---
title: "NeuroMark dFNC: Transferable Templates for Subject-Specific Dynamic Connectivity"
id: "biorxiv-6-neuromark-dfnc-transferable-dynamic-state-templates-for-subject-specific-and"
canonical_url: "https://medichelpline.com/clinical-feed/biorxiv-6-neuromark-dfnc-transferable-dynamic-state-templates-for-subject-specific-and"
content_type: "clinical_feed_article"
specialty: "Neurology"
source_name: "bioRxiv (Biomedical Preprints)"
source_url: "https://www.biorxiv.org/content/10.64898/2026.10.12.758630v1?rss=1"
published_at: "2026-10-17T12:00:00.000Z"
evidence_level: "Verified Feed"
license: "CC-BY-NC-4.0 / Informational Use"
---
# NeuroMark dFNC: Transferable Templates for Subject-Specific Dynamic Connectivity
## Provenance & Clinical Metadata
- **Canonical URL:** https://medichelpline.com/clinical-feed/biorxiv-6-neuromark-dfnc-transferable-dynamic-state-templates-for-subject-specific-and
- **Specialty:** [Neurology](https://medichelpline.com/clinical-feed/neurology.md)
- **Primary Source:** bioRxiv (Biomedical Preprints)
- **Source URL:** [Original Journal Publication](https://www.biorxiv.org/content/10.64898/2026.10.12.758630v1?rss=1)
- **Published At:** 2026-10-17T12:00:00.000Z
- **Evidence Rating:** Verified Feed
## Executive GIST (TL;DR)
- The study introduces **NeuroMark dFNC**, a prior-guided pipeline that transfers reproducible dynamic connectivity state templates into new subjects rather than deriving states de novo. - Reference-state templates were generated from the **UK Biobank** using windowed functional network connectivity and time-frequency moments (TFMs); split-sample reproducibility was evaluated for k-means, ICA, and PCA approaches. - Constrained **ICA** then used those templates to estimate subject-specific state patterns and continuous loadings in independent clinical cohorts (FBIRN schizophrenia and ADNI Alzheimer’s disease), preserving individual connectivity expression. - The authors calibrated ICA loadings by their contribution to reconstructing the original feature matrix, then assigned the maximally contributing state at each timepoint to compute fractional occupancy and conditional mean dwell time. - Using predefined templates removed the need for dataset-specific state discovery and post-hoc state matching, enabling a common state space for cross-cohort comparison while retaining subject-level pattern detail. - In both clinical cohorts the pipeline identified state-dependent, distributed edgewise differences and alterations in selected temporal summaries; frequency-resolved results were reported as exploratory and variable across states and moment-frequency bins. - The article is a preprint and has not been peer-reviewed; the authors reported no competing interests. Data and code links and funding sources were provided in the source.
## Clinical Analysis & Structured Key Points
NeuroMark dFNC: Transferable Dynamic-State Templates for Subject-Specific and Frequency-Resolved Chronnectomics | bioRxiv Skip to main content New Results NeuroMark dFNC: Transferable Dynamic-State Templates for Subject-Specific and Frequency-Resolved Chronnectomics View ORCID Profile M. Moein Esfahani , View ORCID Profile Masoud Seraji , View ORCID Profile Sepehr Salem , View ORCID Profile Kyle M. Jensen , View ORCID Profile Zening Fu , View ORCID Profile Tulay Adali , View ORCID Profile Yu-Ping Wang , View ORCID Profile Vince Calhoun doi: https://doi.org/10.64898/2026.10.12.758630 M. Moein Esfahani 1 Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State, Georgia Tech, Emory University, Atlanta, GA, USA; Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for M. Moein Esfahani For correspondence: mesfahani1{at}gsu.edu Masoud Seraji 2 Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State, Georgia Tech, Emory University, Atlanta, GA, USA, Department of Psychology, University of Texas at Austin, Austin, USA; Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Masoud Seraji Sepehr Salem 3 Georgia State University, Atlanta, GA, USA; Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Sepehr Salem Kyle M. Jensen 1 Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State, Georgia Tech, Emory University, Atlanta, GA, USA; Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Kyle M. Jensen Zening Fu 1 Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State, Georgia Tech, Emory University, Atlanta, GA, USA; Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Zening Fu Tulay Adali 4 Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore County, Baltimore, MD, USA; Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Tulay Adali Yu-Ping Wang 5 Department of Biomedical Engineering, Tulane University, New Orleans, LA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Yu-Ping Wang Vince Calhoun 1 Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State, Georgia Tech, Emory University, Atlanta, GA, USA; Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Vince Calhoun Abstract Info/History Metrics Data/Code Preview PDF Abstract Dynamic functional network connectivity (dFNC) commonly identifies recurring connectivity states from resting-state fMRI, but states derived independently in each dataset lack guaranteed correspondence across studies and cannot be directly estimated in new participants. We introduce NeuroMark dFNC, which extends the NeuroMark principle of prior-guided network estimation to dynamic connectivity by using reproducible reference states as priors for estimating corresponding subject-specific states. Reference-state templates were derived from UK Biobank using windowed functional network connectivity and time-frequency moments (TFMs), with split-sample reproducibility evaluated for k-means, independent component analysis (ICA), and principal component analysis (PCA) template-generation approaches. Constrained ICA then used the templates to estimate subject-specific state patterns and continuous loadings in independent FBIRN schizophrenia and ADNI Alzheimer's disease cohorts. ICA loadings were calibrated by their contribution to reconstruction of the original feature matrix. Fractional occupancy and conditional mean dwell time were calculated from the state with the largest absolute calibrated loading at each observation. The predefined templates avoided the need for dataset-specific state discovery and post-hoc state matching. In both clinical cohorts, the analysis identified state-dependent, distributed edgewise differences and differences in selected temporal summaries. Nominal frequency-resolved findings varied across states and moment-frequency bins and were treated as exploratory. NeuroMark dFNC provides a common state space for cross-cohort comparison while retaining subject-specific connectivity patterns and continuous state expression. Competing Interest Statement The authors have declared no competing interest. Footnotes https://trendscenter.org/data/ Funder Information Declared National Institutes of Health, https://ror.org/01cwqze88 , R01EB006841 National Institutes of Health, https://ror.org/01cwqze88 , R01MH123610 U.S. National Science Foundation, https://ror.org/021nxhr62 , 2316421 U.S. National Science Foundation, https://ror.org/021nxhr62 , 2112455 National Institute on Aging, https://ror.org/049v75w11 , U19AG024904 Copyright The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license . Back to top Previous Next Posted October 17, 2026. Download PDF Data/Code Email Thank you for your interest in spreading the word about bioRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. Your Email * Your Name * Send To * Enter multiple addresses on separate lines or separate them with commas. You are going to email the following NeuroMark dFNC: Transferable Dynamic-State Templates for Subject-Specific and Frequency-Resolved Chronnectomics Message Subject (Your Name) has forwarded a page to you from bioRxiv Message Body (Your Name) thought you would like to see this page from the bioRxiv website. Your Personal Message CAPTCHA This question is for testing whether or not you are a human visitor and to prevent automated spam submissions. Share NeuroMark dFNC: Transferable Dynamic-State Templates for Subject-Specific and Frequency-Resolved Chronnectomics M. Moein Esfahani , Masoud Seraji , Sepehr Salem , Kyle M. Jensen , Zening Fu , Tulay Adali , Yu-Ping Wang , Vince Calhoun bioRxiv 2026.10.12.758630; doi: https://doi.org/10.64898/2026.10.12.758630 Share This Article: Copy Citation Tools NeuroMark dFNC: Transferable Dynamic-State Templates for Subject-Specific and Frequency-Resolved Chronnectomics M. Moein Esfahani , Masoud Seraji , Sepehr Salem , Kyle M. 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