Evidence map›Paper›PMID 39157289›Full record

ArticleFrontiers in systems neuroscience2024

Variation in the distribution of large-scale spatiotemporal patterns of activity across brain states.

Lisa Meyer-Baese, Nmachi Anumba, T Bolt, L Daley, T J LaGrow, Xiaodi Zhang, Nan Xu, Wen-Ju Pan, E H Schumacher, Shella Keilholz

Abstract read
In one paragraph

Article in Frontiers in systems neuroscience, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Spatiotemporal Network Dynamics Reveal Alzheimer's Disease Progression.bioRxiv : the preprint server for biology · 2025
    Article
  5. Article
  6. Article
  7. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

10 authors.

Lisa Meyer-Baese *Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Emory University, Atlanta, GA, United States.
Nmachi Anumba *Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Emory University, Atlanta, GA, United States.
T BoltWallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Emory University, Atlanta, GA, United States.
L DaleyWallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Emory University, Atlanta, GA, United States.
T J LaGrowElectrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, United States.
Xiaodi ZhangWallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Emory University, Atlanta, GA, United States.
Nan XuWallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Emory University, Atlanta, GA, United States.
Wen-Ju PanWallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Emory University, Atlanta, GA, United States.
E H SchumacherPsychology, Georgia Institute of Technology, Atlanta, GA, United States.
Shella KeilholzWallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Emory University, Atlanta, GA, United States.

Funding

Contribution of Ultra Low Frequency LFPs to Functional MRIR01NS078095 · NINDS · EMORY UNIVERSITY · PI KEILHOLZ, SHELLA D · 2012 to 2022
$3.4M
Impact of locus coeruleus-derived tau pathology in a rodent model of early Alzheimer's diseaseR01AG062581 · NIA · EMORY UNIVERSITY · PI KEILHOLZ, SHELLA D, WEINSHENKER, DAVID · 2020 to 2024
$2.3M
Spatiotemporal signatures of neural activity and neurophysiology in the BOLD signalR01MH111416 · NIMH · EMORY UNIVERSITY · PI KEILHOLZ, SHELLA D · 2016 to 2020
$1.9M
Time-varying spatiotemporal causal interactions in the functional brain networksK99NS123113 · NINDS · GEORGIA INSTITUTE OF TECHNOLOGY · PI XU, NAN · 2023 to 2024
$184k
NIA NIH HHS R01 AG062581NIMH NIH HHS R01 MH111416NINDS NIH HHS K99 NS123113NINDS NIH HHS R01 NS078095
6 · The paper itself

Abstract

A few large-scale spatiotemporal patterns of brain activity (quasiperiodic patterns or QPPs) account for most of the spatial structure observed in resting state functional magnetic resonance imaging (rs-fMRI). The QPPs capture well-known features such as the evolution of the global signal and the alternating dominance of the default mode and task positive networks. These widespread patterns of activity have plausible ties to neuromodulatory input that mediates changes in nonlocalized processes, including arousal and attention. To determine whether QPPs exhibit variations across brain conditions, the relative magnitude and distribution of the three strongest QPPs were examined in two scenarios. First, in data from the Human Connectome Project, the relative incidence and magnitude of the QPPs was examined over the course of the scan, under the hypothesis that increasing drowsiness would shift the expression of the QPPs over time. Second, using rs-fMRI in rats obtained with a novel approach that minimizes noise, the relative incidence and magnitude of the QPPs was examined under three different anesthetic conditions expected to create distinct types of brain activity. The results indicate that both the distribution of QPPs and their magnitude changes with brain state, evidence of the sensitivity of these large-scale patterns to widespread changes linked to alterations in brain conditions.

Indexed as

brain statedynamic functional connectivityfunctional connectivityresting state fMRIspatiotemporal dynamic analysis

Identifiers

PMID39157289
PMCPMC11327057

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Registered trials

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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.