A Multimodal Dataset to Investigate Task-Evoked Negative BOLD Response and Neurodegeneration.

Overview

1. Department of Radiology, Weill Cornell Medicine Brain Health Imaging Institute, Quantitative Neuroimaging Laboratory, New York, NY USA
2. Department of Radiology, Weill Cornell Medicine, Brain Health Imaging Institute, New York, NY USA

Abstract

The Quantitative Neuroimaging Laboratory Dataset provides magnetic resonance imaging (MRI) modalities and two resting-state and twelve task-based functional MRI (fMRI) tapping into four cognitive domains (episodic memory, fluid reasoning, processing speed, and crystallized memory). It also includes three positron emission tomography (PET) scans ([18 F]Fluorodeoxyglucose (FDG), Florbetaben, and MK-6240), plus neuropsychological assessments, and vital signs. Currently, 356 participants consented (97 young: 20 ~ 40 years; and 259 elderly: 60 ~ 80 years), while 259 completed at least one scan. We uploaded 4688 MRI/fMRI and 719 PET scans (232 Florbetaben, 251 FDG, and 236 MK-6240). 189 participants completed all scan modalities. All imaging underwent an in-house, pre-processing pipeline developed for each modality. This dataset aims to characterize the spatial and temporal properties of the brain’s hemodynamic response in the opposite direction (i.e., brain deactivation), its task dependency, and its interaction with the brain’s large-scale functional connectivity networks. Ultimately, this will enable the translation of neuroimaging findings into personalized medicine approaches that better characterize and predict individual pathologies in neuropsychiatric diseases.

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

Code

No file of the authors' code could be read here: it is described below, and read at its source.

Code availability

All custom codes are already shared on GitHub, and future development will be shared on our GitHub lab website (https://github.com/QuantitativeNeuroimagingLaboratory). The project’s main README file will also include instructions and parameter choices for the execution of these codes.

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

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Data

Datasets cited

• doi:10.18112/openneuro.ds006148.v1.0.5, at OpenNeuro; found in “Data availability”
• openneuro:ds006148, at OpenNeuro; found in “Data availability”

Data availability

The dataset is currently available at the OpenNeuro and is named QNL NegativeBOLD Dataset (reference number: ds006148 (https://openneuro.org/datasets/ds006148/versions), 10.18112/openneuro.ds006148.v1.0.5)106. Anonymized imaging and cognitive data can be downloaded in the BIDS format. Each participant is assigned a subject ID with a similar format (e.g., sub-C00321 (https://www.ncbi.nlm.nih.gov/nuccore/C00321)), and they may have up to three subfolders (i.e., ses-S001, ses-S002, and ses-S003) corresponding to sessions one, two, and three. Each session has subfolders titled as; “anat” (structural MRI), “func” (fMRI), “perf” (ASL images), “pet” (PET scans), and dwi (dMRI images). The images are in NIfTI format, and each has technical information in “.json” format106. Due to privacy concerns, the demographics are only available as a controlled-access dataset. To gain access to the demographic dataset, researchers are instructed to download the Data Usage Agreement (DUA) under the “file-display” directory of the dataset, fill it in and have it signed by their institution’s authorities and submit for approval from the WCM.

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

De-identification, dataset, and sharing

Each participant who passed the initial screening and made their first appointment was assigned a participant ID number. This ID eliminated the need for identifying information (name, birth date, social security number) in upcoming data gatherings (clinical assessments, questionnaires, psychometric data, or neuroimaging data).

Original neuroimaging data were saved on the Quantitative Neuroimaging Lab (QNL) Digital Imaging and Communications in Medicine (DICOM) server. All neuroimaging data were anonymized with the assigned ID, converted to Neuroimaging Informatics Technology Initiative (NIfTI) format, and saved on three petabytes of QNL storage.

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

Versions

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Version 1, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 7 authors, 5 keywords, 5 MeSH terms, 6 funders, 80 references.

Cite

This paper

Ghaderi Yazdi, B., Ozoria, S., Hojjati, S. H., Chernek, P., Calimag, J., Wang, X. H., & Razlighi, Q. R. (2026). A Multimodal Dataset to Investigate Task-Evoked Negative BOLD Response and Neurodegeneration. Scientific data, 13(1), 744. https://doi.org/10.1038/s41597-026-07081-x

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