129,433 research outputs found
Mutual Information in Frequency and its Application to Measure Cross-Frequency Coupling in Epilepsy
We define a metric, mutual information in frequency (MI-in-frequency), to
detect and quantify the statistical dependence between different frequency
components in the data, referred to as cross-frequency coupling and apply it to
electrophysiological recordings from the brain to infer cross-frequency
coupling. The current metrics used to quantify the cross-frequency coupling in
neuroscience cannot detect if two frequency components in non-Gaussian brain
recordings are statistically independent or not. Our MI-in-frequency metric,
based on Shannon's mutual information between the Cramer's representation of
stochastic processes, overcomes this shortcoming and can detect statistical
dependence in frequency between non-Gaussian signals. We then describe two
data-driven estimators of MI-in-frequency: one based on kernel density
estimation and the other based on the nearest neighbor algorithm and validate
their performance on simulated data. We then use MI-in-frequency to estimate
mutual information between two data streams that are dependent across time,
without making any parametric model assumptions. Finally, we use the MI-in-
frequency metric to investigate the cross-frequency coupling in seizure onset
zone from electrocorticographic recordings during seizures. The inferred
cross-frequency coupling characteristics are essential to optimize the spatial
and spectral parameters of electrical stimulation based treatments of epilepsy.Comment: This paper is accepted for publication in IEEE Transactions on Signal
Processing and contains 15 pages, 9 figures and 1 tabl
Separate cortical stages in amodal completion revealed by functional magnetic resonance adaptation : research article
Background Objects in our environment are often partly occluded, yet we effortlessly perceive them as whole and complete. This phenomenon is called visual amodal completion. Psychophysical investigations suggest that the process of completion starts from a representation of the (visible) physical features of the stimulus and ends with a completed representation of the stimulus. The goal of our study was to investigate both stages of the completion process by localizing both brain regions involved in processing the physical features of the stimulus as well as brain regions representing the completed stimulus. Results Using fMRI adaptation we reveal clearly distinct regions in the visual cortex of humans involved in processing of amodal completion: early visual cortex - presumably V1 - processes the local contour information of the stimulus whereas regions in the inferior temporal cortex represent the completed shape. Furthermore, our data suggest that at the level of inferior temporal cortex information regarding the original local contour information is not preserved but replaced by the representation of the amodally completed percept. Conclusion These findings provide neuroimaging evidence for a multiple step theory of amodal completion and further insights into the neuronal correlates of visual perception
Parametric inference in the large data limit using maximally informative models
Motivated by data-rich experiments in transcriptional regulation and sensory
neuroscience, we consider the following general problem in statistical
inference. When exposed to a high-dimensional signal S, a system of interest
computes a representation R of that signal which is then observed through a
noisy measurement M. From a large number of signals and measurements, we wish
to infer the "filter" that maps S to R. However, the standard method for
solving such problems, likelihood-based inference, requires perfect a priori
knowledge of the "noise function" mapping R to M. In practice such noise
functions are usually known only approximately, if at all, and using an
incorrect noise function will typically bias the inferred filter. Here we show
that, in the large data limit, this need for a pre-characterized noise function
can be circumvented by searching for filters that instead maximize the mutual
information I[M;R] between observed measurements and predicted representations.
Moreover, if the correct filter lies within the space of filters being
explored, maximizing mutual information becomes equivalent to simultaneously
maximizing every dependence measure that satisfies the Data Processing
Inequality. It is important to note that maximizing mutual information will
typically leave a small number of directions in parameter space unconstrained.
We term these directions "diffeomorphic modes" and present an equation that
allows these modes to be derived systematically. The presence of diffeomorphic
modes reflects a fundamental and nontrivial substructure within parameter
space, one that is obscured by standard likelihood-based inference.Comment: To appear in Neural Computatio
Act quickly, decide later: long latency visual processing underlies perceptual decisions but not reflexive behavior
Jolij J, Scholte H, Van Gaal S, Hodgson TL, Lamme VAF (2011) Act quickly, decide later: Long latency visual processing underlies perceptual decisions but not reflexive behavior. Journal of Cognitive Neuroscience 23(12), p 3734-3745
The role of anterior cingulate cortex in the affective evaluation of conflict
An influential theory of anterior cingulate cortex (ACC) function argues that this brain region plays a crucial role in the affective evaluation of performance monitoring and control demands. Specifically, control-demanding processes such as response conflict are thought to be registered as aversive signals by ACC, which in turn triggers processing adjustments to support avoidance learning. In support of conflict being treated as an aversive event, recent behavioral studies demonstrated that incongruent (i.e., conflict inducing), relative to congruent, stimuli can speed up subsequent negative, relative to positive, affective picture processing. Here, we used fMRI to investigate directly whether ACC activity in response to negative versus positive pictures is modulated by preceding control demands, consisting of conflict and task-switching conditions. The results show that negative, relative to positive, pictures elicited higher ACC activation after congruent, relative to incongruent, trials, suggesting that ACC's response to negative (positive) pictures was indeed affectively primed by incongruent (congruent) trials. Interestingly, this pattern of results was observed on task repetitions but disappeared on task alternations. This study supports the proposal that conflict induces negative affect and is the first to show that this affective signal is reflected in ACC activation
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