Homepage of Fetsje Bijma
Research Interests
My research is centered on statistical methods
for life sciences, in particular for neuroscience. During the past
few years I have been working on inverse problems and covariance
models for brain imaging techniques like MEG, EEG and EEG/fMRI.
Currently I'm involved in projects on optimizing the analysis of
EEG/fMRI data, on network analysis for the human brain, and on
network properties of neuron populations.
Courses
Statistical Data Analysis (see www.bb.vu.nl)
Experimental Design and Data Analysis (see www.bb.vu.nl)
Advanced Statistical Methods (see www.bb.vu.nl)
Algemene Statistiek voor BA/Wis (see www.bb.vu.nl)
Statistics voor Ect (see www.bb.vu.nl)
Kansrekening en Stastistiek 2 voor Medische NatuurWetenschappen
Mathematische Methoden 1 voor Medische NatuurWetenschappen (see www.bb.vu.nl)
Statistiek voor SBI/Far/MNW (see www.bb.vu.nl)
Project Wiskunde
Masterclass Haal Meer Uit Je Hersenen
Publications
- Ros BP, Bijma F, de
Munck JC, de Gunst MCM: Existence
and uniqueness of the maximum likelihood estimator for models
with a Kronecker product covariance structure, Journ.
Multivar. Anal. 143: 345-361, 2016
- Ros BP, Bijma F, de
Munck JC, de Gunst MCM: An
integrated hierarchical Gaussian graphical model for
estimating connectivity based on co-registered EEG-fMRI data,
submitted, 2015
- Ros BP, Bijma F, de
Gunst MCM, de Munck JC: A
three domain covariance framework for EEG/MEG data,
NeuroImage 119: 305-315, 2015
- McAssey MP*, Bijma F*:
A clustering coefficient for
complete weighted networks, Network Science 3(2):
183-195, 2015
- McAssey MP*, Bijma F*,
Tarigan B, van Pelt J, van Ooijen A, de Gunst MCM: A morpho-density approach to
estimating neural connectivity, PLOS ONE 9(1): e86526,
doi:10.1371/journal.pone.0086526, 2014 (* first two authors contributed
equally)
- Van Ooyen A, Carnell A, de Ridder S, Tarigan B, Mansvelder H,
Bijma F, de Gunst M, Van
Pelt J: Independently
outgrowing neurons and geometry-based synapse formation
produce networks with realistic synaptic connectivity,
PLoS ONE 9(1): e85858, doi:10.1371/journal.pone.0085858, 2014
- Hindriks R, Jansen R, Bijma
F, Mansvelder HD, de Gunst MCM, van der Vaart AW: Unbiased estimation of Langevin
dynamics from time series with application to hippocampal
field potentials in vitro, Phys.Rev. E 84(2): 021133,
2011
- Hindriks R, Bijma F,
van Dijk BW, van der Werf YD, van Someren EJW, van der
Vaart AW: Dynamics underlying
spontaneous human alpha oscillations: A data-driven approach,
NeuroImage 57(2): 440-451, 2011
- Hindriks R, Bijma F,
van Dijk BW, Stam CJ, van der Werf YD, van Someren EJW, de Munck
JC, van der Vaart AW: Data-Driven
Modeling of Phase Interactions Between Spontaneous MEG
Oscillations, Human Brain Mapping 32(7): 1161-1178,
2011
- de Munck JC, Bijma F: How are evoked responses
generated? The need for a unified mathematical framework,
Clin. Neurophys. 121(2): 127-129, 2010
- de Munck JC, Bijma F:
Three-way matrix analysis, the
MUSIC algorithm and the coupled dipole model, Journ.
Neurosc. Methods 183(1): 63-71, 2009
- Gonçalves SI, Bijma
F, Pouwels, PWJ, Jonker MA, Kuijer JPA, Heethaar RM,
Lopes da Silva FH, De Munck JC: A Data and Model-Driven Approach to Explore
Inter-Subject Variability of Resting-State Brain Activity
Using EEG-fMRI, IEEE Journ. Sel. Top. Sign. Proc. 2(6):
944-953, 2008
- Bijma F, De Munck JC:
A space-frequency analysis of
MEG background processes, NeuroImage 43(3): 478-488,
2008
- Bijma F, De Munck JC,
Huizenga HM, Heethaar RM, Nehorai A: Simultaneous estimation and testing of sources in
multiple MEG data sets, IEEE Trans. Signal Proc.
Special Issue Brain Imaging, 53(9): 3449- 3460, 2005
- Bijma F, De Munck JC,
Heethaar RM: The
spatiotemporal MEG covariance matrix modeled as a sum of
Kronecker products, NeuroImage 27(2): 402-415, 2005
- Bijma F, De Munck JC,
Böcker KBE, Huizenga HM, Heethaar RM: The Coupled Dipole Model: an
integrated model for multiple MEG/EEG data sets,
NeuroImage 23(3): 890-904, 2004
- De Munck JC, Bijma F,
Gaura P, Sieluzycki C, Branco MI, Heethaar RM: A maximum likelihood estimator for
trial-to-trial variations in noisy MEG/EEG data sets,
IEEE Trans. Biomed Eng. 51(12): 2123- 2128, 2004
- Bijma F, De Munck JC,
Huizenga HM, Heethaar RM: A
Mathematical Approach to the Temporal Stationarity of
Background Noise in MEG/EEG measurements, NeuroImage
20(1): 233-243, 2003
- Gonçalves S, De Munck JC, Verbunt JPA, Bijma F, Heethaar RM, Lopes
da Silva FH: In vivo
measurement of the Brain and Skull resistivities using an EIT
based method and realistic models for the head, IEEE
Trans. Biomed. Eng. 50(6): 754-767, 2003
Theses
Bijma F: Mathematical modelling of
Magnetoencephalographic data, Ph.D. Thesis, VU University
Amsterdam, The Netherlands, 2005, cum laude
Bijma F: Generalized Fresnel distributions,
M.Sc. Thesis Mathematics, RuG University of Groningen, The
Netherlands, 2000, cum laude