Mathematical Methods in Neurophysiology
methods for image and data analysis
http://mida.dima.unige.it
Alberto Sorrentino1, Annalisa Pascarella 2, Cristina Campi3, Gianvittorio Luria4, Riccardo Aramini1, Valentina
Vivaldi1, Sara Sommariva1 and Michele Piana1
1 Dipartimento di Matematica, Universit`a di Genova
2 CNR-IAC
3 CNR-SPIN
4 DIME - Sez. Metodi e Modelli Matematici
Magnetoencephalography
Magnetoencephalography (MEG) is a functional imaging technique that measures non–invasively the magnetic field produced
by the neural currents. Modern MEG devices contain between
100 and 500 sensors, each one sampling the magnetic field every
millisecond.
In typical MEG experiments, several brain areas activate and deactivate, with time–varying intensity. In the dipolar approximation, the electrical current in a small brain area is modeled by a
point source, named current dipole.
www.unige.it
Rapid Visual Categorization
We investigate the brain activity elicited in a rapid categorization task. Subjects were asked to
categorize images of animals VS non-animals quickly. Half trials were followed by a dynamic
noise mask, to block feedback effects. We used MEG to record the magnetic neural activity,
and applied both distributed and dipolar source models for localizing in space and time the
corresponding sources.
Stochastic Resonance in the Visual System
Stochastic Resonance is a
well known model that explains how the presence of
noise may help to detect a
sub-threshold signal. We investigated whether such phenomenon occurs in the human visual system, by presenting healthy subjects with
sub-threshold noisy images
of short words, and recording the magnetophysiological responses. We modelled
neural responses with current dipoles and studied how
dipole strength and latency
change with the noise level
[1].
Cortical Constraint
We study the effect of using source orientation constraints in inverse methods in MEG, either
when the data fullfill the constraint and when they do not.
Top left panel: Full view of the cortex surface.
Bottom left panel: Zoom of the surface; the blue
circles represent possible position for the active
sources.
Bayesian Imaging of Dipolar Sources
We developed Bayesian approaches
- a dynamic approach [2, 3, 4]
which analyses time series.
Dipoles can change their location in time, the posterior
density at time t is used as
a prior at time t + 1; we use
sequential Monte Carlo methods (particle filters) to approximate the posterior distribution;
- a static approach [5] that
produces an estimate of the
number of dipoles and of the
dipole parameters, given a
single topography ; by topography we mean the recordings at a single time point,
or at a single frequency after
Fourier transform, or a single
ICA component.
for estimation of multiple dipoles from MEG data:
Bottom right panel: Cortical grids with free
orientation (FO), loose orientation (LOC) and
strict orientation (SOC) and different average
distance between sources (5 and 8 mm).
Reconstruction errors with respect to manually
fitted dipoles in a somatosensory data.
Brain response to median nerve stimulation.
We developed a Matlab Graphical User Interface called
HADES (Highly Automated Dipole EStimation), [6]
EcoG
We addressed the source modelling when the measurements are obtained by subdural strip and
grid electrodes. First the lead-field matrix was created using a new function provided by the
OpenMEEG software. Then we studied the ill-conditioning of this matrix varying both shape and
location of the electrodes grid positions. Finally we performed the source localization by applying
a beamformer to both synthetic data and experimental ElectroCorticoGraphy data.
References
A. Sorrentino, L. Parkkonen, M. Piana, A. M. Massone, L. Narici, S. Carozzo, M. Riani, and W. G. Sannita.
Modulation of brain and behavioural responses to cognitive visual stimuli with varying signal-to-noise ratio.
Clinical Neurophysiology, 117:1098–1105, 2006.
C. Campi, A. Pascarella, A. Sorrentino, and M. Piana.
A Rao-Blackwellized particle filter for magnetoencephalography.
Inverse Problems, 24:025023, 2008.
A. Sorrentino, L. Parkkonen, A. Pascarella, C. Campi, and M. Piana.
Dynamical MEG source modeling with multi-target bayesian filtering.
Human Brain Mapping, 30:1911–1921, 2009.
A. Sorrentino, A.M. Johansen, J.A.D. Aston, T.E. Nichols, and W.S. Kendall.
Dynamic filtering of static dipoles in Magnetoencephalography.
Annals of Applied Statistics, 7:955–988, 2013.
A. Sorrentino, G. Luria, and R. Aramini.
Bayesian multi-dipole modeling of a single topography in meg by adaptive sequential monte-carlo samplers.
Inverse Problems, arXiv 1305.4511 [stat.AP], in press.
C. Campi, A. Pascarella, A. Sorrentino, and M. Piana.
Highly automated dipole estimation.
Computational Intelligence and Neuroscience, 2011:982185, 2011.
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