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Fig 3 -:

Veera Itälinna ; Hanna Kaltiainen ; Nina Forss..
doi:10.1371/journal.pcbi.1011613.g003. , 2023

Link: https://doi.org/10.1371/journal.pcbi.1011613.g003

RT Journal
T1 <Title>Fig 3 -</Title>

UL https://suche.suub.uni-bremen.de/peid=base-ftunivoxfordfig:oai:figshare.com:article_24538522&Exemplar=1&LAN=DE
				
A1 Veera Itälinna
A1 Hanna Kaltiainen
A1 Nina Forss
A1 Mia Liljeström
A1 Lauri Parkkonen
YR 2023
K1 Medicine
K1 Cell Biology
K1 Genetics
K1 Neuroscience
K1 Biotechnology
K1 Cancer
K1 Space Science
K1 Biological Sciences not elsewhere classified
K1 Mathematical Sciences not elsewhere classified
K1 Information Systems not elsewhere classified
K1 quantitative deviation maps
K1 power spectra across
K1 healthy control subjects
K1 demonstrated several population
K1 4 &# 8211
K1 using normative modeling
K1 full normative data
K1 trained model revealed
K1 normative modeling approach
K1 state meg measurements
K1 meg data combined
K1 theta frequency band
K1 current approach could
K1 many brain disorders
K1 individual mtbi patients
K1 25 mtbi patients
K1 brain disorders
K1 normative dataset
K1 learning approach
K1 trained support
K1 could serve
K1 meg studies
K1 meg signals
K1 identify patients
K1 distinguish patients
K1 discriminate patients
K1 deriving meg
K1 frequency ranges
K1 frequency activity
K1 brain injury
K1 detect mtbi
K1 would benefit
K1 wide range
K1 urgently needed
K1 thus providing
K1 significant indicator
K1 results demonstrate
K1 objective markers
K1 multivariate machine
K1 matched subsets
JF doi:10.1371/journal.pcbi.1011613.g003
LK http://dx.doi.org/https://doi.org/10.1371/journal.pcbi.1011613.g003
DO https://doi.org/10.1371/journal.pcbi.1011613.g003
SF ELIB - SuUB Bremen

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