Estimating Distributed Representations of Evoked Responses and Oscillatory Brain Activity

Jensen, Ole; Hesse, Christian A.

doi:10.1093/acprof:oso/9780195307238.003.0007

Cited by 11 publications

(7 citation statements)

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“…Instead, the goal was simply to emulate general alpha-range visuocortical activation in a manner that would roughly reproduce the global patterns of task-related EEG properties observed at the scalp [ 19 , 31 , 32 , 43 ]. This strategy is supported by the fact that any measured scalp electromagnetic distribution may be described by an infinite number of possible source distributions [ 61 ].…”

Section: Methodsmentioning

confidence: 99%

K-th Nearest Neighbor (KNN) Entropy Estimates of Complexity and Integration from Ongoing and Stimulus-Evoked Electroencephalographic (EEG) Recordings of the Human Brain

Trujillo

2019

Entropy

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Information-theoretic measures for quantifying multivariate statistical dependence have proven useful for the study of the unity and diversity of the human brain. Two such measures–integration, I(X), and interaction complexity, CI(X)–have been previously applied to electroencephalographic (EEG) signals recorded during ongoing wakeful brain states. Here, I(X) and CI(X) were computed for empirical and simulated visually-elicited alpha-range (8–13 Hz) EEG signals. Integration and complexity of evoked (stimulus-locked) and induced (non-stimulus-locked) EEG responses were assessed using nonparametric k-th nearest neighbor (KNN) entropy estimation, which is robust to the nonstationarity of stimulus-elicited EEG signals. KNN-based I(X) and CI(X) were also computed for the alpha-range EEG of ongoing wakeful brain states. I(X) and CI(X) patterns differentiated between induced and evoked EEG signals and replicated previous wakeful EEG findings obtained using Gaussian-based entropy estimators. Absolute levels of I(X) and CI(X) were related to absolute levels of alpha-range EEG power and phase synchronization, but stimulus-related changes in the information-theoretic and other EEG properties were independent. These findings support the hypothesis that visual perception and ongoing wakeful mental states emerge from complex, dynamical interaction among segregated and integrated brain networks operating near an optimal balance between order and disorder.

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Section: Methodsmentioning

confidence: 99%

K-th Nearest Neighbor (KNN) Entropy Estimates of Complexity and Integration from Ongoing and Stimulus-Evoked Electroencephalographic (EEG) Recordings of the Human Brain

Trujillo

2019

Entropy

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“…To substantiate the interpretation of the discriminant channels, we performed source localization on its spatial projection over time (see Decoding analysis ). We did not a priori specify regions of interest and therefore made use of the minimum-norm estimation technique 33 (as implemented in FieldTrip), rather than beamformers, because the former is the preferred technique when estimating distributed event-related source activity 34 . Single-trial covariance estimates were calculated from the data during the baseline interval of −200 to 0 ms relative to stimulus-onset.…”

Section: Methodsmentioning

confidence: 99%

Dissociating sensory from decision processes in human perceptual decision making

Mostert

Kok

Lange

2015

Sci Rep

126

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A key question within systems neuroscience is how the brain translates physical stimulation into a behavioral response: perceptual decision making. To answer this question, it is important to dissociate the neural activity underlying the encoding of sensory information from the activity underlying the subsequent temporal integration into a decision variable. Here, we adopted a decoding approach to empirically assess this dissociation in human magnetoencephalography recordings. We used a functional localizer to identify the neural signature that reflects sensory-specific processes, and subsequently traced this signature while subjects were engaged in a perceptual decision making task. Our results revealed a temporal dissociation in which sensory processing was limited to an early time window and consistent with occipital areas, whereas decision-related processing became increasingly pronounced over time, and involved parietal and frontal areas. We found that the sensory processing accurately reflected the physical stimulus, irrespective of the eventual decision. Moreover, the sensory representation was stable and maintained over time when it was required for a subsequent decision, but unstable and variable over time when it was task-irrelevant. In contrast, decision-related activity displayed long-lasting sustained components. Together, our approach dissects neuro-anatomically and functionally distinct contributions to perceptual decisions.

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“…The subjects' T1-weighted MRIs were segmented and the linear transformation matrices from individual anatomical space to the template space were calculated. The minimum-norm estimation algorithm was selected in the source reconstruction ( Dale et al, 2000 ; Jensen and Hesse, 2010 ) and the evoked activity for each voxel was extracted.…”

Section: Methodsmentioning

confidence: 99%

An enriched granger causal model allowing variable static anatomical constraints

Luo

Tian

et al. 2019

NeuroImage: Clinical

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The anatomical connectivity constrains but does not fully determine functional connectivity, especially when one explores into the dynamics over the course of a trial. Therefore, an enriched granger causal model (GCM) integrated with anatomical prior information is proposed in this study, to describe the dynamic effective connectivity to distinguish the depression and explore the pathogenesis of depression. In the proposed frame, the anatomical information was converted via an optimized transformation model, which was then integrated into the normal GCM by variational bayesian model. Magnetoencephalography (MEG) signals and diffusion tensor imaging (DTI) of 24 depressive patients and 24 matched controls were utilized for performance comparison. Together with the sliding windowed MEG signals under sad facial stimuli, the enriched GCM was applied to calculate the regional-pair dynamic effective connectivity, which were repeatedly sifted via feature selection and fed into different classifiers. From the aspects of model errors and recognition accuracy rates, results supported the superiority of the enriched GCM with anatomical priors over the normal GCM. For the effective connectivity with anatomical priors, the best subject discrimination accuracy of SVM was 85.42% (the sensitivity was 87.50% and the specificity was 83.33%). Furthermore, discriminative feature analysis suggested that the enriched GCM that detect the variable anatomical constraint on function could better detect more stringent and less dynamic brain function in depression. The proposed approach is valuable in dynamic functional dysfunction exploration in depression and could be useful for depression recognition.

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Estimating Distributed Representations of Evoked Responses and Oscillatory Brain Activity

Cited by 11 publications

References 0 publications

K-th Nearest Neighbor (KNN) Entropy Estimates of Complexity and Integration from Ongoing and Stimulus-Evoked Electroencephalographic (EEG) Recordings of the Human Brain

K-th Nearest Neighbor (KNN) Entropy Estimates of Complexity and Integration from Ongoing and Stimulus-Evoked Electroencephalographic (EEG) Recordings of the Human Brain

Dissociating sensory from decision processes in human perceptual decision making

An enriched granger causal model allowing variable static anatomical constraints

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