Segmentation of brain electrical activity into microstates: model estimation and validation

Pascual-Marqui, Roberto D.; Michel, Christoph M.; Lehmann, Dietrich

doi:10.1109/10.391164

Cited by 828 publications

(799 citation statements)

References 22 publications

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“…The most dominant scalp topographies appearing in the groupaveraged ERPs from each condition over time were found using a Kmeans cluster analysis (Pasqual-Marqui et al, 1995). The optimal number of clusters to describe the data set was identified using a modified Krzanowski-Lai criterion.…”

Section: Eeg Analysesmentioning

confidence: 99%

Task relevance effects in electrophysiological brain activity: Early, but not first

2014

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A current controversy surrounds the question whether high-level features of a stimulus such as its relevance to the current task may affect early attentional processes. According to one view abruptly appearing stimuli gain priority during an initial feedforward processing stage and therefore capture attention even if they are irrelevant to the task. Alternatively, only stimuli that share a relevant property with the target may capture attention of the observer. Here, we used high-density EEG to test whether task relevance may modulate early feedforward brain activity, or whether it only becomes effective once the physical characteristics of the stimulus have been processed. We manipulated task relevance and visual saliency of distracters presented left or right of an upcoming central target. We found that only the relevance of distracters had an effect on manual reaction times to the target. However, the analysis of electrocortical activity revealed three discrete processing stages during which pure effects of distracter saliency (~80-160 ms), followed by an interaction between saliency and relevance (~130-240 ms) and finally pure effects of relevance (~230-370 ms) were observed. Electrical sources of early saliency effects and later relevance effects were localized in the posterior parietal cortex, predominantly over the right hemisphere. These findings support the view that during the initial feedforward stage only physical (bottom-up) factors determine cortical responses to visual stimuli, while top-down effects interfere at later processing stages.

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Section: Eeg Analysesmentioning

confidence: 99%

Task relevance effects in electrophysiological brain activity: Early, but not first

2014

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“…This method is independent of the reference electrode (Michel et al, 2001(Michel et al, , 2004) and insensitive to pure amplitude modulations across conditions (topographies of normalized maps are compared). A modified hierarchical clustering analysis (Michel et al, 2001;Pascual-Marqui, Michel, & Lehmann, 1995), the agglomerative hierarchical clustering (Murray, Brunet, & Michel, 2008) was used to determine the most dominant configurations of the electric field at the scalp (topographic maps). A modified cross-validation criterion was used to determine the optimal number of maps that explained the best the group-averaged data sets across conditions.…”

Section: Topographic Pattern Analysismentioning

confidence: 99%

“…Statistical smoothing was used to eliminate temporally isolated topographic maps with low strength. This procedure is described in detail in Pascual-Marqui et al (1995). Additionally, a given topography had to be present for at least 10 time frames (20 ms).…”

Section: Topographic Pattern Analysismentioning

confidence: 99%

Comparing Electrophysiological Correlates of Word Production in Immediate and Delayed Naming Through the Analysis of Word Age of Acquisition Effects

Laganaro

Perret

2010

Brain Topogr

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Most EEG studies analysing speech production with event related brain potential (ERP) have adopted silent metalinguistic tasks or delayed or tacit picture naming in order to avoid possible artefacts during motor preparation. A central issue in the interpretation of these results is whether the processes involved in those tasks are comparable to those involved in overt speech production. In the present study we addressed a methodological issue about the integration of stimulus-aligned and response-aligned ERPs in immediate overt picture naming in comparison to delayed production, coupled with a theoretical point on the effect of word Age of Acquisition (AoA). High density EEG recordings were used and waveform analyses and spatio-temporal segmentation were combined on stimulus-aligned and response-aligned ERPs. The same sequence and duration of topographic maps appeared in the immediate and delayed production until around 350 ms after picture onset, revealing similar encoding processes until the beginning of phonological encoding, but modulations linked to word AoA were only observed in the immediate production. Considering stimulus-aligned and responsealigned ERPs together allowed to identify that a stable topography starting around 350 ms lasts 30 ms longer for late-acquired than for early-acquired words. This difference falls within the time-window of phonological encoding and its modulation can be linked to the longer production latencies for late-acquired words.3

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“…Thus, we can interpret the eigenconnectivities as building blocks of dynamic FC with a contribution that varies across time. In contrast to the clustering approach employed in electroencephalography (EEG) microstate (Pascual-Marqui et al, 1995) and FC state analysis (Allen et al, in press), where stable EEG scalp topographies or wholebrain FC states are identified, we used PCA to identify the most abundant building blocks that reappear across time and subjects.…”

Section: Introductionmentioning

confidence: 99%

Principal components of functional connectivity: A new approach to study dynamic brain connectivity during rest

et al. 2013

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Functional connectivity (FC) as measured by correlation between fMRI BOLD time courses of distinct brain regions has revealed meaningful organization of spontaneous fluctuations in the resting brain. However, an increasing amount of evidence points to non-stationarity of FC; i.e., FC dynamically changes over time reflecting additional and rich information about brain organization, but representing new challenges for analysis and interpretation. Here, we propose a data-driven approach based on principal component analysis (PCA) to reveal hidden patterns of coherent FC dynamics across multiple subjects. We demonstrate the feasibility and relevance of this new approach by examining the differences in dynamic FC between 13 healthy control subjects and 15 minimally disabled relapse-remitting multiple sclerosis patients. We estimated whole-brain dynamic FC of regionally-averaged BOLD activity using sliding time windows. We then used PCA to identify FC patterns, termed "eigenconnectivities", that reflect meaningful patterns in FC fluctuations. We then assessed the contributions of these patterns to the dynamic FC at any given time point and identified a network of connections centered on the default-mode network with altered contribution in patients. Our results complement traditional stationary analyses, and reveal novel insights into brain connectivity dynamics and their modulation in a neurodegenerative disease.© 2013 Elsevier Inc. All rights reserved. IntroductionSpontaneous fluctuations of the functional MRI (fMRI) bloodoxygen-level-dependent (BOLD) signal are not random but temporally coherent between distinct brain regions. While these fluctuations were long considered as "noise", Biswal et al. (1995) showed that fluctuations of motor areas were correlated even in the absence of a motor task. Several other networks of coherent BOLD activity between remote brain regions have since been identified, including visual, auditory, language and attention networks, and a network called the "default mode network" (DMN) which reduces its activity during attentiondemanding tasks. These networks of regions with coherent activity during rest are consistent across subjects and closely resemble the brain's functional organization of evoked responses (Damoiseaux et al., 2006;Fox and Raichle, 2007;Laird et al., 2011;Smith et al., 2009). Coherent BOLD activity persists during sleep and in anesthetized monkeys, suggesting that it reflects a fundamental property of the brain's functional organization (Larson-Prior et al., 2009;Vincent et al., 2007).Coherent BOLD activity, known as "functional connectivity" (FC), is modulated by learning (Bassett et al., 2011), cognitive and affective states (Cribben et al., 2012;Ekman et al., 2012;Eryilmaz et al., 2011;Richiardi et al., 2011;Shirer et al., 2012) and also spontaneously Chang and Glover, 2010; Kitzbichler et al., 2009). Chang andGlover (2010) showed that FC between the posterior cingulate cortex, a key region of the default mode network, and various other brain regions was highly dy...

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Segmentation of brain electrical activity into microstates: model estimation and validation

Cited by 828 publications

References 22 publications

Task relevance effects in electrophysiological brain activity: Early, but not first

Task relevance effects in electrophysiological brain activity: Early, but not first

Comparing Electrophysiological Correlates of Word Production in Immediate and Delayed Naming Through the Analysis of Word Age of Acquisition Effects

Principal components of functional connectivity: A new approach to study dynamic brain connectivity during rest

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