Segregation and Integration of Cortical Information Processing Underlying Cross-Modal Perception

2018 ◽  
Vol 31 (5) ◽  
pp. 481-500 ◽  
Author(s):  
G. Vinodh Kumar ◽  
Neeraj Kumar ◽  
Dipanjan Roy ◽  
Arpan Banerjee
Keyword(s):  
Information Processing ◽  
Visual Cues ◽  
Large Scale ◽  
Brain Network ◽  
Stimulus Onset ◽  
Event Related Potential ◽  
Mcgurk Effect ◽  
Neural Representation ◽  
Representational Space ◽  
Spatially Distributed

Visual cues from the speaker’s face influence the perception of speech. An example of this influence is demonstrated by the McGurk-effect where illusory (cross-modal) sounds are perceived following presentation of incongruent audio–visual (AV) stimuli. Previous studies report the engagement of specific cortical modules that are spatially distributed during cross-modal perception. However, the limits of the underlying representational space and the cortical network mechanisms remain unclear. In this combined psychophysical and electroencephalography (EEG) study, the participants reported their perception while listening to a set of synchronous and asynchronous incongruent AV stimuli. We identified the neural representation of subjective cross-modal perception at different organizational levels — at specific locations in sensor space and at the level of the large-scale brain network estimated from between-sensor interactions. We identified an enhanced positivity in the event-related potential peak around 300 ms following stimulus onset associated with cross-modal perception. At the spectral level, cross-modal perception involved an overall decrease in power at the frontal and temporal regions at multiple frequency bands and at all AV lags, along with an increased power at the occipital scalp region for synchronous AV stimuli. At the level of large-scale neuronal networks, enhanced functional connectivity at the gamma band involving frontal regions serves as a marker of AV integration. Thus, we report in one single study that segregation of information processing at individual brain locations and integration of information over candidate brain networks underlie multisensory speech perception.

Neuronal Synchronization and Selective Color Processing in the Human Brain

2004 ◽  
Vol 16 (3) ◽  
pp. 503-522 ◽  
Author(s):  
Matthias M. Müller ◽  
Andreas Keil
Keyword(s):  
Large Scale ◽  
Time Course ◽  
Time Window ◽  
Brain Activity ◽  
Stimulus Onset ◽  
Gamma Band ◽  
Event Related Potential ◽  
Alpha Band ◽  
Color Processing ◽  
The Time Domain

In the present study, subjects selectively attended to the color of checkerboards in a feature-based attention paradigm. Induced gamma band responses (GBRs), the induced alpha band, and the event-related potential (ERP) were analyzed to uncover neuronal dynamics during selective feature processing. Replicating previous ERP findings, the selection negativity (SN) with a latency of about 160 msec was extracted. Furthermore, and similarly to previous EEG studies, a gamma band peak in a time window between 290 and 380 msec was found. This peak had its major energy in the 55to 70-Hz range and was significantly larger for the attended color. Contrary to previous human induced gamma band studies, a much earlier 40to 50-Hz peak in a time window between 160 and 220 msec after stimulus onset and, thus, concurrently to the SN was prominent with significantly more energy for attended as opposed to unattended color. The induced alpha band (9.8–11.7 Hz), on the other hand, exhibited a marked suppression for attended color in a time window between 450 and 600 msec after stimulus onset. A comparison of the time course of the 40to 50-Hz and 55to 70-Hz induced GBR, the induced alpha band, and the ERP revealed temporal coincidences for changes in the morphology of these brain responses. Despite these similarities in the time domain, the cortical source configuration was found to discriminate between induced GBRs and the SN. Our results suggest that large-scale synchronous high-frequency brain activity as measured in the human GBR play a specific role in attentive processing of stimulus features.

scholarly journals Homeostatic brain network mechanisms facilitate perceptual binding of audio-visual speech

2020 ◽  
Author(s):  
Abhishek Mukherjee ◽  
Soibam Shyamchand Singh ◽  
Dipanjan Ray ◽  
Partha Raghunathan ◽  
Arpan Banerjee
Keyword(s):  
Information Processing ◽  
Visual Cues ◽  
Predictive Coding ◽  
Brain Regions ◽  
Brain Network ◽  
Visual Speech ◽  
Daily Lives ◽  
Speech Stimuli ◽  
Illusory Perception ◽  
Brain Functional Connectivity

In daily lives, speech perception requires binding of spatiotemporally disjoint auditory and visual cues. On the other hand, functional segregation and integration are the two complementary mechanisms that capture brain information processing. Here, we demonstrate using fMRI recordings that subjective perceptual experience of multisensory speech stimuli is dependent on a homeostatic balance of segregation and integration mechanisms. Previous reports conceptualized posterior superior temporal sulcus as the key brain region for binding signals from multiple sensory streams. However, we report an enhancement of segregated information processing in distributed brain regions, defined as the perceptual binding network. The seed-based whole brain functional connectivity of each node in this network was anti-correlated with higher propensity for illusory perception. Interestingly, the perceptual binding network was anti-correlated with other intrinsic brain networks, such as dorsal attention and default mode networks during cross-modal perception. The pattern disappeared for people who rarely reported the illusory perception, further strengthening the hypothesis of homeostatic balance. The cognitive theories of Bayesian causal inference and predictive coding hypothesis could explain the balance of segregative and integrative mechanisms during cross-modal perception.

scholarly journals Transitions in brain-network level information processing dynamics are driven by alterations in neural gain

2019 ◽  
Author(s):  
Mike Li ◽  
Yinuo Han ◽  
Matthew J. Aburn ◽  
Michael Breakspear ◽  
Russell A. Poldrack ◽  
...  
Keyword(s):  
Information Processing ◽  
Information Transfer ◽  
Large Scale ◽  
Functional Integration ◽  
Brain Network ◽  
Information Storage ◽  
Transfer Entropy ◽  
Arousal System ◽  
Neural Gain ◽  
The Brain

AbstractA key component of the flexibility and complexity of the brain is its ability to dynamically adapt its functional network structure between integrated and segregated brain states depending on the demands of different cognitive tasks. Integrated states are prevalent when performing tasks of high complexity, such as maintaining items in working memory, consistent with models of a global workspace architecture. Recent work has suggested that the balance between integration and segregation is under the control of ascending neuromodulatory systems, such as the noradrenergic system. In a previous large-scale nonlinear oscillator model of neuronal network dynamics, we showed that manipulating neural gain led to a ‘critical’ transition in phase synchrony that was associated with a shift from segregated to integrated topology, thus confirming our original prediction. In this study, we advance these results by demonstrating that the gain-mediated phase transition is characterized by a shift in the underlying dynamics of neural information processing. Specifically, the dynamics of the subcritical (segregated) regime are dominated by information storage, whereas the supercritical (integrated) regime is associated with increased information transfer (measured via transfer entropy). Operating near to the critical regime with respect to modulating neural gain would thus appear to provide computational advantages, offering flexibility in the information processing that can be performed with only subtle changes in gain control. Our results thus link studies of whole-brain network topology and the ascending arousal system with information processing dynamics, and suggest that the constraints imposed by the ascending arousal system constrain low-dimensional modes of information processing within the brain.Author summaryHigher brain function relies on a dynamic balance between functional integration and segregation. Previous work has shown that this balance is mediated in part by alterations in neural gain, which are thought to relate to projections from ascending neuromodulatory nuclei, such as the locus coeruleus. Here, we extend this work by demonstrating that the modulation of neural gain alters the information processing dynamics of the neural components of a biophysical neural model. Specifically, we find that low levels of neural gain are characterized by high Active Information Storage, whereas higher levels of neural gain are associated with an increase in inter-regional Transfer Entropy. Our results suggest that the modulation of neural gain via the ascending arousal system may fundamentally alter the information processing mode of the brain, which in turn has important implications for understanding the biophysical basis of cognition.

scholarly journals Decreased integration of EEG source-space networks in disorders of consciousness

2018 ◽  
Author(s):  
Jennifer Rizkallah ◽  
Jitka Annen ◽  
Julien Modolo ◽  
Olivia Gosseries ◽  
Pascal Benquet ◽  
...  
Keyword(s):  
Information Processing ◽  
Large Scale ◽  
Brain Networks ◽  
Brain Network ◽  
High Density ◽  
Disorders Of Consciousness ◽  
Functional Brain ◽  
Space Networks ◽  
Functional Brain Networks ◽  
Processing Network

AbstractIncreasing evidence links disorders of consciousness (DOC) with disruptions in functional connectivity between distant brain areas. However, to which extent the balance of brain network segregation and integration is modified in DOC patients remains unclear. Using high-density electroencephalography (EEG), the objective of our study was to characterize the local and global topological changes of DOC patients’ functional brain networks.Resting state high-density-EEG data were collected and analyzed from 82 participants: 61 DOC patients recovering from coma with various levels of consciousness (EMCS (n=6), MCS+ (n=29), MCS- (n=17) and UWS (n=9)), and 21 healthy subjects (i.e., controls). Functional brain networks in five different EEG frequency bands and the broadband signal were estimated using an EEG connectivity approach at the source level. Graph theory-based analyses were used to evaluate group differences between healthy volunteers and patient groups.Results showed that networks in DOC patients are characterized by impaired global information processing (network integration) and increased local information processing (network segregation) as compared to controls. The large-scale functional brain networks had integration decreasing with lower level of consciousness.

scholarly journals Biophysical mechanisms governing large-scale brain network dynamics underlying individual-specific variability of perception

2019 ◽  
Author(s):  
G. Vinodh Kumar ◽  
Shrey Dutta ◽  
Siddharth Talwar ◽  
Dipanjan Roy ◽  
Arpan Banerjee
Keyword(s):  
Large Scale ◽  
Realistic Model ◽  
Brain Network ◽  
Individual Variability ◽  
Emergent Behavior ◽  
Mcgurk Effect ◽  
Biophysical Model ◽  
Alpha Band ◽  
Global Coherence ◽  
Large Scale Networks

AbstractPerception necessitates interaction amongst neuronal ensembles, the dynamics of which can be conceptualized as the emergent behavior of coupled dynamical systems. Here, we propose a detailed neurobiologically realistic model that captures the neural mechanisms of inter-individual variability observed in cross-modal speech perception. From raw EEG signals recorded from human participants when they were presented with speech vocalizations of McGurk-incongruent and congruent audio-visual (AV) stimuli, we computed the global coherence metric to capture the neural variability of large-scale networks. We identified that participants’ McGurk susceptibility was negatively correlated to their alpha-band global coherence. The proposed biophysical model conceptualized the global coherence dynamics emerge from coupling between the interacting neural masses - representing the sensory specific auditory/visual areas and modality non-specific associative/integrative regions. Subsequently, we could predict that an extremely weak direct AV coupling result in a decrease in alpha band global coherence - mimicking the cortical dynamics of participants with higher McGurk susceptibility. Source connectivity analysis also showed decreased connectivity between sensory specific regions in participants more susceptible to McGurk effect, thus establishing an empirical validation to the prediction. Overall, our study provides an outline to link variability in structural and functional connectivity metrics to variability of performance that can be useful for several perception & action task paradigms.

A new method to predict anomaly in brain network based on graph deep learning

2020 ◽  
Vol 31 (6) ◽  
pp. 681-689
Author(s):  
Jalal Mirakhorli ◽  
Hamidreza Amindavar ◽  
Mojgan Mirakhorli
Keyword(s):  
Deep Learning ◽  
Large Scale ◽  
Brain Plasticity ◽  
Brain Network ◽  
High Order ◽  
Brain Diseases ◽  
Simultaneous Occurrence ◽  
Generative Adversarial Network ◽  
The Brain ◽  
Brain Connections

AbstractFunctional magnetic resonance imaging a neuroimaging technique which is used in brain disorders and dysfunction studies, has been improved in recent years by mapping the topology of the brain connections, named connectopic mapping. Based on the fact that healthy and unhealthy brain regions and functions differ slightly, studying the complex topology of the functional and structural networks in the human brain is too complicated considering the growth of evaluation measures. One of the applications of irregular graph deep learning is to analyze the human cognitive functions related to the gene expression and related distributed spatial patterns. Since a variety of brain solutions can be dynamically held in the neuronal networks of the brain with different activity patterns and functional connectivity, both node-centric and graph-centric tasks are involved in this application. In this study, we used an individual generative model and high order graph analysis for the region of interest recognition areas of the brain with abnormal connection during performing certain tasks and resting-state or decompose irregular observations. Accordingly, a high order framework of Variational Graph Autoencoder with a Gaussian distributer was proposed in the paper to analyze the functional data in brain imaging studies in which Generative Adversarial Network is employed for optimizing the latent space in the process of learning strong non-rigid graphs among large scale data. Furthermore, the possible modes of correlations were distinguished in abnormal brain connections. Our goal was to find the degree of correlation between the affected regions and their simultaneous occurrence over time. We can take advantage of this to diagnose brain diseases or show the ability of the nervous system to modify brain topology at all angles and brain plasticity according to input stimuli. In this study, we particularly focused on Alzheimer’s disease.

Brain reactivity to humorous films is affected by insomnia

SLEEP ◽  
2021 ◽  
Author(s):  
Ernesto Sanz-Arigita ◽  
Yannick Daviaux ◽  
Marc Joliot ◽  
Bixente Dilharreguy ◽  
Jean-Arthur Micoulaud-Franchi ◽  
...  
Keyword(s):  
Emotional Reactivity ◽  
Brain Activity ◽  
Brain Network ◽  
Anterior Cingulate ◽  
Neural Representation ◽  
Cingulate Gyrus ◽  
Anterior Cingulate Gyrus ◽  
Left Caudate ◽  
Neural Reactivity ◽  
The Right

Abstract Study objectives Emotional reactivity to negative stimuli has been investigated in insomnia, but little is known about emotional reactivity to positive stimuli and its neural representation. Methods We used 3T fMRI to determine neural reactivity during the presentation of standardized short, 10-40-s, humorous films in insomnia patients (n=20, 18 females, aged 27.7 +/- 8.6 years) and age-matched individuals without insomnia (n=20, 19 females, aged 26.7 +/- 7.0 years), and assessed humour ratings through a visual analogue scale (VAS). Seed-based functional connectivity was analysed for left and right amygdala networks: group-level mixed-effects analysis (FLAME; FSL) was used to compare amygdala connectivity maps between groups. Results fMRI seed-based analysis of the amygdala revealed stronger neural reactivity in insomnia patients than in controls in several brain network clusters within the reward brain network, without humour rating differences between groups (p = 0.6). For left amygdala connectivity, cluster maxima were in the left caudate (Z=3.88), left putamen (Z=3.79) and left anterior cingulate gyrus (Z=4.11), while for right amygdala connectivity, cluster maxima were in the left caudate (Z=4.05), right insula (Z=3.83) and left anterior cingulate gyrus (Z=4.29). Cluster maxima of the right amygdala network were correlated with hyperarousal scores in insomnia patients only. Conclusions Presentation of humorous films leads to increased brain activity in the neural reward network for insomnia patients compared to controls, related to hyperarousal features in insomnia patients, in the absence of humor rating group differences. These novel findings may benefit insomnia treatment interventions.

scholarly journals Identifying a Common Functional Framework for Apathy Large-Scale Brain Network

2021 ◽  
Vol 11 (7) ◽  
pp. 679
Author(s):  
Vincenzo Alfano ◽  
Mariachiara Longarzo ◽  
Giulia Mele ◽  
Marcello Esposito ◽  
Marco Aiello ◽  
...  
Keyword(s):  
Large Scale ◽  
Brain Magnetic Resonance Imaging ◽  
Brain Network ◽  
Neural Pathways ◽  
Resonance Imaging ◽  
Daily Life Activities ◽  
Functional Differences ◽  
Central Gyrus ◽  
Magnetic Resonance Imaging Mri ◽  
Disease Specific

Apathy is a neuropsychiatric condition characterized by reduced motivation, initiative, and interest in daily life activities, and it is commonly reported in several neurodegenerative disorders. The study aims to investigate large-scale brain networks involved in apathy syndrome in patients with frontotemporal dementia (FTD) and Parkinson’s disease (PD) compared to a group of healthy controls (HC). The study sample includes a total of 60 subjects: 20 apathetic FTD and PD patients, 20 non apathetic FTD and PD patients, and 20 HC matched for age. Two disease-specific apathy-evaluation scales were used to measure the presence of apathy in FTD and PD patients; in the same day, a 3T brain magnetic resonance imaging (MRI) with structural and resting-state functional (fMRI) sequences was acquired. Differences in functional connectivity (FC) were assessed between apathetic and non-apathetic patients with and without primary clinical diagnosis revealed, using a whole-brain, seed-to-seed approach. A significant hypoconnectivity between apathetic patients (both FTD and PD) and HC was detected between left planum polare and both right pre- or post-central gyrus. Finally, to investigate whether such neural alterations were due to the underlying neurodegenerative pathology, we replicated the analysis by considering two independent patients’ samples (i.e., non-apathetic PD and FTD). In these groups, functional differences were no longer detected. These alterations may subtend the involvement of neural pathways implicated in a specific reduction of information/elaboration processing and motor outcome in apathetic patients.

scholarly journals A Behavioral and Electrophysiological Investigation of Effects of Visual Congruence on Olfactory Sensitivity During Habituation to Prolonged Odors

2020 ◽  
Vol 45 (9) ◽  
pp. 845-854
Author(s):  
Nicholas Fallon ◽  
Timo Giesbrecht ◽  
Anna Thomas ◽  
Andrej Stancak
Keyword(s):  
Cognitive Processing ◽  
Visual Cues ◽  
Reaction Times ◽  
Event Related Potential ◽  
Detection Sensitivity ◽  
Moderate Intensity ◽  
Odor Perception ◽  
Potential Analysis ◽  
Odor Detection ◽  
Central Processing

Abstract Congruent visual cues augment sensitivity to brief olfactory presentations and habituation of odor perception is modulated by central-cognitive processing including context. However, it is not known whether habituation to odors could interact with cross-modal congruent stimuli. The present research investigated the effect of visual congruence on odor detection sensitivity during continuous odor exposures. We utilized a multimethod approach, including subjective behavioral responses and reaction times (RTs; study 1) and electroencephalography (EEG, study 2). Study 1: 25 participants received 2-min presentations of moderate-intensity floral odor delivered via olfactometer with congruent (flower) and incongruent (object) image presentations. Participants indicated odor perception after each image. Detection sensitivity and RTs were analyzed in epochs covering the period of habituation. Study 2: 25 new participants underwent EEG recordings during 145-s blocks of odor presentations with congruent or incongruent images. Participants passively observed images and intermittently rated the perceived intensity of odor. Event-related potential analysis was utilized to evaluate brain processing related to odor–visual pairs across the period of habituation. Odor detection sensitivity and RTs were improved by congruent visual cues. Results highlighted a diminishing influence of visual congruence on odor detection sensitivity as habituation occurred. Event-related potential analysis revealed an effect of congruency on electrophysiological processing in the N400 component. This was only evident in early periods of odor exposure when perception was strong. For the first time, this demonstrates the modulation of central processing of odor–visual pairs by habituation. Frontal negativity (N400) responses encode the aspects of cross-modal congruence for odor–vision cross-modal tasks.

scholarly journals Reconfiguration of human evolving large-scale epileptic brain networks prior to seizures: an evaluation with node centralities

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Rieke Fruengel ◽  
Timo Bröhl ◽  
Thorsten Rings ◽  
Klaus Lehnertz
Keyword(s):  
Large Scale ◽  
Brain Networks ◽  
Brain Regions ◽  
Brain Network ◽  
Theoretical Concept ◽  
Global Network ◽  
Time Resolved ◽  
Functional Connections ◽  
Node Centrality ◽  
Network Mechanism

AbstractPrevious research has indicated that temporal changes of centrality of specific nodes in human evolving large-scale epileptic brain networks carry information predictive of impending seizures. Centrality is a fundamental network-theoretical concept that allows one to assess the role a node plays in a network. This concept allows for various interpretations, which is reflected in a number of centrality indices. Here we aim to achieve a more general understanding of local and global network reconfigurations during the pre-seizure period as indicated by changes of different node centrality indices. To this end, we investigate—in a time-resolved manner—evolving large-scale epileptic brain networks that we derived from multi-day, multi-electrode intracranial electroencephalograpic recordings from a large but inhomogeneous group of subjects with pharmacoresistant epilepsies with different anatomical origins. We estimate multiple centrality indices to assess the various roles the nodes play while the networks transit from the seizure-free to the pre-seizure period. Our findings allow us to formulate several major scenarios for the reconfiguration of an evolving epileptic brain network prior to seizures, which indicate that there is likely not a single network mechanism underlying seizure generation. Rather, local and global aspects of the pre-seizure network reconfiguration affect virtually all network constituents, from the various brain regions to the functional connections between them.

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