Evidence map›Paper›PMID 40800294›Full record

ArticleImaging neuroscience (Cambridge, Mass.)2024

Spectral graph model for fMRI: A biophysical, connectivity-based generative model for the analysis of frequency-resolved resting-state fMRI.

Ashish Raj, Benjamin S Sipes, Parul Verma, Daniel H Mathalon, Bharat Biswal, Srikantan Nagarajan

Abstract read
In one paragraph

Article in Imaging neuroscience (Cambridge, Mass.), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

6 authors.

Ashish RajDepartment of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, United States.
Benjamin S SipesDepartment of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, United States.
Parul VermaDepartment of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, United States.
Daniel H MathalonDepartment of Psychiatry and Behavioral Sciences, University of California, San Francisco, and Veterans Affairs San Francisco Health Care System, San Francisco, CA, United States.
Bharat BiswalDepartment of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ, United States.
Srikantan NagarajanDepartment of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, United States.

Funding

Understanding the dynamic interactions between tau pathology and microgliamediated inflammation in Alzheimer's DiseaseR01AG072753 · NIA · WEILL MEDICAL COLL OF CORNELL UNIV · PI BUTLER, TRACY A., GUPTA, AJAY · 2021 to 2025
$5.3M
Network Modelling of Multimodal Dynamics in Alzheimer’s Disease and DementiaRF1AG062196 · NIA · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI NAGARAJAN, SRIKANTAN S., RAJ, ASHISH · 2019 to 2019
$3.0M
Multimodal modeling framework for fusing structural and functional connectome dataR01EB022717 · NIBIB · WEILL MEDICAL COLL OF CORNELL UNIV · PI NAGARAJAN, SRIKANTAN S., RAJ, ASHISH · 2016 to 2018
$1.6M
NIA NIH HHS R01 AG072753NIA NIH HHS RF1 AG062196NIBIB NIH HHS R01 EB022717
6 · The paper itself

Abstract

Resting-state functional MRI (rs-fMRI) is a popular and widely used technique to explore the brain's functional organization and to examine whether it is altered in neurological or mental disorders. The most common approach for its analysis targets the measurement of the synchronized fluctuations between brain regions, characterized as functional connectivity (FC), typically relying on pairwise correlations in activity across different brain regions. While hugely successful in exploring state- and disease-dependent network alterations, these statistical graph theory tools suffer from two key limitations. First, they discard useful information about the rich frequency content of the fMRI signal. The rich spectral information now achievable from advances in fast multiband acquisitions is consequently being underutilized. Second, the analyzed FCs are phenomenological without a direct neurobiological underpinning in the underlying structures and processes in the brain. There does not currently exist a complete generative model framework for whole brain resting fMRI that is informed by its underlying biological basis in the structural connectome. Here we propose that a different approach can solve both challenges at once: the use of an appropriately realistic yet parsimonious biophysics-informed signal generation model followed by graph spectral (i.e., eigen) decomposition. We call this model a spectral graph model (SGM) for fMRI, using which we can not only quantify the structure-function relationship in individual subjects, but also condense the variable and individual-specific repertoire of fMRI signal's spectral and spatial features into a small number of biophysically interpretable parameters. We expect this model-based analysis of rs-fMRI that seamlessly integrates with structure can be used to examine state and trait characteristics of structure-function relationships in a variety of brain disorders.

Indexed as

fMRIfunctional networksgraph harmonicsgraph Laplacianspectral graph theorystructural connectivity

Identifiers

PMID40800294
PMCPMC12315728

What OpenQuestion holds

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.