Exploring Physiological Differences in Brain Areas Using Statistical Complexity Analysis of BOLD Signals
The brain is a fundamental organ for the human body to function properly, for which it needs to receive a continuous flow of blood, which explains the existence of control mechanisms that act to maintain this flow as constant as possible in a process known as cerebral autoregulation. One way to obta...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-01-01
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| Series: | Entropy |
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| Online Access: | https://www.mdpi.com/1099-4300/26/1/81 |
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| author | Catalina Morales-Rojas Ronney B. Panerai José Luis Jara |
| author_facet | Catalina Morales-Rojas Ronney B. Panerai José Luis Jara |
| author_sort | Catalina Morales-Rojas |
| collection | DOAJ |
| description | The brain is a fundamental organ for the human body to function properly, for which it needs to receive a continuous flow of blood, which explains the existence of control mechanisms that act to maintain this flow as constant as possible in a process known as cerebral autoregulation. One way to obtain information on how the levels of oxygen supplied to the brain vary is through of BOLD (Magnetic Resonance) images, which have the advantage of greater spatial resolution than other forms of measurement, such as transcranial Doppler. However, they do not provide good temporal resolution nor allow for continuous prolonged examination. Thus, it is of great importance to find a method to detect regional differences from short BOLD signals. One of the existing alternatives is complexity measures that can detect changes in the variability and temporal organisation of a signal that could reflect different physiological states. The so-called statistical complexity, created to overcome the shortcomings of entropy alone to explain the concept of complexity, has shown potential with haemodynamic signals. The aim of this study is to determine by using statistical complexity whether it is possible to find differences between physiologically distinct brain areas in healthy individuals. The data set includes BOLD images of 10 people obtained at the University Hospital of Leicester NHS Trust with a 1.5 Tesla magnetic resonance imaging scanner. The data were captured for 180 s at a frequency of 1 Hz. Using various combinations of statistical complexities, no differences were found between hemispheres. However, differences were detected between grey matter and white matter, indicating that these measurements are sensitive to differences in brain tissues. |
| first_indexed | 2024-03-08T10:57:13Z |
| format | Article |
| id | doaj.art-66365e1cb9d54cafb3030ffdd1b67f59 |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-03-08T10:57:13Z |
| publishDate | 2024-01-01 |
| publisher | MDPI AG |
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| series | Entropy |
| spelling | doaj.art-66365e1cb9d54cafb3030ffdd1b67f592024-01-26T16:23:16ZengMDPI AGEntropy1099-43002024-01-012618110.3390/e26010081Exploring Physiological Differences in Brain Areas Using Statistical Complexity Analysis of BOLD SignalsCatalina Morales-Rojas0Ronney B. Panerai1José Luis Jara2Departamento de Ingeniería Informática, Facultad de Ingeniería, Universidad de Santiago de Chile, Santiago 9170022, ChileDepartment of Cardiovascular Sciences, University of Leicester, Leicester LE1 7RH, UKDepartamento de Ingeniería Informática, Facultad de Ingeniería, Universidad de Santiago de Chile, Santiago 9170022, ChileThe brain is a fundamental organ for the human body to function properly, for which it needs to receive a continuous flow of blood, which explains the existence of control mechanisms that act to maintain this flow as constant as possible in a process known as cerebral autoregulation. One way to obtain information on how the levels of oxygen supplied to the brain vary is through of BOLD (Magnetic Resonance) images, which have the advantage of greater spatial resolution than other forms of measurement, such as transcranial Doppler. However, they do not provide good temporal resolution nor allow for continuous prolonged examination. Thus, it is of great importance to find a method to detect regional differences from short BOLD signals. One of the existing alternatives is complexity measures that can detect changes in the variability and temporal organisation of a signal that could reflect different physiological states. The so-called statistical complexity, created to overcome the shortcomings of entropy alone to explain the concept of complexity, has shown potential with haemodynamic signals. The aim of this study is to determine by using statistical complexity whether it is possible to find differences between physiologically distinct brain areas in healthy individuals. The data set includes BOLD images of 10 people obtained at the University Hospital of Leicester NHS Trust with a 1.5 Tesla magnetic resonance imaging scanner. The data were captured for 180 s at a frequency of 1 Hz. Using various combinations of statistical complexities, no differences were found between hemispheres. However, differences were detected between grey matter and white matter, indicating that these measurements are sensitive to differences in brain tissues.https://www.mdpi.com/1099-4300/26/1/81MRIBOLDstatistical complexitycerebral haemodynamics |
| spellingShingle | Catalina Morales-Rojas Ronney B. Panerai José Luis Jara Exploring Physiological Differences in Brain Areas Using Statistical Complexity Analysis of BOLD Signals Entropy MRI BOLD statistical complexity cerebral haemodynamics |
| title | Exploring Physiological Differences in Brain Areas Using Statistical Complexity Analysis of BOLD Signals |
| title_full | Exploring Physiological Differences in Brain Areas Using Statistical Complexity Analysis of BOLD Signals |
| title_fullStr | Exploring Physiological Differences in Brain Areas Using Statistical Complexity Analysis of BOLD Signals |
| title_full_unstemmed | Exploring Physiological Differences in Brain Areas Using Statistical Complexity Analysis of BOLD Signals |
| title_short | Exploring Physiological Differences in Brain Areas Using Statistical Complexity Analysis of BOLD Signals |
| title_sort | exploring physiological differences in brain areas using statistical complexity analysis of bold signals |
| topic | MRI BOLD statistical complexity cerebral haemodynamics |
| url | https://www.mdpi.com/1099-4300/26/1/81 |
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