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Author:Koistinen, Olli-Pekka
Title:Bayesian Classification of fMRI Patterns for Natural Audiovisual Stimuli Using Sparsity Promoting Laplace Priors
Luonnollisiin audiovisuaalisiin ärsykkeisiin liittyvän fMRI-aktivaation bayesilainen luokittelu harvoja ratkaisuja suosivia Laplace-prioreja käyttäen
Publication type:Master's thesis
Publication year:2012
Pages:[11] + 69      Language:   eng
Department/School:Lääketieteellisen tekniikan ja laskennallisen tieteen laitos
Main subject:Laskennallinen tekniikka   (S-114)
Supervisor:Lampinen, Jouko
Instructor:Jylänki, Pasi ; Vehtari, Aki
Electronic version URL: http://urn.fi/URN:NBN:fi:aalto-201210043223
OEVS:
Electronic archive copy is available via Aalto Thesis Database.
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Location:P1 Ark Aalto  878   | Archive
Keywords:audiovisual
auditory cortex
automatic relevance determination
Bayesian
classification
expectation propagation
fMRI
Laplace prior
music
speech
audiovisuaalinen
bayesilainen
fMRI
kuuloaivokuori
Laplace-priori
luokittelu
musiikki
puhe
Abstract (eng): Bayesian linear binary classification models with sparsity promoting Laplace priors were applied to discriminate fMRI patterns related to natural auditory and audiovisual speech and music stimuli.
The region of interest comprised the auditory cortex and some surrounding regions related to auditory processing.

Truly sparse posterior mean solutions for the classifier weights were obtained by implementing an automatic relevance determination method using expectation propagation (ARDEP).
In ARDEP, the Laplace prior was decomposed into a Gaussian scale mixture, and these scales were optimised by maximising their marginal posterior density.
ARDEP was also compared to two other methods, which integrated approximately over the original Laplace prior: LAEP approximated the posterior as well by expectation propagation, whereas MCMC used a Markov chain Monte Carlo simulation method implemented by Gibbs sampling.

The resulting brain maps were consistent with previous studies for simpler stimuli and suggested that the proposed model is also able to reveal additional information about activation patterns related to natural audiovisual stimuli.
The predictive performance of the model was significantly above chance level for all approximate inference methods.
Regardless of intensive pruning of features, ARDEP was able to describe all of the most discriminative brain regions obtained by LAEP and MCMC.
However, ARDEP lost the more specific shape of the regions by representing them as one or more smaller spots, removing also some relevant features.
Abstract (fin): Bayesilaisia lineaarisia binääriluokittelumalleja ja harvoja ratkaisuja suosivia Laplace- prioreja sovellettiin erottelemaan luonnollisiin auditorisiin ja audiovisuaalisiin puhe- ja musiikkiärsykkeisiin liittyvää fMRI-aktivaatiota kuuloaivokuorella ja sitä ympäröivillä auditoriseen prosessointiin liittyvillä alueilla.

Absoluuttisen harvoja posteriorisia odotusarvoratkaisuja luokittimien painoille saatiin expectation propagation -algoritmin avulla toteutetulla automatic relevance determination -menetelmällä (ARDEP).
ARDEP-menetelmässä hyödynnettiin Laplace-priorin gaussista skaalahajotelmaa, jonka skaalaparametrit optimoitiin maksimoimalla niiden marginaalinen posterioritiheys.
Menetelmää verrattiin myös kahteen muuhun menetelmään, jotka integroivat approksimatiivisesti alkuperäisen Laplace-priorin yli: LAEP approksimoi posteriorijakaumaa niin ikään expectation propagation -algoritmin avulla, kun taas MCMC käytti Gibbs -poiminnalla toteutettua Markovin ketju Monte Carlo -simulaatiomenetelmää.

Tuloksena saadut aivokartat olivat linjassa aikaisempien, yksinkertaisemmilla ärsykkeillä saatujen tutkimustulosten kanssa, ja niiden perusteella bayesilaisten luokittelumallien avulla on mahdollista saada myös uudenlaista tietoa siitä, miten luonnollisia audiovisuaalisia ärsykkeitä koodataan aivoissa.
Mallien ennustuskyky oli kaikilla approksimaatiomenetelmillä merkittävästi sattumanvaraista tasoa korkeampi.
Piirteiden voimakkaasta karsinnasta huolimatta ARDEP pystyi kuvaamaan kaikki huomattavimmat LAEP:n ja MCMC:n erottelemat aivoalueet.
ARDEP menetti kuitenkin alueiden tarkemman muodon esittämällä ne yhtenä tai useampana pienempänä alueena, poistaen myös osan merkittävistä piirteistä.
ED:2012-09-21
INSSI record number: 45281
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