Evidence map›Paper›PMID 36772649›Full record

ArticleSensors (Basel, Switzerland)2023

Age Related Functional Connectivity Signature Extraction Using Energy-Based Machine Learning Techniques.

Sravani Varanasi, Roopan Tuli, Fei Han, Rong Chen, Fow-Sen Choa

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2023. 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

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

2 citing papers in PubMed.

  1. Article
  2. 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

5 authors.

Sravani VaranasiDepartment of Electrical Engineering and Computer Science, University of Maryland Baltimore County, Baltimore, MD 21250, USA.ORCID 0000-0002-8862-0049
Roopan TuliDepartment of Electrical Engineering, Santa Clara University, Santa Clara, CA 95053, USA.
Fei HanThe Hilltop Institute, University of Maryland Baltimore County, Baltimore, MD 21250, USA.ORCID 0000-0003-2454-4187
Rong ChenDepartment of Diagnostic Radiology and Nuclear Medicine, University of Maryland Baltimore, Baltimore, MD 21201, USA.
Fow-Sen ChoaDepartment of Electrical Engineering and Computer Science, University of Maryland Baltimore County, Baltimore, MD 21250, USA.ORCID 0000-0001-9613-6110

Funding

Dissemination of Real-time Neural Decoding for Cellular imaging LaboratoryR01NS110421 · NINDS · UNIVERSITY OF MARYLAND BALTIMORE · PI BHATTACHARYYA, SHUVRA S, CHEN, RONG · 2018 to 2022
$860k
NINDS NIH HHS R01 NS110421NINDS NIH HHS R01-NS110421
6 · The paper itself

Abstract

The study of brain connectivity plays an important role in understanding the functional organizations of the brain. It also helps to identify connectivity signatures that can be used for evaluating neural disorders and monitoring treatment efficacy. In this work, age-related changes in brain connectivity are studied to obtain aging signatures based on various modeling techniques. These include an energy-based machine learning technique to identify brain network interaction differences between two age groups with a large (30 years) age gap between them. Disconnectivity graphs and activation maps of the seven prominent resting-state networks (RSN) were obtained from functional MRI data of old and young adult subjects. Two-sample

Indexed as

AgingBrainAdultBrain MappingHumansMachine LearningMagnetic Resonance ImagingNeural PathwaysYoung Adultbrain connectivityenergy landscapefMRImachine learningresting state network

Identifiers

PMID36772649
PMCPMC9920122

What OpenQuestion holds

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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.