Voltage Topographies Research Articles

Shared and distinct electroencephalogram microstate abnormalities across schizophrenia, bipolar disorder, and depression.

Microstates of an electroencephalogram (EEG) are canonical voltage topographies that remain quasi-stable for 90 ms, serving as the foundational elements of brain dynamics. Different changes in EEG microstates can be observed in psychiatric disorders like schizophrenia (SCZ), major depressive disorder (MDD), and bipolar disorder (BD). However, the similarities and disparatenesses in whole-brain dynamics on a subsecond timescale among individuals diagnosed with SCZ, BD, and MDD are unclear. This study included 1112 participants (380 individuals diagnosed with SCZ, 330 with BD, 212 with MDD, and 190 demographically matched healthy controls [HCs]). We assembled resting-state EEG data and completed a microstate analysis of all participants using a cross-sectional design. Our research indicates that SCZ, BD, and MDD exhibit distinct patterns of transition among the four EEG microstate states (A, B, C, and D). The analysis of transition probabilities showed a higher frequency of switching from microstates A to B and from B to A in each patient group compared to the HC group, and less frequent transitions from microstates A to C and from C to A in the SCZ and MDD groups compared to the HC group. And the probability of the microstate switching from C to D and D to C in the SCZ group significantly increased compared to those in the patient and HC groups. Our findings provide crucial insights into the abnormalities involved in distributing neural assets and enabling proper transitions between different microstates in patients with major psychiatric disorders.

Changes in electroencephalographic microstates between evening and morning are associated with overnight sleep slow waves in healthy individuals.

Microstates are semi-stable voltage topographies that account for most of electroencephalogram (EEG) variance. However, the impact of time of the day and sleep on microstates has not been examined. To address this gap, we assessed whether microstates differed between the evening and morning and whether sleep slow waves correlated with microstate changes in healthy participants. Forty-five healthy participants were recruited. Each participant underwent 6 minutes of resting state EEG recordings in the evening and morning, interleaved by sleep EEGs. Evening-to-morning changes in microstate duration, coverage, and occurrence were assessed. Furthermore, correlation between microstate changes and sleep slow-wave activity (SWA) and slow-wave density (SWD) were performed. Two-way ANOVAs with microstate class (A, B, C, and D) and time (evening and morning) revealed significant microstate class × time interaction for duration (F(44) = 5.571, p = 0.002), coverage (F(44) = 6.833, p = 0.001), and occurrence (F(44) = 5.715, p = 0.002). Post hoc comparisons showed significant effects for microstate C duration (padj = 0.048, Cohen's d = -0.389), coverage (padj = 0.002, Cohen's d = -0.580), and occurrence (padj = 0.002, Cohen's d = -0.606). Topographic analyses revealed inverse correlations between SWD, but not SWA, and evening-to-morning changes in microstate C duration (r = -0.51, padj = 0.002), coverage (r = -0.45, padj = 0.006), and occurrence (r = -0.38, padj = 0.033). Microstate characteristics showed significant evening-to-morning changes associated with, and possibly regulated by, sleep slow waves. These findings suggest that future microstate studies should control for time of day and sleep effects.

Effects of modafinil on electroencephalographic microstates in healthy adults.

Modafinil has been proposed as a potentially effective clinical treatment for neuropsychiatric disorders characterized by cognitive control deficits. However, the precise effects of modafinil, particularly on brain network functions, are not completely understood. To address this gap, we examined the effects of modafinil on resting-state brain activity in 30 healthy adults using microstate analysis. Electroencephalographic (EEG) microstates are discrete voltage topographies generated from resting-state network activity. Using a placebo-controlled, within-subjects design, we examined changes to microstate parameters following placebo (0mg), low (100mg), and high (200mg) modafinil doses. We also examined the functional significance of these microstates via associations between microstate parameters and event-related potential indexes of conflict monitoring and automatic error processing (N2 and error-related negativity) and behavioral responses (accuracy and RT) from a subsequent flanker interference task. Five microstates emerged following each treatment condition, including four canonical microstates (A-D). Modafinil increased microstate C proportion and occurrence regardless of dose, relative to placebo. Modafinil also decreased microstate A proportion and microstate B proportion and occurrence relative to placebo. These modafinil-related changes in microstate parameters were not associated with similar changes in flanker ERPs or behavior. Finally, modafinil made transitions between microstates A and B less likely and transitions from A and B to C more likely. Previous fMRI work has correlated microstates A and B with auditory and visual networks and microstate C with a salience network. Thus, our results suggest modafinil may deactivate large-scale sensory networks in favor of a higher order functional network during resting-state in healthy adults.

EEG microstates suggest atypical resting-state network activity in high-functioning children and adolescents with autism spectrum development.

EEG microstates represent transient electrocortical events that reflect synchronized activities of large-scale networks, which allows investigations of brain dynamics with sub-second resolution. We recorded resting EEG from 38 children and adolescents with Autism Spectrum Development (ASD) and 48 age, IQ, sex, and handedness-matched typically developing (TD) participants. The EEG was segmented into a time series of microstates using modified k-means clustering of scalp voltage topographies. The frequency and global explained variance (GEV) of a specific microstate (type C) were significantly lower in the ASD group compared to the TD group while the duration of the same microstate was correlated with the presence of ASD-related behaviors. The duration of this microstate was also positively correlated with participant age in the TD group, but not in the ASD group. Further, the frequency and duration of the microstate were significantly correlated with the overall alpha power only in the TD group. The signal strength and GEV for another microstate (type G) was greater in the ASD group than the TD group, and the associated topographical pattern differed between groups with greater variations in the ASD group. While more work is needed to clarify the underlying neural sources, the existing literature supports associations between the two microstates and the default mode and salience networks. The current study suggests specific alterations of temporal dynamics of the resting cortical network activities as well as their developmental trajectories and relationships to alpha power, which has been proposed to reflect reduced neural inhibition in ASD.

Distinct multisensory perceptual processes guide enhanced auditory recognition memory in older cochlear implant users

In naturalistic situations, sounds are often perceived in conjunction with matching visual impressions. For example, we see and hear the neighbor’s dog barking in the garden. Still, there is a good chance that we recognize the neighbor’s dog even when we only hear it barking, but do not see it behind the fence. Previous studies with normal-hearing (NH) listeners have shown that the audio-visual presentation of a perceptual object (like an animal) increases the probability to recognize this object later on, even if the repeated presentation of this object occurs in a purely auditory condition. In patients with a cochlear implant (CI), however, the electrical hearing of sounds is impoverished, and the ability to recognize perceptual objects in auditory conditions is significantly limited. It is currently not well understood whether CI users – as NH listeners – show a multisensory facilitation for auditory recognition. The present study used event-related potentials (ERPs) and a continuous recognition paradigm with auditory and audio-visual stimuli to test the prediction that CI users show a benefit from audio-visual perception. Indeed, the congruent audio-visual context resulted in an improved recognition ability of objects in an auditory-only condition, both in the NH listeners and the CI users. The ERPs revealed a group-specific pattern of voltage topographies and correlations between these ERP maps and the auditory recognition ability, indicating a different processing of congruent audio-visual stimuli in CI users when compared to NH listeners. Taken together, our results point to distinct cortical processing of naturalistic audio-visual objects in CI users and NH listeners, which however allows both groups to improve the recognition ability of these objects in a purely auditory context. Our findings are of relevance for future clinical research since audio-visual perception might also improve the auditory rehabilitation after cochlear implantation.

Large-Scale EEG Neural Network Changes in Response to Therapeutic TMS

Abstract Transcranial magnetic stimulation (TMS) likely induces functional connectivity changes in aberrant circuits implicated in depression. Electroencephalography (EEG) “microstates” are voltage topographies hypothesized to represent large-scale resting networks. Canonical microstates have recently been proposed as markers for major depressive disorder (MDD), but it is not known if or how they change following TMS. Resting EEG was obtained from 49 patients with MDD before and after six weeks of daily TMS. Polarity-insensitive modified k-means clustering was used to segment EEGs into constituent microstates. Microstates were localized via sLORETA. Microstate changes over time, and between TMS responder and non-responder groups, were compared with t-tests. Six microstates (MS1-MS6) were identified. Response to TMS was associated with an increase in occurrence and coverage of MS 2, and decreased occurrence and coverage of MS 3. Non-responders showed no significant changes in any microstate. Changes in both MS 2 and MS 3 occurrence and coverage correlated with symptom change. Localization of MS2 suggests that TMS may upregulate the positive affect “reward” network (improvement in anhedonia) in responders while MS3 localization may represent diminished inhibitory activity of the frontoparietal network, possibly related to emergence from the cognitive “fog” of depression. Keywords: EEG, TMS, Microstates, Networks

Differences in Intersubject Early Readiness Potentials Between Voluntary and Instructed Actions.

Readiness potential (RP) is a slow negative electroencephalogram (EEG) potential prior to voluntary action and was first described by Kornhuber and Deecke (1965). Recent studies have demonstrated that a few subjects do not exhibit standard RP before voluntary action. In our previous study, we also found that some subjects did not show an early RP preceding instructed action. Although this phenomenon may be meaningful, no studies have yet investigated its origins. In the present study, we designed and implemented an experimental paradigm involving voluntary and instructed actions in the form of hand movements from 29 subjects with concurrent acquisition of EEGs. According to whether the subjects showed a standard RP waveform during instructed action, they were divided into the SHOW and NOSHOW group. Then, the RPs and voltage topographies were plotted for each group. Finally, the slope of each epoch at the early RP phase was estimated. We showed that early RPs were absent in 14 of 29 subjects during instructed actions. Besides, based on the slow cortical potential (SCP) sampling hypothesis, we also showed a decreased proportion in the negative potential for the NOSHOW group. Our results suggested that early RP is absent among approximately half of subjects during instructed action and that the decreased proportion of negative potential shifts may account for the absence of early RP in the NOSHOW group.

Abnormalities in electroencephalographic microstates are state and trait markers of major depressive disorder.

Neuroimaging studies have shown that major depressive disorder (MDD) is characterized by abnormal neural activity and connectivity. However, hemodynamic imaging techniques lack the temporal resolution needed to resolve the dynamics of brain mechanisms underlying MDD. Moreover, it is unclear whether putative abnormalities persist after remission. To address these gaps, we used microstate analysis to study resting-state brain activity in major depressive disorder (MDD). Electroencephalographic (EEG) "microstates" are canonical voltage topographies that reflect brief activations of components of resting-state brain networks. We used polarity-insensitive k-means clustering to segment resting-state high-density (128-channel) EEG data into microstates. Data from 79 healthy controls (HC), 63 individuals with MDD, and 30 individuals with remitted MDD (rMDD) were included. The groups produced similar sets of five microstates, including four widely-reported canonical microstates (A-D). The proportion of microstate D was decreased in MDD and rMDD compared to the HC group (Cohen's d = 0.63 and 0.72, respectively) and the duration and occurrence of microstate D was reduced in the MDD group compared to the HC group (Cohen's d = 0.43 and 0.58, respectively). Among the MDD group, proportion and duration of microstate D were negatively correlated with symptom severity (Spearman's rho = -0.34 and -0.46, respectively). Finally, microstate transition probabilities were nonrandom and the MDD group, relative to the HC and the rMDD groups, exhibited multiple distinct transition probabilities, primarily involving microstates A and C. Our findings highlight both state and trait abnormalities in resting-state brain activity in MDD.

How Electroencephalogram Reference Influences the Movement Readiness Potential?

Readiness potential (RP) based on electroencephalograms (EEG) has been studied extensively in recent years, but no studies have investigated the influence of the reference electrode on RP. In order to investigate the reference effect, 10 subjects were recruited and the original vertex reference (Cz) was used to record the raw EEG signal when the subjects performed a motor preparation task. The EEG was then transformed to the common average reference (CAR) and reference electrode standardization technique (REST) reference, and we analyzed the RP waveform and voltage topographies and calculated the classification accuracy of idle and RP EEG segments. Our results showed that the RP waveform and voltage topographies were greatly influenced by the reference, but the classification accuracy was less affected if proper channels were selected as features. Since the Cz channel is near the primary motor cortex, where the source of RP is located, using the REST and CAR references is recommended to get accurate RP waveforms and voltage topographies.

Quantifying the time for accurate EEG decoding of single value-based decisions

BACKGROUNDRecent neuroimaging studies suggest that value-based decision-making may rely on mechanisms of evidence accumulation. However no studies have explicitly investigated the time when single decisions are taken based on such an accumulation process. NEW METHODHere, we outline a novel electroencephalography (EEG) decoding technique which is based on accumulating the probability of appearance of prototypical voltage topographies and can be used for predicting subjects’ decisions. We use this approach for studying the time-course of single decisions, during a task where subjects were asked to compare reward vs. loss points for accepting or rejecting offers. RESULTSWe show that based on this new method, we can accurately decode decisions for the majority of the subjects. The typical time-period for accurate decoding was modulated by task difficulty on a trial-by-trial basis. Typical latencies of when decisions are made were detected at ∼500ms for ‘easy’ vs. ∼700ms for ‘hard’ decisions, well before subjects’ response (∼340ms). Importantly, this decision time correlated with the drift rates of a diffusion model, evaluated independently at the behavioral level. COMPARISON WITH EXISTING METHOD(S)We compare the performance of our algorithm with logistic regression and support vector machine and show that we obtain significant results for a higher number of subjects than with these two approaches. We also carry out analyses at the average event-related potential level, for comparison with previous studies on decision-making. ConclusionsWe present a novel approach for studying the timing of value-based decision-making, by accumulating patterns of topographic EEG activity at single-trial level.

Non-parametric statistical analysis of voltage topographies and source images on the epoch level

Non-parametric statistical analysis of voltage topographies and source images on the epoch level

Synthetic event-related potentials: A computational bridge between neurolinguistic models and experiments

Our previous work developed Synthetic Brain Imaging to link neural and schema network models of cognition and behavior to PET and fMRI studies of brain function. We here extend this approach to Synthetic Event-Related Potentials (Synthetic ERP). Although the method is of general applicability, we focus on ERP correlates of language processing in the human brain. The method has two components: Phase 1: To generate cortical electro-magnetic source activity from neural or schema network models; and Phase 2: To generate known neurolinguistic ERP data (ERP scalp voltage topographies and waveforms) from putative cortical source distributions and activities within a realistic anatomical model of the human brain and head. To illustrate the challenges of Phase 2 of the methodology, spatiotemporal information from Friederici’s 2002 model of auditory language comprehension was used to define cortical regions and time courses of activation for implementation within a forward model of ERP data. The cortical regions from the 2002 model were modeled using atlas-based masks overlaid on the MNI high definition single subject cortical mesh. The electromagnetic contribution of each region was modeled using current dipoles whose position and orientation were constrained by the cortical geometry. In linking neural network computation via EEG forward modeling to empirical results in neurolinguistics, we emphasize the need for neural network models to link their architecture to geometrically sound models of the cortical surface, and the need for conceptual models to refine and adopt brain-atlas based approaches to allow precise brain anchoring of their modules. The detailed analysis of Phase 2 sets the stage for a brief introduction to Phase 1 of the program, including the case for a schema-theoretic approach to language production and perception presented in detail elsewhere. Unlike Dynamic Causal Modeling (DCM) and Bojak’s mean field model, Synthetic ERP builds on models of networks that mediate the relation between the brain’s inputs, outputs, and internal states in executing a specific task. The neural networks used for Synthetic ERP must include neuroanatomically realistic placement and orientation of the cortical pyramidal neurons. These constraints pose exciting challenges for future work in neural network modeling that is applicable to systems and cognitive neuroscience.

Auditory perceptual decision-making based on semantic categorization of environmental sounds

Discriminating complex sounds relies on multiple stages of differential brain activity. The specific roles of these stages and their links to perception were the focus of the present study. We presented 250 ms duration sounds of living and man-made objects while recording 160-channel electroencephalography (EEG). Subjects categorized each sound as that of a living, man-made or unknown item. We tested whether/when the brain discriminates between sound categories even when not transpiring behaviorally. We applied a single-trial classifier that identified voltage topographies and latencies at which brain responses are most discriminative. For sounds that the subjects could not categorize, we could successfully decode the semantic category based on differences in voltage topographies during the 116-174 ms post-stimulus period. Sounds that were correctly categorized as that of a living or man-made item by the same subjects exhibited two periods of differences in voltage topographies at the single-trial level. Subjects exhibited differential activity before the sound ended (starting at 112 ms) and on a separate period at ~270 ms post-stimulus onset. Because each of these periods could be used to reliably decode semantic categories, we interpreted the first as being related to an implicit tuning for sound representations and the second as being linked to perceptual decision-making processes. Collectively, our results show that the brain discriminates environmental sounds during early stages and independently of behavioral proficiency and that explicit sound categorization requires a subsequent processing stage.

The timing of exploratory decision-making revealed by single-trial topographic EEGanalyses

Decision-making in an uncertain environment is driven by two major needs: exploring the environment to gather information or exploiting acquired knowledge to maximize reward. The neural processes underlying exploratory decision-making have been mainly studied by means of functional magnetic resonance imaging, overlooking any information about the time when decisions are made. Here, we carried out an electroencephalography (EEG) experiment, in order to detect the time when the brain generators responsible for these decisions have been sufficiently activated to lead to the next decision. Our analyses, based on a classification scheme, extract time-unlocked voltage topographies during reward presentation and use them to predict the type of decisions made on the subsequent trial. Classification accuracy, measured as the area under the Receiver Operator's Characteristic curve was on average 0.65 across 7 subjects. Classification accuracy was above chance levels already after 516ms on average, across subjects. We speculate that decisions were already made before this critical period, as confirmed by a positive correlation with reaction times across subjects. On an individual subject basis, distributed source estimations were performed on the extracted topographies to statistically evaluate the neural correlates of decision-making. For trials leading to exploration, there was significantly higher activity in dorsolateral prefrontal cortex and the right supramarginal gyrus; areas responsible for modulating behavior under risk and deduction. No area was more active during exploitation. We show for the first time the temporal evolution of differential patterns of brain activation in an exploratory decision-making task on a single-trial basis.

Decoding stimulus-related information from single-trial EEG responses based on voltage topographies

Neuroimaging studies typically compare experimental conditions using average brain responses, thereby overlooking the stimulus-related information conveyed by distributed spatio-temporal patterns of single-trial responses. Here, we take advantage of this rich information at a single-trial level to decode stimulus-related signals in two event-related potential (ERP) studies. Our method models the statistical distribution of the voltage topographies with a Gaussian Mixture Model (GMM), which reduces the dataset to a number of representative voltage topographies. The degree of presence of these topographies across trials at specific latencies is then used to classify experimental conditions. We tested the algorithm using a cross-validation procedure in two independent EEG datasets. In the first ERP study, we classified left- versus right-hemifield checkerboard stimuli for upper and lower visual hemifields. In a second ERP study, when functional differences cannot be assumed, we classified initial versus repeated presentations of visual objects. With minimal a priori information, the GMM model provides neurophysiologically interpretable features - vis a vis voltage topographies - as well as dynamic information about brain function. This method can in principle be applied to any ERP dataset testing the functional relevance of specific time periods for stimulus processing, the predictability of subject's behavior and cognitive states, and the discrimination between healthy and clinical populations.

Perceptual decision making of environmental sounds

Event Abstract Back to Event Perceptual decision making of environmental sounds Marzia De Lucia1*, Athina Tzovara1, 2, Fosco Bernasconi3, Lucas Spierer3 and Micah M. Murray3 1 Center for Biomedical Imaging, Switzerland 2 University of Lausanne, Switzerland 3 Neuropsychology and Neurorehabilitation Service, Department of Clinical Neurosciences, University Hospital, Switzerland Discriminating complex sounds relies on multiple stages of differential brain activity. The specific roles of these stages and their links to perception remain unknown and were the focus of the present study. We presented sounds of living and man-made objects while recording 160-channel electroencephalography (EEG). Subjects categorized each sound as a living, man-made or unknown object. We tested whether/when the brain discriminates between sound categories even when not transpiring behaviorally. We applied a single-trial EEG classifier that identified voltage topographies and latencies at which responses are most discriminative. For sounds that the subjects could not identify, we obtained differences in voltage topographies along the 111-180ms post stimulus period and an average classification accuracy of 66%. Sounds that were correctly recognized as living or man-made by the same cohort of subjects exhibited at least two periods of differences in voltage topographies at the single-trial level. All subjects exhibited differential activity before the sound ended and a second starting at ~280ms post-stimulus onset providing 63% and 65% average accuracy respectively. Because each of these two periods could be used to reliably decode semantic categories, we interpreted the first as being related to an implicit tuning for object representations and the second linked to perceptual decision-making processes. Collectively our results show that the brain discriminates complex sounds during early stages and independently of behavioral proficiency and that explicit sound identification relies on a subsequent stage of differential activity. Keywords: decision-making, EEG Conference: XI International Conference on Cognitive Neuroscience (ICON XI), Palma, Mallorca, Spain, 25 Sep - 29 Sep, 2011. Presentation Type: Poster Presentation Topic: Poster Sessions: Decision Making, Reward Processing & Response Selection Citation: De Lucia M, Tzovara A, Bernasconi F, Spierer L and Murray MM (2011). Perceptual decision making of environmental sounds. Conference Abstract: XI International Conference on Cognitive Neuroscience (ICON XI). doi: 10.3389/conf.fnhum.2011.207.00370 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 23 Nov 2011; Published Online: 28 Nov 2011. * Correspondence: Dr. Marzia De Lucia, Center for Biomedical Imaging, Lausanne, Switzerland, marzia.de-lucia@chuv.ch Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Marzia De Lucia Athina Tzovara Fosco Bernasconi Lucas Spierer Micah M Murray Google Marzia De Lucia Athina Tzovara Fosco Bernasconi Lucas Spierer Micah M Murray Google Scholar Marzia De Lucia Athina Tzovara Fosco Bernasconi Lucas Spierer Micah M Murray PubMed Marzia De Lucia Athina Tzovara Fosco Bernasconi Lucas Spierer Micah M Murray Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.

Prediction of exploratory decision-making from single-trial topographic EEG analyses

Event Abstract Back to Event Prediction of exploratory decision-making from single-trial topographic EEG analyses Athina Tzovara1, 2*, Micah M. Murray1, 2, Nicolas Bourdaud3, Ricardo Chavarriaga3, José Del R. Millán3 and Marzia De Lucia1, 2 1 Center for Biomedical Imaging, University Hospital, Switzerland 2 University of Lausanne, Switzerland 3 Chair on Non-Invasive Brain-Computer Interface, Ecole Polytechnique Fédéral de Lausanne, Switzerland Decision-making in an uncertain environment is driven by two major needs: exploring the environment to gather information or exploiting acquired knowledge to maximize reward. The processes underlying exploratory decision-making have been mainly studied by means of functional magnetic resonance imaging leaving their temporal aspects under-explored. Here, using a well-known gambling paradigm in reinforcement learning we aim at detecting when in time, at the single-trial level, the brain generators responsible for these decisions have been sufficiently activated to lead to the following decision. Our analyses, following a classification scheme, are based on extracting time-unlocked voltage topographies during reward presentation and using them to predict decisions made on the next trial. Classification accuracy, measured as the area under the Receiver Operator’s Characteristic curve was on average across 7 subjects 0.65±0.02. The crucial time period, over which enough evidence was accumulated to accurately make this prediction, started from the display of the previous reward and lasted on average across trials and subjects up to 570±18 ms post-stimulus. On an individual subject basis, distributed source estimations were performed on the extracted topographies to statistically evaluate the neural correlates of decision making. For trials leading to exploration, there was significantly higher activity in dorsolateral prefrontal cortex and in the right parietal supramarginal gyrus; areas responsible for task-switching and modulating behavior under risk and deduction. No area was more active during exploitation. We show for the first time the temporal evolution of differential patterns of brain activation in an exploratory decision-making task on a single-trial basis. Keywords: decision-making, EEG Conference: XI International Conference on Cognitive Neuroscience (ICON XI), Palma, Mallorca, Spain, 25 Sep - 29 Sep, 2011. Presentation Type: Poster Presentation Topic: Poster Sessions: Decision Making, Reward Processing & Response Selection Citation: Tzovara A, Murray MM, Bourdaud N, Chavarriaga R, Millán J and De Lucia M (2011). Prediction of exploratory decision-making from single-trial topographic EEG analyses. Conference Abstract: XI International Conference on Cognitive Neuroscience (ICON XI). doi: 10.3389/conf.fnhum.2011.207.00371 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 23 Nov 2011; Published Online: 28 Nov 2011. * Correspondence: Dr. Athina Tzovara, Center for Biomedical Imaging, University Hospital, Lausanne, Switzerland, athina.tz@gmail.com Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Athina Tzovara Micah M Murray Nicolas Bourdaud Ricardo Chavarriaga José Del R. Millán Marzia De Lucia Google Athina Tzovara Micah M Murray Nicolas Bourdaud Ricardo Chavarriaga José Del R. Millán Marzia De Lucia Google Scholar Athina Tzovara Micah M Murray Nicolas Bourdaud Ricardo Chavarriaga José Del R. Millán Marzia De Lucia PubMed Athina Tzovara Micah M Murray Nicolas Bourdaud Ricardo Chavarriaga José Del R. Millán Marzia De Lucia Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.

Non-retinotopic feature integration decreases response-locked brain activity as revealed by electrical neuroimaging

When presented with dynamic scenes, the brain integrates visual elements across space and time. Such non-retinotopic processing has been intensively studied from a psychophysical point of view, but little is known about the underlying neural processes. Here we used high-density EEG to reveal neural correlates of non-retinotopic feature integration. In an offset-discrimination task we presented sequences of lines for which feature integration depended on a small, endogenous shift of attention. Attention effects were observed in the stimulus-locked evoked potentials but non-retinotopic feature integration was reflected in voltage topographies time-locked to the behavioral response, lasting for about 400 ms. Statistical parametric mapping of estimated current densities revealed that this integration reduced electrical activity in an extensive network of brain areas, with the effects progressing from high-level visual, via frontal, to central ones. The results suggest that endogenously timed neural processes, rather than bottom-up ones, underlie non-retinotopic feature integration.

Distinct Cerebral Pathways for Object Identity and Number in Human Infants

All humans, regardless of their culture and education, possess an intuitive understanding of number. Behavioural evidence suggests that numerical competence may be present early on in infancy. Here, we present brain-imaging evidence for distinct cerebral coding of number and object identity in 3-mo-old infants. We compared the visual event-related potentials evoked by unforeseen changes either in the identity of objects forming a set, or in the cardinal of this set. In adults and 4-y-old children, number sense relies on a dorsal system of bilateral intraparietal areas, different from the ventral occipitotemporal system sensitive to object identity. Scalp voltage topographies and cortical source modelling revealed a similar distinction in 3-mo-olds, with changes in object identity activating ventral temporal areas, whereas changes in number involved an additional right parietoprefrontal network. These results underscore the developmental continuity of number sense by pointing to early functional biases in brain organization that may channel subsequent learning to restricted brain areas.

Brain electrical tomography (BET) analysis of induced gamma band responses during a simple object recognition task

The formation of cortical object representations requires the activation of cell assemblies, correlated by induced oscillatory bursts above 20 Hz (gamma band), which are characterized by trial-by-trial latency fluctuations around a mean of approximately 300 ms after stimulus onset. The present electroencephalogram (EEG) study was intended to uncover to the generators of induced gamma band responses (GBRs) and to analyze phase-synchronization between these sources. A standard object recognition task was used to elicit gamma activity. At the scalp surface (electrode space), we found an augmentation of induced GBRs after the presentation of meaningful (familiar) as opposed to meaningless (unfamiliar) stimuli, which was accompanied by a dense pattern of significant phase-locking values between distant recording sites. Subsequently, intracranial current density distributions compatible with the observed scalp voltage topographies were estimated by means of VARETA (Variable Resolution Electromagnetic Tomography). In source space brain electrical tomographies (BETs) revealed widespread generators of induced GBRs at temporal, parietal, posterior, and frontal areas. Phase-locking analysis was calculated between re-constructed electrode signals based on separate forward solutions of the observed generators, thereby eliminating the possibly confounding influence of activity from areas not under observation. The results support the view that induced GBRs signify synchronous neuronal activity in a broadly distributed network during object recognition. The localization of the generators of event-related potentials (ERPs), evoked gamma activity, and induced alpha activity revealed different sources as compared to the induced GBR and, thus, seem to mirror complementary functions during the present task as compared to induced high-frequency brain dynamics.