A Comparison Between Pattern-Based Classifiers and Inverted Encoding Models for fMRI Decoding
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2020-10-28
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en
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Abstract
Pattern-based classification has become the standard approach for
the analysis of functional magnetic resonance imaging (fMRI)
data. These classifiers use any discriminating information, that
is present in the activity patterns across voxels, in order to decode
stimuli or experimental conditions from the measured brain
activity. Recently, however, explicit models of neural representation
have become popular as an alternative approach to analysing
fMRI data. These so-called encoding models describe explicitly
how stimuli modulate the activity of neuronal populations,
that is then eventually measured with fMRI. By inverting these
models, they can also be used for decoding (i.e., for going from
brain activity to stimuli). In this study, we systematically compare
these two approaches commonly used for decoding. To do
so, we use a linear support vector machine (SVM) and an inverted
encoding model (IEM) to decode orientation from the activity
patterns in primary and secondary visual cortex as measured
with fMRI. Specifically, we investigate whether the two
methods can produce comparable outputs and results. Additionally,
we test whether these results are similarly influenced
by different design and preprocessing parameters. We find that
similar decoding accuracies and confusion matrices can be obtained
from both methods. Moreover, the SVM and IEM decoding
accuracies are affected in the same way by the different parameters:
Decoding accuracies increase with more data used to
train the classifier or used to fit the encoding model. For both
methods, accuracies also first increase when more voxels are selected
until reaching a plateau (SVM) or maximum (IEM). Furthermore,
small amounts of spatial smoothing (3 or 4 mm) are
beneficial for both approaches, whereas decoding accuracies decrease
for larger amounts of smoothing. While both methods
are comparable in terms of decoding accuracy, only IEM can be
used to obtain channel response profiles that characterize the responses
of neuronal subpopulations tuned to different feature values.
Moreover, these channel response profiles are surprisingly
unaffected by even large amounts of spatial smoothing, revealing
a dissociation between the IEM response profiles and the IEM
decoding accuracies.
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Faculteit der Sociale Wetenschappen