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Blood volume-sensitive laminar fMRI with VASO in human hippocampus: Capabilities and biophysical challenges at clinical 7T scanners.

Code ↔ Paper

9 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 9 matches · 1 of them tie a paragraph to a whole file, not to given lines: a weak match, whose lines are not tinted
  1. [1] § Methods › Data analysis › Functional data preprocessing ↔ preproc_seg/Pipeline.sh, lines 1–39 · score 0.94 · outermost edge, reversed phase, co registered functional, acquisition slab, distortion corrected, preprocessed
  2. [2] § Methods › Data analysis › Anatomical data processing ↔ preproc_seg/Pipeline.sh, lines 495–583 · score 0.91 · skull stripped, FSL FAST, MP2RAGE, BET, Singularity, tissue
  3. [3] § Methods › Data analysis › General linear model (GLM) analysis ↔ preproc_seg/Pipeline.sh, lines 495–583 · score 0.87 · ANTs multivariate template, aCompCor, GM mask, functional slab, inference, cropped
  4. [4] § Results › Part 2: Sequence validation with an autobiographical memory paradigm › Disentangling brain activations between memory and math trials ↔ voxelwise_glm/sensitivity_analysis/sensitivity.R, lines 1–24 · score 0.72 · minimum detectable, sample Cohen, standardized MDES, sensitivity, power
  5. [5] § Methods › Data analysis › General linear model (GLM) analysis ↔ voxelwise_glm/subfield_activity_contrast_reg.sh, lines 113–186 · score 0.68 · GM mask, functional slab, inference, ANTs, cropped, template
  6. [6] § Methods › Data analysis › Extraction of laminar profiles in HC subfields ↔ layering/VPF_create_hippocampus_layers.m, lines 1–138 · score 0.61 · inner surface, equidistant, landmark, MATLAB, boundary, CA4
  7. [7] § Results › Part 2: Sequence validation with an autobiographical memory paradigm › Disentangling brain activations between memory and math trials ↔ Figurs_script/subfield_activity_plots.R, lines 1–12 · score 0.57 · entire anatomically defined, hippocampal subfields, conjunction, ROIs, voxels, memory
  8. [8] § Results › Part 2: Sequence validation with an autobiographical memory paradigm › Laminar profiles of HC subfields for memory vs math trials ↔ layering/SLopes_significance_assessment/sig_assessment.R, lines 114–150 · score 0.52 · random intercepts, fitted, interaction, mixed, profiles, model
  9. [9] § Results › Part 2: Sequence validation with an autobiographical memory paradigm › Disentangling brain activations between memory and math trials ↔ Figurs_script/Contrast_to_noise_ratio/CNR.sh, the whole file · a weak match · score 0.51 · ResMS.nii, noise ratio, CNR, HC, VASO

Paper

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

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The authors' code

Shell · 583 lines · 29 KB · no license · 3 matches

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It can be read at the source: preproc_seg/Pipeline.sh.

Overview

Authors: Khazar Ahmadi1,2, Stephanie Swegle2, Sriranga Kashyap3, Antoine Bouyeure1, Peter Bandettini2, Nikolai Axmacher1, Laurentius Huber2,4
ORCID iDs: Khazar Ahmadi, Antoine Bouyeure, Nikolai Axmacher
  1. Department of Neuropsychology, Institute of Cognitive Neuroscience, Faculty of Psychology, Ruhr University Bochum, Bochum, Germany
  2. National Institutes of Health, Bethesda, MD, United States
  3. Krembil Brain Institute, University Health Network, Toronto, ON, Canada
  4. Martinos Center, MGH, Harvard Medical School, Charlestown, MA, United States
Institutions: National Institutes of Health (United States); Ruhr University Bochum (Germany); University Health Network (Canada); Krembil Brain Institute; Harvard University (United States); Massachusetts General Hospital (United States)
Journal: Imaging neuroscience (Cambridge, Mass.), volume 4, article IMAG.a.1197
Dates: received 31 August 2025; accepted 23 February 2026; published online 9 April 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1162/imag.a.1197 · PMID 41970695 · PMCID PMC13069395 · OpenAlex W7136342614
Open access: diamond, a free copy (OpenAlex)
Status: code verified
Categories: fMRI (modality), human (organism), systems (subfield)
Methods: Smoothing, state filtering, decompositions, Statistics, fMRI & imaging, Spectral & time-frequency
Keywords: laminar fMRI, VASO contrast, hippocampus, memory, ultra-high field
Topic: Functional Brain Connectivity Studies (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: European Research Council (864164)
Citations: cited by 2 papers (Europe PMC); 75 references in the paper

Abstract

Sub-millimeter resolution functional magnetic resonance imaging (fMRI) at ultra-high field (≥ 7T) has offered an unprecedented opportunity to probe mesoscopic computations at a columnar or laminar level. However, its application has been primarily restricted to the neocortex. Inferior brain regions, particularly the hippocampus (HC), are challenging targets for laminar fMRI. Recent developments in acquisition methods have shown the feasibility of laminar recordings in the HC using gradient-echo blood oxygenation level-dependent (BOLD) contrast. Nonetheless, the spatial specificity of the BOLD signal is compromised by the draining veins’ bias. Cerebral blood volume (CBV)-sensitive sequences including vascular space occupancy (VASO) have emerged as a promising approach to capture the laminar activity with mitigated venous bias. Yet, its feasibility in the HC is unclear and challenged by methodological constraints. Here, we optimized VASO to mitigate the macrovasculature contribution in HC. By evaluating a series of advanced acquisition strategies tailored to HC, we obtained improved VASO signal quality with minimal artifacts. The optimized protocol was further validated with an autobiographical memory task. Our findings show that combining the high detection power of gradient-echo BOLD with the vein-bias-mitigated VASO contrast allows for differentiation between neural activity-related BOLD signals and those biased by draining veins. These results demonstrate the feasibility of submillimeter VASO acquired with conventional 7T scanners in the HC to map the circuit-level mechanisms of memory retrieval across HC subfields, laying a foundation to investigate the microcircuitry of HC-driven complex cognitive functions and their alterations in neurodegeneration and epilepsy.

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 9 matches between paragraphs and lines of code.

gitlab.ruhr-uni-bochum.de/neuropsy/vaso_hc

License: none: the authors keep all their rights
State: the link answers, verified on 29 September 2026
Evidence: files inventoried
Commit: a4ef8f28dd92d233e86c4ece66c7f4708d8bd740, 16 January 2026
Languages: MATLAB (13), R (6), Shell (4)
Size: 87 files, 23 scripts
Software Heritage: not archived
Found in: “Data and Code Availability”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: FSL (4 files), ggplot2 (4 files), ANTs (3 files), SPM (3 files), nlme (2 files), AFNI (1 file), emmeans (1 file), GIfTI library for MATLAB (1 file), Image Processing Toolbox (1 file), Tools for NIfTI and ANALYZE image (MATLAB) (1 file), tidyverse (1 file)
Availability: 1 check, the latest on 29 September 2026: the link answers
  • 29 September 2026: the link answers
24 files, not copied: shown from their source

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At the source: gitlab.ruhr-uni-bochum.de/neuropsy/vaso_hc

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:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 23 scripts, each with its path and the digest of its content;
  • 9 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

Datasets cited

Data and Code Availability

Anonymized data have been deposited on Zenodo: https://zenodo.org/records/16032692. All original codes are provided in this GitLab repository: https://gitlab.ruhr-uni-bochum.de/neuropsy/vaso_hc.

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

Versions

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Version 1, 29 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 7 authors, 5 keywords, 1 funder, 72 references.

Cite

This paper

Ahmadi, K., Swegle, S., Kashyap, S., Bouyeure, A., Bandettini, P., Axmacher, N., & Huber, L. (2026). Blood volume-sensitive laminar fMRI with VASO in human hippocampus: Capabilities and biophysical challenges at clinical 7T scanners. Imaging neuroscience (Cambridge, Mass.), 4, IMAG.a.1197. https://doi.org/10.1162/imag.a.1197

BibTeX

@article{ahmadi2026blood,
author = {Ahmadi, Khazar and Swegle, Stephanie and Kashyap, Sriranga and Bouyeure, Antoine and Bandettini, Peter and Axmacher, Nikolai and Huber, Laurentius},
title = {{Blood volume-sensitive laminar fMRI with VASO in human hippocampus: Capabilities and biophysical challenges at clinical 7T scanners}},
journal = {Imaging neuroscience (Cambridge, Mass.)},
year = {2026},
month = apr,
volume = {4},
pages = {IMAG.a.1197},
publisher = {MIT Press},
issn = {2837-6056},
doi = {10.1162/imag.a.1197},
url = {https://doi.org/10.1162/imag.a.1197},
pmid = {41970695},
pmcid = {PMC13069395}
}

RIS

TY - JOUR
AU - Ahmadi, Khazar
AU - Swegle, Stephanie
AU - Kashyap, Sriranga
AU - Bouyeure, Antoine
AU - Bandettini, Peter
AU - Axmacher, Nikolai
AU - Huber, Laurentius
TI - Blood volume-sensitive laminar fMRI with VASO in human hippocampus: Capabilities and biophysical challenges at clinical 7T scanners
T2 - Imaging neuroscience (Cambridge, Mass.)
J2 - Imaging Neurosci (Camb)
PY - 2026
DA - 2026/04/09
VL - 4
SP - IMAG.a.1197
SN - 2837-6056
PB - MIT Press
DO - 10.1162/imag.a.1197
UR - https://doi.org/10.1162/imag.a.1197
LA - en
ER -

CSL-JSON

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