scholarly journals Entropy of the Multi-Channel EEG Recordings Identifies the Distributed Signatures of Negative, Neutral and Positive Affect in Whole-Brain Variability

Entropy ◽  
2019 ◽  
Vol 21 (12) ◽  
pp. 1228 ◽  
Author(s):  
Soheil Keshmiri ◽  
Masahiro Shiomi ◽  
Hiroshi Ishiguro
Keyword(s):  
Positive Affect ◽  
Fundamental Property ◽  
Brain Activation ◽  
Neural Substrates ◽  
Brain Regions ◽  
Subjective Experiences ◽  
Whole Brain ◽  
Eeg Recordings ◽  
Negative Feelings ◽  
The Brain

Individuals’ ability to express their subjective experiences in terms of such attributes as pleasant/unpleasant or positive/negative feelings forms a fundamental property of their affect and emotion. However, neuroscientific findings on the underlying neural substrates of the affect appear to be inconclusive with some reporting the presence of distinct and independent brain systems and others identifying flexible and distributed brain regions. A common theme among these studies is the focus on the change in brain activation. As a result, they do not take into account the findings that indicate the brain activation and its information content does not necessarily modulate and that the stimuli with equivalent sensory and behavioural processing demands may not necessarily result in differential brain activation. In this article, we take a different stance on the analysis of the differential effect of the negative, neutral and positive affect on the brain functioning in which we look into the whole-brain variability: that is the change in the brain information processing measured in multiple distributed regions. For this purpose, we compute the entropy of individuals’ muti-channel EEG recordings who watched movie clips with differing affect. Our results suggest that the whole-brain variability significantly differentiates between the negative, neutral and positive affect. They also indicate that although some brain regions contribute more to such differences, it is the whole-brain variational pattern that results in their significantly above chance level prediction. These results imply that although the underlying brain substrates for negative, neutral and positive affect exhibit quantitatively differing degrees of variability, their differences are rather subtly encoded in the whole-brain variational patterns that are distributed across its entire activity.

scholarly journals Emergence of the Affect from the Variation in the Whole-Brain Flow of Information

2019 ◽  
Vol 10 (1) ◽  
pp. 8
Author(s):  
Soheil Keshmiri ◽  
Masahiro Shiomi ◽  
Hiroshi Ishiguro
Keyword(s):  
Information Flow ◽  
Cortical Neurons ◽  
Large Scale ◽  
Brain Activation ◽  
Brain Regions ◽  
Neural Basis ◽  
Whole Brain ◽  
Affect States ◽  
Flow Of Information ◽  
The Brain

Over the past few decades, the quest for discovering the brain substrates of the affect to understand the underlying neural basis of the human’s emotions has resulted in substantial and yet contrasting results. Whereas some point at distinct and independent brain systems for the Positive and Negative affects, others propose the presence of flexible brain regions. In this respect, there are two factors that are common among these previous studies. First, they all focused on the change in brain activation, thereby neglecting the findings that indicate that the stimuli with equivalent sensory and behavioral processing demands may not necessarily result in differential brain activation. Second, they did not take into consideration the brain regional interactivity and the findings that identify that the signals from individual cortical neurons are shared across multiple areas and thus concurrently contribute to multiple functional pathways. To address these limitations, we performed Granger causal analysis on the electroencephalography (EEG) recordings of the human subjects who watched movie clips that elicited Negative, Neutral, and Positive affects. This allowed us to look beyond the brain regional activation in isolation to investigate whether the brain regional interactivity can provide further insights for understanding the neural substrates of the affect. Our results indicated that the differential affect states emerged from subtle variation in information flow of the brain cortical regions that were in both hemispheres. They also showed that these regions that were rather common between affect states than distinct to a specific affect were characterized with both short- as well as long-range information flow. This provided evidence for the presence of simultaneous integration and differentiation in the brain functioning that leads to the emergence of different affects. These results are in line with the findings on the presence of intrinsic large-scale interacting brain networks that underlie the production of psychological events. These findings can help advance our understanding of the neural basis of the human’s emotions by identifying the signatures of differential affect in subtle variation that occurs in the whole-brain cortical flow of information.

scholarly journals Increased sensitivity to strong perturbations in a whole-brain model of LSD

2021 ◽  
Author(s):  
Beatrice M. Jobst ◽  
Selen Atasoy ◽  
Adrián Ponce-Alvarez ◽  
Ana Sanjuán ◽  
Leor Roseman ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
External Perturbation ◽  
Brain Regions ◽  
Lysergic Acid ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging ◽  
Whole Brain ◽  
Response Diversity ◽  
Brain States ◽  
The Brain

AbstractLysergic acid diethylamide (LSD) is a potent psychedelic drug, which has seen a revival in clinical and pharmacological research within recent years. Human neuroimaging studies have shown fundamental changes in brain-wide functional connectivity and an expansion of dynamical brain states, thus raising the question about a mechanistic explanation of the dynamics underlying these alterations. Here, we applied a novel perturbational approach based on a whole-brain computational model, which opens up the possibility to externally perturb different brain regions in silico and investigate differences in dynamical stability of different brain states, i.e. the dynamical response of a certain brain region to an external perturbation. After adjusting the whole-brain model parameters to reflect the dynamics of functional magnetic resonance imaging (fMRI) BOLD signals recorded under the influence of LSD or placebo, perturbations of different brain areas were simulated by either promoting or disrupting synchronization in the regarding brain region. After perturbation offset, we quantified the recovery characteristics of the brain area to its basal dynamical state with the Perturbational Integration Latency Index (PILI) and used this measure to distinguish between the two brain states. We found significant changes in dynamical complexity with consistently higher PILI values after LSD intake on a global level, which indicates a shift of the brain’s global working point further away from a stable equilibrium as compared to normal conditions. On a local level, we found that the largest differences were measured within the limbic network, the visual network and the default mode network. Additionally, we found a higher variability of PILI values across different brain regions after LSD intake, indicating higher response diversity under LSD after an external perturbation. Our results provide important new insights into the brain-wide dynamical changes underlying the psychedelic state - here provoked by LSD intake - and underline possible future clinical applications of psychedelic drugs in particular psychiatric disorders.HighlightsNovel offline perturbational method applied on functional magnetic resonance imaging (fMRI) data under the effect of lysergic acid diethylamide (LSD)Shift of brain’s global working point to more complex dynamics after LSD intakeConsistently longer recovery time after model perturbation under LSD influenceStrongest effects in resting state networks relevant for psychedelic experienceHigher response diversity across brain regions under LSD influence after an external in silico perturbation

scholarly journals Neural Substrates of Social Status Inference: Roles of Medial Prefrontal Cortex and Superior Temporal Sulcus

2014 ◽  
Vol 26 (5) ◽  
pp. 1131-1140 ◽  
Author(s):  
Malia Mason ◽  
Joe C. Magee ◽  
Susan T. Fiske
Keyword(s):  
Social Order ◽  
Neural Substrates ◽  
Brain Regions ◽  
Third Party ◽  
Social Interdependence ◽  
Medial Pfc ◽  
State Inference ◽  
The Brain ◽  
Track Status

The negotiation of social order is intimately connected to the capacity to infer and track status relationships. Despite the foundational role of status in social cognition, we know little about how the brain constructs status from social interactions that display it. Although emerging cognitive neuroscience reveals that status judgments depend on the intraparietal sulcus, a brain region that supports the comparison of targets along a quantitative continuum, we present evidence that status judgments do not necessarily reduce to ranking targets along a quantitative continuum. The process of judging status also fits a social interdependence analysis. Consistent with third-party perceivers judging status by inferring whose goals are dictating the terms of the interaction and who is subordinating their desires to whom, status judgments were associated with increased recruitment of medial pFC and STS, brain regions implicated in mental state inference.

scholarly journals Algebra dissociates from arithmetic in the brain semantic network

2021 ◽  
Author(s):  
Dazhi Cheng ◽  
Mengyi Li ◽  
Naiyi Wang ◽  
Liangyuan Ouyang ◽  
Xinlin Zhou
Keyword(s):  
Supplementary Motor Area ◽  
Semantic Network ◽  
Brain Activation ◽  
Brain Regions ◽  
Motor Area ◽  
Angular Gyrus ◽  
Precentral Gyrus ◽  
Single Trial ◽  
Brain Behavior ◽  
The Brain

Abstract Background Mathematical expressions mainly include arithmetic (such as 8 − (1 + 3)) and algebraic expressions (such as a − (b + c)). Previous studies shown that both algebraic processing and arithmetic involved the bilateral parietal brain regions. Although behavioral and neuropsychological studies have revealed the dissociation between algebra and arithmetic, how algebraic processing is dissociated from arithmetic in brain networks is still unclear. Methods Using functional magnetic resonance imaging (fMRI), this study scanned 30 undergraduates and directly compared the brain activation during algebra and arithmetic. Brain activations, single-trial (item-wise) interindividual correlation and mean-trial interindividual correlation related to algebra processing were compared with those related to arithmetic. Results Brain activation analyses showed that algebra elicited greater activation in the angular gyrus and arithmetic elicited greater activation in the bilateral supplementary motor area, left insula, and left inferior parietal lobule. Interindividual single-trial brain-behavior correlation revealed significant brain-behavior correlations in the semantic network, including the middle temporal gyri, inferior frontal gyri, dorsomedial prefrontal cortices, and left angular gyrus, for algebra. For arithmetic, the significant brain-behavior correlations were located in the phonological network, including the precentral gyrus and supplementary motor area, and in the visuospatial network, including the bilateral superior parietal lobules. Conclusion These findings suggest that algebra relies on the semantic network and arithmetic relies on the phonological and visuospatial networks.

scholarly journals Concept generation techniques change patterns of brain activation during engineering design

2020 ◽  
Vol 6 ◽  
Author(s):  
Tripp Shealy ◽  
John Gero ◽  
Mo Hu ◽  
Julie Milovanovic
Keyword(s):  
Prefrontal Cortex ◽  
Morphological Analysis ◽  
Engineering Students ◽  
Near Infrared ◽  
Brain Activation ◽  
Brain Regions ◽  
Concept Generation ◽  
Functional Near Infrared Spectroscopy ◽  
Cognitive Activation ◽  
The Brain

Abstract This paper presents the results of studying the brain activations of 30 engineering students when using three different design concept generation techniques: brainstorming, morphological analysis, and TRIZ. Changes in students’ brain activation in the prefrontal cortex were measured using functional near-infrared spectroscopy. The results are based on the area under the curve analysis of oxygenated hemodynamic response as well as an assessment of functional connectivity using Pearson’s correlation to compare students’ cognitive brain activations using these three different ideation techniques. The results indicate that brainstorming and morphological analysis demand more cognitive activation across the prefrontal cortex (PFC) compared to TRIZ. The highest cognitive activation when brainstorming and using morphological analysis is in the right dorsolateral PFC (DLPFC) and ventrolateral PFC. These regions are associated with divergent thinking and ill-defined problem-solving. TRIZ produces more cognitive activation in the left DLPFC. This region is associated with convergent thinking and making judgments. Morphological analysis and TRIZ also enable greater coordination (i.e., synchronized activation) between brain regions. These findings offer new evidence that structured techniques like TRIZ reduce cognitive activation, change patterns of activation and increase coordination between regions in the brain.

scholarly journals A null model of the mouse whole-neocortex micro-connectome

2019 ◽  
Author(s):  
Michael W Reimann ◽  
Michael Gevaert ◽  
Ying Shi ◽  
Huanxiang Lu ◽  
Henry Markram ◽  
...  
Keyword(s):  
Electron Microscopy ◽  
Null Model ◽  
Brain Regions ◽  
Whole Brain ◽  
Micro Scale ◽  
Connectivity Patterns ◽  
Experimental Findings ◽  
The Brain ◽  
Scale Data ◽  
Meso Scale

1AbstractConnectomics, the study of the structure of networks of synaptically connected neurons, is one of the most important frontiers of neuroscience. Great advances are being made on the level of macro- and meso-scale connectomics, that is the study of how and which populations of neurons are wired together by tracing axons of anatomically and genetically defined neurons throughout the brain. Similarly, the use of electron-microscopy and statistical connectome models has improved our understanding of micro-connectomics, that is the study of connectivity patterns between individual neurons. We have combined these two complementary views of connectomics to build a first draft statistical model of the neuron-to-neuron micro-connectome of a whole mouse neocortex. We combined available data on region-to-region connectivity and individual whole-brain axon reconstructions to model in addition to the meso-scale trends also the innervation of individual neurons by individual axons, within and across regions. This process revealed a novel targeting principle that allowed us to predict the innervation logic of individual axons from meso-scale data. The resulting micro-connectome of 10 million neurons and 88 billion synapses recreates biological trends of targeting on the macro-meso- and micro-scale, i.e. targeting of brain regions, domains and layers within a brain region down to individual neurons. This openly accessible connectome can serve as a powerful null model to compare experimental findings to and as a substrate for whole-brain simulations of detailed neural networks.

scholarly journals Relative Brain Size and Its Relation with the Associative Pallium in Birds

2016 ◽  
Vol 87 (2) ◽  
pp. 69-77 ◽  
Author(s):  
Ferran Sayol ◽  
Louis Lefebvre ◽  
Daniel Sol
Keyword(s):  
Brain Size ◽  
Large Fraction ◽  
Biological Significance ◽  
Size Variation ◽  
Brain Structures ◽  
Brain Regions ◽  
Whole Brain ◽  
Small Areas ◽  
Mosaic Evolution ◽  
The Brain

Despite growing interest in the evolution of enlarged brains, the biological significance of brain size variation remains controversial. Much of the controversy is over the extent to which brain structures have evolved independently of each other (mosaic evolution) or in a coordinated way (concerted evolution). If larger brains have evolved by the increase of different brain regions in different species, it follows that comparisons of the whole brain might be biologically meaningless. Such an argument has been used to criticize comparative attempts to explain the existing variation in whole-brain size among species. Here, we show that pallium areas associated with domain-general cognition represent a large fraction of the entire brain, are disproportionally larger in large-brained birds and accurately predict variation in the whole brain when allometric effects are appropriately accounted for. While this does not question the importance of mosaic evolution, it suggests that examining specialized, small areas of the brain is not very helpful for understanding why some birds have evolved such large brains. Instead, the size of the whole brain reflects consistent variation in associative pallium areas and hence is functionally meaningful for comparative analyses.

Affective Neuroscience: Past, Present, and Future

2009 ◽  
Vol 1 (4) ◽  
pp. 355-368 ◽  
Author(s):  
Tim Dalgleish ◽  
Barnaby D. Dunn ◽  
Dean Mobbs
Keyword(s):  
Final Section ◽  
Neural Substrates ◽  
Brain Regions ◽  
Historical Overview ◽  
Affective Neuroscience ◽  
Future Directions ◽  
Current State ◽  
State Of Research ◽  
The Brain ◽  
Made In

The discipline of affective neuroscience is concerned with the underlying neural substrates of emotion and mood. This review presents an historical overview of the pioneering work in affective neuroscience of James and Lange, Cannon and Bard, and Hess, Papez, and MacLean before summarizing the current state of research on the brain regions identified by these seminal researchers. We also discuss the more recent strides made in the field of affective neuroscience. A final section considers different hypothetical organizations of affective neuroanatomy and highlights future directions for the discipline.

scholarly journals Network analysis of whole-brain fMRI dynamics: A new framework based on dynamic communicability

2018 ◽  
Author(s):  
Matthieu Gilson ◽  
Nikos E. Kouvaris ◽  
Gustavo Deco ◽  
Jean-François Mangin ◽  
Cyril Poupon ◽  
...  
Keyword(s):  
Network Analysis ◽  
Network Theory ◽  
Random Network ◽  
Effective Connectivity ◽  
Structural Connectivity ◽  
Real Data ◽  
Brain Regions ◽  
Network Activity ◽  
Whole Brain ◽  
The Brain

AbstractNeuroimaging techniques such as MRI have been widely used to explore the associations between brain areas. Structural connectivity (SC) captures the anatomical pathways across the brain and functional connectivity (FC) measures the correlation between the activity of brain regions. These connectivity measures have been much studied using network theory in order to uncover the distributed organization of brain structures, in particular FC for task-specific brain communication. However, the application of network theory to study FC matrices is often “static” despite the dynamic nature of time series obtained from fMRI. The present study aims to overcome this limitation by introducing a network-oriented analysis applied to whole-brain effective connectivity (EC) useful to interpret the brain dynamics. Technically, we tune a multivariate Ornstein-Uhlenbeck (MOU) process to reproduce the statistics of the whole-brain resting-state fMRI signals, which provides estimates for MOU-EC as well as input properties (similar to local excitabilities). The network analysis is then based on the Green function (or network impulse response) that describes the interactions between nodes across time for the estimated dynamics. This model-based approach provides time-dependent graph-like descriptor, named communicability, that characterize the roles that either nodes or connections play in the propagation of activity within the network. They can be used at both global and local levels, and also enables the comparison of estimates from real data with surrogates (e.g. random network or ring lattice). In contrast to classical graph approaches to study SC or FC, our framework stresses the importance of taking the temporal aspect of fMRI signals into account. Our results show a merging of functional communities over time (in which input properties play a role), moving from segregated to global integration of the network activity. Our formalism sets a solid ground for the analysis and interpretation of fMRI data, including task-evoked activity.

scholarly journals Neural Substrates Underlying Learning-Related Changes of the Unconditioned Fear Response

2013 ◽  
Vol 7 (1) ◽  
pp. 41-52 ◽  
Author(s):  
Kimberly H. Wood ◽  
Dystany Kuykendall ◽  
Lawrence W. Ver Hoef ◽  
David C. Knight
Keyword(s):  
Associative Learning ◽  
Pavlovian Conditioning ◽  
Emotional Response ◽  
Negative Relationship ◽  
Neural Substrates ◽  
Brain Regions ◽  
Anterior Insula ◽  
Fmri Signal ◽  
Posterior Insula ◽  
The Brain

The ability to predict an impending threat during Pavlovian conditioning diminishes the emotional response that is produced once the threat is encountered. Diminution of the threat response appears to be mediated by somewhat independent associative learning and expectancy-related processes. Therefore, the present study was designed to better understand the neural mechanisms that support associative learning processes, independent of expectancy, that influence the emotional response to a threat. Healthy volunteers took part in a Pavlovian conditioning procedure during which trait anxiety, expectation of the unconditioned stimulus (UCS), skin conductance response (SCR), and functional magnetic resonance imaging (fMRI) signal were assessed. The results showed no evidence for associative learning that was independent of expectation. Threat-related SCR expression was diminished on predictable trials vs. unpredictable trials of the UCS (i.e. conditioned UCR diminution). Similar to SCR, conditioned UCR diminution was observed within the left dorsolateral PFC, dorsomedial PFC, ventromedial PFC, and left anterior insula. In contrast, potentiation of the threat-related fMRI signal response was observed within left dorsolateral PFC, inferior parietal lobule (IPL), and posterior insula. A negative relationship was observed between UCS expectancy and UCR expression within the dorsomedial PFC, ventromedial PFC, and anterior insula. Finally, the anticipatory fMRI signal responses within the PFC, posterior cingulate, and amygdala showed an inverse relationship with threat-related activation within the brain regions that showed UCR diminution. The current findings suggest that the PFC and amygdala support learning-related processes that impact the magnitude of the emotional response to a threat.

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