Cargando…

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

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 connect...

Descripción completa

Detalles Bibliográficos
Autores principales: Varanasi, Sravani, Tuli, Roopan, Han, Fei, Chen, Rong, Choa, Fow-Sen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9920122/
https://www.ncbi.nlm.nih.gov/pubmed/36772649
http://dx.doi.org/10.3390/s23031603
_version_ 1784886992896524288
author Varanasi, Sravani
Tuli, Roopan
Han, Fei
Chen, Rong
Choa, Fow-Sen
author_facet Varanasi, Sravani
Tuli, Roopan
Han, Fei
Chen, Rong
Choa, Fow-Sen
author_sort Varanasi, Sravani
collection PubMed
description 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 t-tests were performed on the local minimums with Bonferroni correction to control the family-wise error rate. These local minimums are connectivity states showing not only which brain regions but also how strong they are working together. They work as aging signatures that can be used to differentiate young and old groups. We found that the attention network’s connectivity signature is a state with all the regions working together and young subjects have a stronger average connectivity among these regions. We have also found a common pattern between young and old subjects where the left and right brain regions of the frontal network are sometimes working separately instead of together. In summary, in this work, we combined machine learning and statistical approaches to extract connectivity signatures, which can be utilized to distinguish aging brains and monitor possible treatment efficacy.
format Online
Article
Text
id pubmed-9920122
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-99201222023-02-12 Age Related Functional Connectivity Signature Extraction Using Energy-Based Machine Learning Techniques Varanasi, Sravani Tuli, Roopan Han, Fei Chen, Rong Choa, Fow-Sen Sensors (Basel) Article 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 t-tests were performed on the local minimums with Bonferroni correction to control the family-wise error rate. These local minimums are connectivity states showing not only which brain regions but also how strong they are working together. They work as aging signatures that can be used to differentiate young and old groups. We found that the attention network’s connectivity signature is a state with all the regions working together and young subjects have a stronger average connectivity among these regions. We have also found a common pattern between young and old subjects where the left and right brain regions of the frontal network are sometimes working separately instead of together. In summary, in this work, we combined machine learning and statistical approaches to extract connectivity signatures, which can be utilized to distinguish aging brains and monitor possible treatment efficacy. MDPI 2023-02-01 /pmc/articles/PMC9920122/ /pubmed/36772649 http://dx.doi.org/10.3390/s23031603 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Varanasi, Sravani
Tuli, Roopan
Han, Fei
Chen, Rong
Choa, Fow-Sen
Age Related Functional Connectivity Signature Extraction Using Energy-Based Machine Learning Techniques
title Age Related Functional Connectivity Signature Extraction Using Energy-Based Machine Learning Techniques
title_full Age Related Functional Connectivity Signature Extraction Using Energy-Based Machine Learning Techniques
title_fullStr Age Related Functional Connectivity Signature Extraction Using Energy-Based Machine Learning Techniques
title_full_unstemmed Age Related Functional Connectivity Signature Extraction Using Energy-Based Machine Learning Techniques
title_short Age Related Functional Connectivity Signature Extraction Using Energy-Based Machine Learning Techniques
title_sort age related functional connectivity signature extraction using energy-based machine learning techniques
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9920122/
https://www.ncbi.nlm.nih.gov/pubmed/36772649
http://dx.doi.org/10.3390/s23031603
work_keys_str_mv AT varanasisravani agerelatedfunctionalconnectivitysignatureextractionusingenergybasedmachinelearningtechniques
AT tuliroopan agerelatedfunctionalconnectivitysignatureextractionusingenergybasedmachinelearningtechniques
AT hanfei agerelatedfunctionalconnectivitysignatureextractionusingenergybasedmachinelearningtechniques
AT chenrong agerelatedfunctionalconnectivitysignatureextractionusingenergybasedmachinelearningtechniques
AT choafowsen agerelatedfunctionalconnectivitysignatureextractionusingenergybasedmachinelearningtechniques