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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-02-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/3/1603 |
| _version_ | 1797623225073532928 |
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| author | Sravani Varanasi Roopan Tuli Fei Han Rong Chen Fow-Sen Choa |
| author_facet | Sravani Varanasi Roopan Tuli Fei Han Rong Chen Fow-Sen Choa |
| author_sort | Sravani Varanasi |
| collection | DOAJ |
| 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 <i>t</i>-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. |
| first_indexed | 2024-03-11T09:25:39Z |
| format | Article |
| id | doaj.art-cf44bbb710534da483efdcf61998137e |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T09:25:39Z |
| publishDate | 2023-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-cf44bbb710534da483efdcf61998137e2023-11-16T18:03:34ZengMDPI AGSensors1424-82202023-02-01233160310.3390/s23031603Age Related Functional Connectivity Signature Extraction Using Energy-Based Machine Learning TechniquesSravani Varanasi0Roopan Tuli1Fei Han2Rong Chen3Fow-Sen Choa4Department of Electrical Engineering and Computer Science, University of Maryland Baltimore County, Baltimore, MD 21250, USADepartment of Electrical Engineering, Santa Clara University, Santa Clara, CA 95053, USAThe Hilltop Institute, University of Maryland Baltimore County, Baltimore, MD 21250, USADepartment of Diagnostic Radiology and Nuclear Medicine, University of Maryland Baltimore, Baltimore, MD 21201, USADepartment of Electrical Engineering and Computer Science, University of Maryland Baltimore County, Baltimore, MD 21250, USAThe 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 <i>t</i>-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.https://www.mdpi.com/1424-8220/23/3/1603brain connectivityenergy landscapefMRIresting state networkmachine learning |
| spellingShingle | Sravani Varanasi Roopan Tuli Fei Han Rong Chen Fow-Sen Choa Age Related Functional Connectivity Signature Extraction Using Energy-Based Machine Learning Techniques Sensors brain connectivity energy landscape fMRI resting state network machine learning |
| 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 | brain connectivity energy landscape fMRI resting state network machine learning |
| url | https://www.mdpi.com/1424-8220/23/3/1603 |
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