Evidence map›Paper›PMID 37699440›Full record

ArticleNeuroImage2023

Dynamic functional connectivity MEG features of Alzheimer's disease.

Huaqing Jin, Kamalini G Ranasinghe, Pooja Prabhu, Corby Dale, Yijing Gao, Kiwamu Kudo, Keith Vossel, Ashish Raj, Srikantan S Nagarajan, Fei Jiang

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in NeuroImage, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
1.9field-weighted citation impact, top 14% of its field
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

5 citing papers in PubMed, 11 citations in OpenAlex.

  1. Article
  2. Article
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  4. Review
  5. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors at 3 institutions in 2 countries.

Huaqing JinDepartment of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA.
Kamalini G RanasingheDepartment of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA; Memory and Aging Center, University of California San Francisco, San Francisco, CA, USA.
Pooja PrabhuDepartment of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA.
Corby DaleDepartment of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA.
Yijing GaoDepartment of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA.
Kiwamu KudoMedical Imaging Business Center, Ricoh Company, Ltd., Kanazawa, 920-0177, Japan.
Keith VosselDepartment of Neurology, University of California Los Angeles, Los Angeles, CA, USA.
Ashish RajDepartment of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA.
Srikantan S NagarajanDepartment of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA. Electronic address: sri@ucsf.edu.
Fei JiangDepartment of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, CA, USA. Electronic address: fei.jiang@ucsf.edu.
University of California, San Francisco · USRicoh (Japan) · JPUniversity of California, Los Angeles · US

Funding

Understanding disorder-specific neural pathophysiology in laryngeal dystonia and voice tremorP50DC019900 · NIDCD · MASSACHUSETTS EYE AND EAR INFIRMARY · PI Kristina Simonyan · 2021 to 2026
$11.9M
The role of the cerebellum in speechR01DC017091 · NIDCD · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI John Francis Houde, RICHARD IVRY · 2019 to 2026
$4.5M
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
Dynamic Brain Imaging of Speech in Primary Progressive AphasiaRF1NS100440 · NINDS · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI GORNO TEMPINI, MARIA LUISA, HOUDE, JOHN FRANCIS · 2023 to 2023
$2.4M
Neurophysiological Assessments of Network Dysfunction in Alzheimer's DiseaseK08AG058749 · NIA · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI RANASINGHE, KAMALINI GAYATHREE · 2019 to 2023
$859k
Development of Dynamic Resting State Functional Connectivity Machine Learning Framework for DementiaK25AG071840 · NIA · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI fei jiang · 2022 to 2026
$701k
Multimodal imaging measures to assess synaptic dysfunction in Alzheimer's diseaseR21AG077498 · NIA · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI RANASINGHE, KAMALINI GAYATHREE · 2022 to 2022
$484k
NIA NIH HHS K08 AG058749NIA NIH HHS K25 AG071840NIA NIH HHS R21 AG077498NIA NIH HHS RF1 AG062196NIDCD NIH HHS P50 DC019900NIDCD NIH HHS R01 DC017091NINDS NIH HHS RF1 NS100440
6 · The paper itself

Abstract

Dynamic resting state functional connectivity (RSFC) characterizes time-varying fluctuations of functional brain network activity. While many studies have investigated static functional connectivity, it has been unclear whether features of dynamic functional connectivity are associated with neurodegenerative diseases. Popular sliding-window and clustering methods for extracting dynamic RSFC have various limitations that prevent extracting reliable features to address this question. Here, we use a novel and robust time-varying dynamic network (TVDN) approach to extract the dynamic RSFC features from high resolution magnetoencephalography (MEG) data of participants with Alzheimer's disease (AD) and matched controls. The TVDN algorithm automatically and adaptively learns the low-dimensional spatiotemporal manifold of dynamic RSFC and detects dynamic state transitions in data. We show that amongst all the functional features we investigated, the dynamic manifold features are the most predictive of AD. These include: the temporal complexity of the brain network, given by the number of state transitions and their dwell times, and the spatial complexity of the brain network, given by the number of eigenmodes. These dynamic features have higher sensitivity and specificity in distinguishing AD from healthy subjects than the existing benchmarks do. Intriguingly, we found that AD patients generally have higher spatial complexity but lower temporal complexity compared with healthy controls. We also show that graph theoretic metrics of dynamic component of TVDN are significantly different in AD versus controls, while static graph metrics are not statistically different. These results indicate that dynamic RSFC features are impacted in neurodegenerative disease like Alzheimer's disease, and may be crucial to understanding the pathophysiological trajectory of these diseases.

Indexed as

Alzheimer DiseaseNeurodegenerative DiseasesBrainHumansMagnetic Resonance ImagingMagnetoencephalographyAlzheimer’s diseaseBrain state switchDementiaDynamic resting stateFunctional connectivityFunctional magnetic resonanceMagnetoencephalographyMulti-modality imaging

Identifiers

PMID37699440
PMCPMC10865998
OpenAlexW4386606476

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.