Analysis of fMRI signals from working memory tasks and resting-state of brain: neutrosophic-entropy-based clustering algorithm
Authors:
- Pritpal Singh,
- Marcin Wątorek,
- Anna Ceglarek,
- Magdalena Fąfrowicz,
- Koryna Lewandowska,
- Tadeusz Marek,
- Barbara Sikora-Wachowicz,
- Paweł Oświęcimka
Abstract
This study applies a neutrosophic-entropy-based clustering algorithm (NEBCA) to analyze the fMRI signals. We consider the data obtained from four different working memory tasks and the brain’s resting state for the experimental purpose. Three non-overlapping clusters of data related to temporal brain activity are determined and statistically analyzed. Moreover, we used the Uniform Manifold Approximation and Projection (UMAP) method to reduce system dimensionality and present the effectiveness of NEBCA. The results show that using NEBCA, we are able to distinguish between different working memory tasks and resting-state and identify subtle differences in the related activity of brain regions. By analyzing the statistical properties of the entropy inside the clusters, the various regions of interest (ROIs), according to Automated Anatomical Labeling (AAL) atlas crucial for clustering procedure, are determined. The inferior occipital gyrus is established as an important brain region in distinguishing the resting state from the tasks. Moreover, the inferior occipital gyrus and superior parietal lobule are identified as necessary to correct the data discrimination related to the different memory tasks. We verified the statistical significance of the results through the two-sample t-test and analysis of surrogates performed by randomization of the cluster elements. The presented methodology is also appropriate to determine the influence of time of day on brain activity patterns. The differences between working memory tasks and resting-state in the morning are related to a lower index of small-worldness and sleep inertia in the first hours after waking. We also compared the performance of NEBCA to two existing algorithms, KMCA and FKMCA. We showed the advantage of the NEBCA over these algorithms that could not effectively accumulate fMRI signals with higher variability.
- Record ID
- CUT7370e3d78c52431d8277429fc752dcb3
- Publication categories
- ;
- Author
- Journal series
- International Journal of Neural Systems, ISSN 0129-0657, e-ISSN 1793-6462
- Issue year
- 2022
- Vol
- 32
- No
- 4
- Pages
- 2250012-1-2250012-20
- Article number
- 2250012
- Other elements of collation
- rys.; schem.; tab.; wykr.; Bibliografia (na s.) - 2250012-18-2250012-20; Bibliografia (liczba pozycji) - 65; Oznaczenie streszczenia - Streszcz. ang.; Numeracja w czasopiśmie - Vol. 32, No. 04
- Keywords in English
- neutrosophic set, entropy, clustering, functional magnetic resonance imaging signal, working memory, resting state
- ASJC Classification
- ;
- DOI
- DOI:10.1142/S0129065722500125 Opening in a new tab
- URL
- https://www.worldscientific.com/doi/10.1142/S0129065722500125 Opening in a new tab
- Related project
- Sztuczne sieci neuronowe inspirowane biologicznie (TEAM-NET Fundacji na rzecz Nauki Polskiej). . Project leader at PK: , ,
- Wpływ pory dnia na neuronalne mechanizmy leżące u podłoża zniekształceń w pamięci krótkotrwałej wywołanych interferencją o charakterze leksykalnym i przestrzennym - badanie fMRI. . Project leader at PK: , ,
- Sztuczne sieci neuronowe inspirowane biologicznie (TEAM-NET Fundacji na rzecz Nauki Polskiej). . Project leader at PK: , ,
- Language
- eng (en) English
- Score (nominal)
- 140
- Score source
- journalList
- Score
- Publication indicators
- Citation count
- 10
- Additional fields
- Indeksowana w: Scopus
- Uniform Resource Identifier
- https://cris.pk.edu.pl/info/article/CUT7370e3d78c52431d8277429fc752dcb3/
- URN
urn:pkr-prod:CUT7370e3d78c52431d8277429fc752dcb3
* presented citation count is obtained through Internet information analysis, and it is close to the number calculated by the Publish or PerishOpening in a new tab system.