A4 Artikkeli konferenssijulkaisussa
Dynamic Functional Connectivity Captures Individuals’ Unique Brain Signatures (2020)
Gandhi, R., Garimella, A., Toiviainen, P., & Alluri, V. (2020). Dynamic Functional Connectivity Captures Individuals’ Unique Brain Signatures. In M. Mahmud, S. Vassanelli, M. S. Kaiser, & N. Zhong (Eds.), BI 2020 : 13th International Conference on Brain Informatics, Proceedings (pp. 97-106). Springer. Lecture Notes in Computer Science, 12241. https://doi.org/10.1007/978-3-030-59277-6_9
JYU-tekijät tai -toimittajat
Julkaisun tiedot
Julkaisun kaikki tekijät tai toimittajat: Gandhi, Rohan; Garimella, Arun; Toiviainen, Petri; Alluri, Vinoo
Emojulkaisu: BI 2020 : 13th International Conference on Brain Informatics, Proceedings
Emojulkaisun toimittajat: Mahmud, Mufti; Vassanelli, Stefano; Kaiser, M. Shamim; Zhong, Ning
Konferenssi:
- International Conference on Brain Informatics
Konferenssin paikka ja aika: Padua, Italy, 19.9.2020
ISBN: 978-3-030-59276-9
eISBN: 978-3-030-59277-6
Lehti tai sarja: Lecture Notes in Computer Science
ISSN: 0302-9743
eISSN: 1611-3349
Julkaisuvuosi: 2020
Sarjan numero: 12241
Artikkelin sivunumerot: 97-106
Kirjan kokonaissivumäärä: 378
Kustantaja: Springer
Kustannuspaikka: Cham
Julkaisumaa: Sveitsi
Julkaisun kieli: englanti
DOI: https://doi.org/10.1007/978-3-030-59277-6_9
Julkaisun avoin saatavuus: Ei avoin
Julkaisukanavan avoin saatavuus:
Julkaisu on rinnakkaistallennettu (JYX): https://jyx.jyu.fi/handle/123456789/72913
Tiivistelmä
Recent neuroimaging evidence suggest that there exists a unique individual-specific functional connectivity (FC) pattern consistent across tasks. The objective of our study is to utilize FC patterns to identify an individual using a supervised machine learning approach. To this end, we use two previously published data sets that comprises resting-state and task-based fMRI responses. We use static FC measures as input to a linear classifier to evaluate its performance. We additionally extend this analysis to capture dynamic FC using two approaches: the common sliding window approach and the more recent phase synchrony-based measure. We found that the classification models using dynamic FC patterns as input outperform their static analysis counterpart by a significant margin for both data sets. Furthermore, sliding window-based analysis proved to capture more individual-specific brain connectivity patterns than phase synchrony measures for resting-state data while the reverse pattern was observed for the task-based data set. Upon investigating the effects of feature reduction, we found that feature elimination significantly improved results up to a point with near-perfect classification accuracy for the task-based data set while a gradual decrease in the accuracy was observed for resting-state data set. The implications of these findings are discussed. The results we have are promising and present a novel direction to investigate further.
YSO-asiasanat: toiminnallinen magneettikuvaus; yksilö; tunnistaminen; koneoppiminen
Vapaat asiasanat: fMRI; functional connectivity; classification; variance inflation factor; individual differences
Liittyvät organisaatiot
OKM-raportointi: Kyllä
Raportointivuosi: 2020
JUFO-taso: 1
