. with open(out_file1, "w") as out:
  190. json.dump(answers, out)
  193. # now ask for the actual findings in the report
  195. for qu in ['ALL-free']:
  196. instruction_message = f'{report} {question[qu]}'
  197. answer_tmp = ask_llama(system_message,instruction_message, pipel)
  198. answer_short = answer_tmp[0]['generated_text'].split("<|eot_id|>\n\nAssistant: ")[-1]
  199. findings = answer_short
  200. print(f'Finished question {qu}. Response: {findings}')
  202. findings_list = clean_json_string(findings)
  204. new_findings = []
  205. if isinstance(findings_list, list):
  206. if len(findings_list)>=1:
  207. # Second round of LLAMA to self-correct
  208. for f_tmp in findings_list:
  209. try:
  210. # ask it to self-correct
  211. instruction_message = f"{report} Is {f_tmp['General Name']} explicitly mentioned in the report? Answer Yes or No"
  212. answer_tmp = ask_llama(system_message,instruction_message, pipel)
  213. answer_short = answer_tmp[0]['generated_text'].split("<|eot_id|>\n\nAssistant: ")[-1]
  214. f_tmp['Test'] = answer_short
  215. except:
  216. f_tmp['Test'] = 'UNKNOWN'
  218. try:
  219. # remove names and dates from the term
  220. instruction_message = f"Does the following text contain names or dates? Answer Yes or No: {f_tmp['Details']} "
  221. answer_tmp = ask_llama(system_message,instruction_message, pipel)
  222. answer_short = answer_tmp[0]['generated_text'].split("<|eot_id|>\n\nAssistant: ")[-1]
  223. f_tmp['Details_did'] = answer_short
  224. except:
  225. f_tmp['Details_did'] = 'UNKNOWN'
  227. new_findings.append(f_tmp)
  229. # save participants finding with scan and brain info
  230. with open(out_file2, "w") as out:
  231. json.dump(new_findings, out)
  233. print('Done :) ')

ask_llama_open.py at commit aa4dce4, under CC-BY-NC-4.0 · at the source

Overview

Authors: Charlotte Maschke1, Peter N Hadar2, Yicheng Zhang2, Jian Li2,3, Gauri Ganjoo4, Andrew Hoopes5, Alessandro Guazzo2, Aditya Gupta2, Manohar Ghanta2, Bruce Nearing1, Christine Tsien Silvers6, Bharath Gunapati6, Robert Thomas1, Jennifer A Kim7, Shibani S Mukerji2, Adrian Dalca3,5, Sahar Zafar2, Alice D Lam2, Emmanuel Mignot4, M Brandon Westover1
ORCID iDs: Charlotte Maschke, Aditya Gupta, Emmanuel Mignot, M Brandon Westover
  1. Department of Neurology, Beth Israel Deaconess Medical Center (BIDMC), Boston, MA USA
  2. Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA USA
  3. Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA USA
  4. Stanford University, Palo Alto, USA
  5. Computer Science & Artificial Intelligence Lab, EECS, MIT, Cambridge, MA USA
  6. Amazon Web Services, Seattle, USA
  7. Yale University, New Haven, CT USA
Institutions: Beth Israel Deaconess Medical Center (United States); Harvard University (United States); Massachusetts General Hospital (United States); Athinoula A. Martinos Center for Biomedical Imaging (United States); Stanford University (United States); Massachusetts Institute of Technology (United States); Amazon (United States) (United States); Yale University (United States)
Journal: Scientific data, volume 13, issue 1, article 1176
Dates: received 15 October 2025; accepted 30 April 2026; published online 21 May 2026
Type: Data paper · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41597-026-07421-x · PMID 42168237 · PMCID PMC13462082 · OpenAlex W7161994276
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: structural MRI / diffusion (modality), EEG (modality), PET / SPECT (modality), human (organism), methods / tools (subfield)
Methods: Smoothing, state filtering, decompositions
Keywords: Neurology, Research data
MeSH: Brain*, Neuroimaging*, Electroencephalography, Humans, Large Language Models, Magnetic Resonance Imaging, Neurophysiology, Positron-Emission Tomography, Tomography, Emission-Computed, Single-Photon, Tomography, X-Ray Computed (* major topic)
Topic: Functional Brain Connectivity Studies (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: NIBIB NIH HHS (P41 EB030006, R01 EB033773, R01 EB023281); U.S. Department of Health & Human Services | National Institutes of Health (NIH) (R01AG082693, RF1NS128961, R01EB023281, R01HL161253, P41EB030006, R01NS126282, R01NS130119, R01NS131347, R01AG073410, R01AG073598, RF1NS120947); NINDS NIH HHS (R01 NS117904, R01 NS131347, UG3 NS123307, R01 NS126282, RF1 NS128961, R01 NS130119, RF1 NS120947); U.S. Department of Health & Human Services | NIH | National Institute of Mental Health (NIMH) (R01MH134823, R01MH131194); NHLBI NIH HHS (R01 HL161253); NIMH NIH HHS (R01 MH131194, R01 MH134823); U.S. Department of Health & Human Services | NIH | National Institute of Neurological Disorders and Stroke (NINDS) (UG3NS123307, R01NS117904); NIA NIH HHS (R01 AG073598, R01 AG082693, R01 AG073410, RF1 AG064312, U19 AG071754); Fondation pour la Recherche Médicale (Foundation for Medical Research in France) (SPF202409019374)
Citations: not cited yet (Europe PMC); 23 references in the paper

Abstract

The abstract is not reproduced here: the paper's license (CC BY-NC-ND) does not allow it. Read it in the paper, at the publisher or on Europe PMC.

Repository

Its files are read in the Code ↔ Paper reader above, with 4 matches between paragraphs and lines of code.

bdsp-core/BigBrainImagingDatabase

License: CC-BY-NC-4.0
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: aa4dce47f3e35c793709874a347f19605fc4f5a6, 24 May 2026
Languages: Python (2), Jupyter (1)
Size: 6 files, 3 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, license file, 1 notebook
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: NumPy (3 files), pandas (3 files), PyTorch (2 files), Hugging Face Transformers (2 files), pydicom (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
5 files
At the source: github.com/bdsp-core/BigBrainImagingDatabase

Code availability statement

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Read it in the paper: doi.org/10.1038/s41597-026-07421-x.

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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;
  • 3 scripts, each with its path and the digest of its content;
  • 4 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.

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Read it in the paper: doi.org/10.1038/s41597-026-07421-x.

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Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 20 authors, 2 keywords, 10 MeSH terms, 9 funders, 20 references.

Cite

This paper

Maschke, C., Hadar, P. N., Zhang, Y., Li, J., Ganjoo, G., Hoopes, A., Guazzo, A., Gupta, A., Ghanta, M., Nearing, B., Silvers, C. T., Gunapati, B., Thomas, R., Kim, J. A., Mukerji, S. S., Dalca, A., Zafar, S., Lam, A. D., Mignot, E., & Westover, M. B. (2026). The Brain Imaging and Neurophysiology Dataset of large-scale multimodal neural data. Scientific data, 13(1), 1176. https://doi.org/10.1038/s41597-026-07421-x

BibTeX

@article{maschke2026brain,
author = {Maschke, Charlotte and Hadar, Peter N and Zhang, Yicheng and Li, Jian and Ganjoo, Gauri and Hoopes, Andrew and Guazzo, Alessandro and Gupta, Aditya and Ghanta, Manohar and Nearing, Bruce and Silvers, Christine Tsien and Gunapati, Bharath and Thomas, Robert and Kim, Jennifer A and Mukerji, Shibani S and Dalca, Adrian and Zafar, Sahar and Lam, Alice D and Mignot, Emmanuel and Westover, M Brandon},
title = {{The Brain Imaging and Neurophysiology Dataset of large-scale multimodal neural data}},
journal = {Scientific data},
year = {2026},
month = may,
volume = {13},
number = {1},
pages = {1176},
publisher = {Nature Publishing Group},
issn = {2052-4463},
doi = {10.1038/s41597-026-07421-x},
url = {https://doi.org/10.1038/s41597-026-07421-x},
pmid = {42168237},
pmcid = {PMC13462082}
}

RIS

TY - JOUR
AU - Maschke, Charlotte
AU - Hadar, Peter N
AU - Zhang, Yicheng
AU - Li, Jian
AU - Ganjoo, Gauri
AU - Hoopes, Andrew
AU - Guazzo, Alessandro
AU - Gupta, Aditya
AU - Ghanta, Manohar
AU - Nearing, Bruce
AU - Silvers, Christine Tsien
AU - Gunapati, Bharath
AU - Thomas, Robert
AU - Kim, Jennifer A
AU - Mukerji, Shibani S
AU - Dalca, Adrian
AU - Zafar, Sahar
AU - Lam, Alice D
AU - Mignot, Emmanuel
AU - Westover, M Brandon
TI - The Brain Imaging and Neurophysiology Dataset of large-scale multimodal neural data
T2 - Scientific data
J2 - Sci Data
PY - 2026
DA - 2026/05/21
VL - 13
IS - 1
SP - 1176
SN - 2052-4463
PB - Nature Publishing Group
DO - 10.1038/s41597-026-07421-x
UR - https://doi.org/10.1038/s41597-026-07421-x
LA - en
ER -

CSL-JSON

{
"id": "10.1038/s41597-026-07421-x",
"type": "article-journal",
"title": "The Brain Imaging and Neurophysiology Dataset of large-scale multimodal neural data",
"container-title": "Scientific data",
"author": [
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"family": "Maschke",
"given": "Charlotte"
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{
"family": "Hadar",
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{
"family": "Zhang",
"given": "Yicheng"
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{
"family": "Li",
"given": "Jian"
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{
"family": "Ganjoo",
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},
{
"family": "Hoopes",
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},
{
"family": "Guazzo",
"given": "Alessandro"
},
{
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{
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{
"family": "Nearing",
"given": "Bruce"
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{
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"given": "Christine Tsien"
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{
"family": "Gunapati",
"given": "Bharath"
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{
"family": "Thomas",
"given": "Robert"
},
{
"family": "Kim",
"given": "Jennifer A"
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{
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{
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{
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"given": "Alice D"
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{
"family": "Mignot",
"given": "Emmanuel"
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{
"family": "Westover",
"given": "M Brandon"
}
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"container-title-short": "Sci Data",
"volume": "13",
"issue": "1",
"page": "1176",
"DOI": "10.1038/s41597-026-07421-x",
"PMID": "42168237",
"PMCID": "PMC13462082",
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"issued": {
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