NeuroMark dFNC and Time–Frequency Moment Templates for Biomarker Discovery
Dynamic functional network connectivity (dFNC) and time–frequency moment (TFM) templates derived from multiple large-scale neuroimaging datasets, including more than 100,000 subjects. These templates provide reference patterns for biomarker discovery and benchmarking in schizophrenia (SZ), Alzheimer’s disease (AD), and other neurological and psychiatric disorders.
The templates were developed by Moein Esfahani and collaborators and are available in NIfTI format.
dFNC templates (a json sidecar may be found at source location for additional description):
tpl-NeuromarkdFNCICA10_connectivity.nii
tpl-NeuromarkdFNCKMEANS10_connectivity.nii
tpl-NeuromarkdFNCPCA10_connectivity.nii
TFM templates (a json sidecar may be found at source location for additional description):
tpl-NeuromarkTFMICA10_connectivity.nii
tpl-NeuromarkTFMKMEANS10_connectivity.nii
tpl-NeuromarkTFMPCA10_connectivity.nii
Reference: In press (Esfahani et al., 2026)
Previous similar study with fewer subjects: Esfahani, M., Esaulov, V., Venkateswara, H., & Calhoun, V. (2024). Neuromark dfnc patterns: A fully automated pipeline to estimate subject-specific states from rs-fmri data via constrained ica of dfnc in+ 100k subjects. bioRxiv. https://doi.org/10.1101/2025.01.29.635539
Cellular and molecular Neuroimaging Maps Released April, 2025, Updated Sept. 2025.
Cellular and molecular maps encompassing the 24 cell types inferred in study below are downloadable as NIfTI files by clicking here.
Reference:
Filtered Sliding Window Correlation. Please see Victor M. Vergara, PhD and Vince D Calhoun, PhD, “Filtered Correlation and Allowed Frequency Spectra in Dynamic Functional Connectivity”, Journal of Neuroscience Methods, July 2020.
Large ICA networks used in paper A. Iraji et al., “The spatial chronnectome reveals a dynamic interplay between functional segregation and integration”, HBM, 2019.
Autism Aggregate ICA map and labels used in paper:
Z. Fu, Y. Tu, X. Di, Y. Du, J. Sui, B. B. Biswal, Z. Zhang, N. de Lacy, V. D. Calhoun, “Transient increased thalamic-sensory connectivity and decreased whole-brain dynamism in autism”, Neuroimage 190, 2019, 191-204
Resting State Aggregate ICA maps used in papers:
- B. Rashid, E. Damaraju, G. D. Pearlson and V. D. Calhoun, “Dynamic connectivity states estimated from resting fMRI Identify differences among Schizophrenia, bipolar disorder, and healthy control subjects”, Front Hum Neurosci. 2014; 8: 897.
- B. Rashid, M. Arabshirani, E. Damaraju, M. Cetin, R. Miller, G. D. Pearlson and V. D. Calhoun, “Classification of schizophrenia and bipolar patients using static and dynamic resting-state fMRI brain connectivity”, NeuroImage, Volume 134, 1 July 2016, Pages 645-657.
Data visualization in the neurosciences: overcoming the curse of dimensionality. Click here for more information
- E. A. Allen, E. B. Erhardt, V. D. Calhoun. “Data visualization in the neurosciences: overcoming the curse of dimensionality”, Neuron, 74(4), 603-608, 2012.
Resting state data from E. Allen, E. Damaraju, S. M. Plis, E. Erhardt, T. Eichele, and V. D.Calhoun, “Tracking whole-brain connectivity dynamics in the resting state”, Cereb Cortex, Nov 2012.
T-maps of all 100 components See Component Labels
Composite T maps of 7 groups resampled to 1mm^3 space and their labels.
Mean ICA components from Stevens, V. D. Calhoun, G. D. Pearlson, and K. A. Kiehl, “Brain network dynamics during error commission,” Hum.Brain Map., vol. 30, pp. 24-37, 2009