Open AccessStatistical inference in brain graphs using threshold‐free network‐based statistics
Author(s) -
Baggio Hugo C.,
Abos Alexandra,
Segura Barbara,
Campabadal Anna,
GarciaDiaz Anna,
Uribe Carme,
Compta Yaroslau,
Marti Maria Jose,
Valldeoriola Francesc,
Junque Carme
Publication year - 2018
Publication title -
human brain mapping
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.005
H-Index - 191
eISSN - 1097-0193
pISSN - 1065-9471
DOI - 10.1002/hbm.24007
Subject(s) - statistical inference , inference , computer science , statistical hypothesis testing , statistic , set (abstract data type) , test statistic , artificial intelligence , multiple comparisons problem , a priori and a posteriori , measure (data warehouse) , data mining , pattern recognition (psychology) , statistics , mathematics , philosophy , epistemology , programming language
The description of brain networks as graphs where nodes represent different brain regions and edges represent a measure of connectivity between a pair of nodes is an increasingly used approach in neuroimaging research. The development of powerful methods for edge‐wise group‐level statistical inference in brain graphs while controlling for multiple‐testing associated false‐positive rates, however, remains a difficult task. In this study, we use simulated data to assess the properties of threshold‐free network‐based statistics (TFNBS). The TFNBS combines threshold‐free cluster enhancement, a method commonly used in voxel‐wise statistical inference, and network‐based statistic (NBS), which is frequently used for statistical analysis of brain graphs. Unlike the NBS, TFNBS generates edge‐wise significance values and does not require the a priori definition of a hard cluster‐defining threshold. Other test parameters, nonetheless, need to be set. We show that it is possible to find parameters that make TFNBS sensitive to strong and topologically clustered effects, while appropriately controlling false‐positive rates. Our results show that the TFNBS is an adequate technique for the statistical assessment of brain graphs.