scholarly journals Cholinergic neuromodulation of inhibitory interneurons facilitates functional integration in whole-brain models

2021 ◽  
Vol 17 (2) ◽  
pp. e1008737
Author(s):  
Carlos Coronel-Oliveros ◽  
Rodrigo Cofré ◽  
Patricio Orio
Keyword(s):  
Computational Models ◽  
Functional Organization ◽  
Brain Activity ◽  
Functional Integration ◽  
Whole Brain ◽  
Inhibitory Circuits ◽  
Inhibitory Feedback ◽  
Biophysical Mechanism ◽  
The Brain

Segregation and integration are two fundamental principles of brain structural and functional organization. Neuroimaging studies have shown that the brain transits between different functionally segregated and integrated states, and neuromodulatory systems have been proposed as key to facilitate these transitions. Although whole-brain computational models have reproduced this neuromodulatory effect, the role of local inhibitory circuits and their cholinergic modulation has not been studied. In this article, we consider a Jansen & Rit whole-brain model in a network interconnected using a human connectome, and study the influence of the cholinergic and noradrenergic neuromodulatory systems on the segregation/integration balance. In our model, we introduce a local inhibitory feedback as a plausible biophysical mechanism that enables the integration of whole-brain activity, and that interacts with the other neuromodulatory influences to facilitate the transition between different functional segregation/integration regimes in the brain.

scholarly journals Cholinergic neuromodulation of inhibitory interneurons facilitates functional integration in whole-brain models

2020 ◽  
Author(s):  
Carlos Coronel-Oliveros ◽  
Rodrigo Cofré ◽  
Patricio Orio
Keyword(s):  
Computational Models ◽  
Functional Organization ◽  
Brain Activity ◽  
Functional Integration ◽  
Whole Brain ◽  
Inhibitory Circuits ◽  
Proposed Model ◽  
Inhibitory Feedback ◽  
Noradrenergic Modulation ◽  
The Brain

AbstractSegregation and integration are two fundamental principles of brain structural and functional organization. Neuroimaging studies have shown that the brain transits between different functionally segregated and integrated states, and neuromodulatory systems have been proposed as key to facilitate these transitions. Although computational models have reproduced the effect of neuromodulation at the whole-brain level, the role of local inhibitory circuits and their cholinergic modulation has not been studied. In this article, we consider a Jansen & Rit whole-brain model in a network interconnected using a human connectome, and study the influence of the cholinergic and noradrenergic neuromodulatory systems on the segregation/integration balance. In our model, a newly introduced local inhibitory feedback enables the integration of whole-brain activity, and its modulation interacts with the other neuromodulatory influences to facilitate the transit between different functional states. Moreover, the new proposed model is able to reproduce an inverted-U relationship between noradrenergic modulation and network integration. Our work proposes a new possible mechanism behind segregation and integration in the brain.

scholarly journals K+-p-nitrophenylphosphatase activity in rat brain and liver

2009 ◽  
pp. 121-126
Author(s):  
M Ďurfinová ◽  
M Brechtlová ◽  
B Líška ◽  
Ž Barošková
Keyword(s):  
Rat Brain ◽  
Brain Activity ◽  
Brain Homogenate ◽  
Whole Brain ◽  
Pellet Fraction ◽  
Total Brain ◽  
Rat Brain And Liver ◽  
Protein Rich ◽  
The Brain

K+-p-nitrophenylphosphatase (K+pNPPase) is the enzyme, which is considered to be involved in K+-dependent hydrolysis of the phosphoenzyme in the reaction cycle of Na+, K+-ATPase. The aim of our present study was to characterize some features of K+pNPPase in homogenates of the rat brain and liver. We determined p-nitrophenylphosphatase (pNPPase) activity in the presence of various ion combinations (Mg2++K+, Mg2+, K+). We found a higher total pNPPase activity in the brain (0.8±0.079 nkat/mg protein) than in the liver (0.08±0.01 nkat/mg protein). Contrary to the liver, the main part of the total brain activity was K+-dependent. The activity of K+pNPPase was significantly higher in cerebral cortex homogenates (0.86±0.073 nkat/mg protein) in comparison to those of the whole brain (0.57±0.075 nkat/mg protein). The specific K+pNPPase activity was two times higher in the isolated pellet fraction (0.911±0.07 nkat/mg protein), rich in synaptosomes, compared to the whole brain homogenate (0.57±0.075 nkat/mg protein). Our results demonstrate the high activity of K+pNPPase in the brain tissue and its distribution mainly into the pellet fraction, what might indicate a possible role of K+pNPPase in specific structures of the brain, e.g. in synaptosomes.

scholarly journals The unbalanced reorganization of weaker functional connections induces the altered brain network topology in schizophrenia

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Rossana Mastrandrea ◽  
Fabrizio Piras ◽  
Andrea Gabrielli ◽  
Nerisa Banaj ◽  
Guido Caldarelli ◽  
...  
Keyword(s):  
Resting State ◽  
Functional Organization ◽  
Functional Integration ◽  
Brain Network ◽  
Modular Organization ◽  
Node Degree ◽  
Imaging Data ◽  
Brain Disorder ◽  
Functional Connections ◽  
The Brain

AbstractNetwork neuroscience shed some light on the functional and structural modifications occurring to the brain associated with the phenomenology of schizophrenia. In particular, resting-state functional networks have helped our understanding of the illness by highlighting the global and local alterations within the cerebral organization. We investigated the robustness of the brain functional architecture in 44 medicated schizophrenic patients and 40 healthy comparators through an advanced network analysis of resting-state functional magnetic resonance imaging data. The networks in patients showed more resistance to disconnection than in healthy controls, with an evident discrepancy between the two groups in the node degree distribution computed along a percolation process. Despite a substantial similarity of the basal functional organization between the two groups, the expected hierarchy of healthy brains' modular organization is crumbled in schizophrenia, showing a peculiar arrangement of the functional connections, characterized by several topologically equivalent backbones. Thus, the manifold nature of the functional organization’s basal scheme, together with its altered hierarchical modularity, may be crucial in the pathogenesis of schizophrenia. This result fits the disconnection hypothesis that describes schizophrenia as a brain disorder characterized by an abnormal functional integration among brain regions.

scholarly journals Consciousness, decision making, and volition: freedom beyond chance and necessity

2021 ◽  
Author(s):  
Hans Liljenström
Keyword(s):  
Decision Making ◽  
Free Will ◽  
Brain Activity ◽  
Brain Structures ◽  
Complex Process ◽  
Neural Information ◽  
Neural Information Processing ◽  
Stochastic Population Model ◽  
The Brain

AbstractWhat is the role of consciousness in volition and decision-making? Are our actions fully determined by brain activity preceding our decisions to act, or can consciousness instead affect the brain activity leading to action? This has been much debated in philosophy, but also in science since the famous experiments by Libet in the 1980s, where the current most common interpretation is that conscious free will is an illusion. It seems that the brain knows, up to several seconds in advance what “you” decide to do. These studies have, however, been criticized, and alternative interpretations of the experiments can be given, some of which are discussed in this paper. In an attempt to elucidate the processes involved in decision-making (DM), as an essential part of volition, we have developed a computational model of relevant brain structures and their neurodynamics. While DM is a complex process, we have particularly focused on the amygdala and orbitofrontal cortex (OFC) for its emotional, and the lateral prefrontal cortex (LPFC) for its cognitive aspects. In this paper, we present a stochastic population model representing the neural information processing of DM. Simulation results seem to confirm the notion that if decisions have to be made fast, emotional processes and aspects dominate, while rational processes are more time consuming and may result in a delayed decision. Finally, some limitations of current science and computational modeling will be discussed, hinting at a future development of science, where consciousness and free will may add to chance and necessity as explanation for what happens in the world.

scholarly journals Inferring brain-computational mechanisms with models of activity measurements

2016 ◽  
Vol 371 (1705) ◽  
pp. 20160278 ◽  
Author(s):  
Nikolaus Kriegeskorte ◽  
Jörn Diedrichsen
Keyword(s):  
Computational Models ◽  
Brain Activity ◽  
Visual Object ◽  
Visual Object Recognition ◽  
Summary Statistics ◽  
Activity Data ◽  
Individual Measurement ◽  
Activity Measurements ◽  
The Brain ◽  
The Way

High-resolution functional imaging is providing increasingly rich measurements of brain activity in animals and humans. A major challenge is to leverage such data to gain insight into the brain's computational mechanisms. The first step is to define candidate brain-computational models (BCMs) that can perform the behavioural task in question. We would then like to infer which of the candidate BCMs best accounts for measured brain-activity data. Here we describe a method that complements each BCM by a measurement model (MM), which simulates the way the brain-activity measurements reflect neuronal activity (e.g. local averaging in functional magnetic resonance imaging (fMRI) voxels or sparse sampling in array recordings). The resulting generative model (BCM-MM) produces simulated measurements. To avoid having to fit the MM to predict each individual measurement channel of the brain-activity data, we compare the measured and predicted data at the level of summary statistics. We describe a novel particular implementation of this approach, called probabilistic representational similarity analysis (pRSA) with MMs, which uses representational dissimilarity matrices (RDMs) as the summary statistics. We validate this method by simulations of fMRI measurements (locally averaging voxels) based on a deep convolutional neural network for visual object recognition. Results indicate that the way the measurements sample the activity patterns strongly affects the apparent representational dissimilarities. However, modelling of the measurement process can account for these effects, and different BCMs remain distinguishable even under substantial noise. The pRSA method enables us to perform Bayesian inference on the set of BCMs and to recognize the data-generating model in each case. This article is part of the themed issue ‘Interpreting BOLD: a dialogue between cognitive and cellular neuroscience’.

Is Artistic Composition in Abstract Art Detected Automatically?

2021 ◽  
pp. 102-106
Author(s):  
Claudia Menzel ◽  
Gyula Kovács ◽  
Gregor U. Hayn-Leichsenring ◽  
Christoph Redies
Keyword(s):  
Human Brain ◽  
Significant Role ◽  
Mismatch Negativity ◽  
Brain Activity ◽  
Perceptual Processing ◽  
Abstract Art ◽  
Electrical Brain Activity ◽  
Image Set ◽  
The Brain

Most artists who create abstract paintings place the pictorial elements not at random, but arrange them intentionally in a specific artistic composition. This arrangement results in a pattern of image properties that differs from image versions in which the same pictorial elements are randomly shuffled. In the article under discussion, the original abstract paintings of the author’s image set were rated as more ordered and harmonious but less interesting than their shuffled counterparts. The authors tested whether the human brain distinguishes between these original and shuffled images by recording electrical brain activity in a particular paradigm that evokes a so-called visual mismatch negativity. The results revealed that the brain detects the differences between the two types of images fast and automatically. These findings are in line with models that postulate a significant role of early (low-level) perceptual processing of formal image properties in aesthetic evaluations.

scholarly journals Frequency-dependent circuits anchored in the dorsal and ventral left anterior insula

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Yifeng Wang ◽  
Qijun Zou ◽  
Yujia Ao ◽  
Yang Liu ◽  
Yujie Ouyang ◽  
...  
Keyword(s):  
Functional Organization ◽  
Functional Integration ◽  
Anterior Insula ◽  
Frequency Effect ◽  
Frequency Dependent ◽  
Functional Connections ◽  
Human Connectome Project ◽  
Cognitive Neurosciences ◽  
The Right

Abstract The hub role of the right anterior insula (AI) has been emphasized in cognitive neurosciences and been demonstrated to be frequency-dependently organized. However, the functional organization of left AI (LAI) has not been systematically investigated. Here we used 100 unrelated datasets from the Human Connectome Project to study the frequency-dependent organization of LAI along slow 6 to slow 1 bands. The broadband functional connectivity of LAI was similar to previous findings. In slow 6-slow 3 bands, both dorsal and ventral seeds in LAI were correlated to the salience network (SN) and language network (LN) and anti-correlated to the default mode network (DMN). However, these seeds were only correlated to the LAI in slow 2-slow 1 bands. These findings indicate that broadband and narrow band functional connections reflect different functional organizations of the LAI. Furthermore, the dorsal seed had a stronger connection with the LN and anti-correlation with DMN while the ventral seed had a stronger connection within the SN in slow 6-slow 3 bands. In slow 2-slow 1 bands, both seeds had stronger connections with themselves. These observations indicate distinctive functional organizations for the two parts of LAI. Significant frequency effect and frequency by seed interaction were also found, suggesting different frequency characteristics of these two seeds. The functional integration and functional segregation of LDAI and LVAI were further supported by their cognitive associations. The frequency- and seed-dependent functional organizations of LAI may enlighten future clinical and cognitive investigations.

SUPER-SYNERGY IN THE BRAIN: THE GRANDMOTHER PERCEPT IS MANIFESTED BY MULTIPLE OSCILLATIONS

2004 ◽  
Vol 14 (02) ◽  
pp. 453-491 ◽  
Author(s):  
EROL BAŞAR ◽  
MURAT ÖZGÖREN ◽  
SIREL KARAKAŞ ◽  
CANAN BAŞAR-EROĞLU
Keyword(s):  
Experimental Work ◽  
Brain Function ◽  
Present Report ◽  
Whole Brain ◽  
Animal Brain ◽  
Oscillatory Dynamics ◽  
New Research ◽  
The Brain ◽  
Face Support

The present report describes the dynamic foundations of long-standing experimental work in the field of oscillatory dynamics in the human and animal brain. It aims to show the role of multiple oscillations in the integrative brain function, memory, and complex perception by a recently introduced conceptional framework: the super-synergy in the whole brain. Results of recent experiments related to the percept of the grandmother-face support our concept of super-synergy in the whole brain in order to explain manifestation of Gestalts and Memory-Stages. This report may also provide new research avenues in macrodynamics of the brain.

scholarly journals Transient brain networks underlying interpersonal strategies during synchronized action

2020 ◽  
Author(s):  
Ole Adrian Heggli ◽  
Ivana Konvalinka ◽  
Joana Cabral ◽  
Elvira Brattico ◽  
Morten L Kringelbach ◽  
...  
Keyword(s):  
Computational Models ◽  
Brain Activity ◽  
Brain Regions ◽  
Brain Network ◽  
Human Interaction ◽  
Action Perception ◽  
Network Information ◽  
Mutual Adaptation ◽  
Underlying Mechanisms ◽  
The Brain

Abstract Interpersonal coordination is a core part of human interaction, and its underlying mechanisms have been extensively studied using social paradigms such as joint finger-tapping. Here, individual and dyadic differences have been found to yield a range of dyadic synchronization strategies, such as mutual adaptation, leading–leading, and leading–following behaviour, but the brain mechanisms that underlie these strategies remain poorly understood. To identify individual brain mechanisms underlying emergence of these minimal social interaction strategies, we contrasted EEG-recorded brain activity in two groups of musicians exhibiting the mutual adaptation and leading–leading strategies. We found that the individuals coordinating via mutual adaptation exhibited a more frequent occurrence of phase-locked activity within a transient action–perception-related brain network in the alpha range, as compared to the leading–leading group. Furthermore, we identified parietal and temporal brain regions that changed significantly in the directionality of their within-network information flow. Our results suggest that the stronger weight on extrinsic coupling observed in computational models of mutual adaptation as compared to leading–leading might be facilitated by a higher degree of action–perception network coupling in the brain.

Creativity or mental illness: Possible errors of relational priming in neural networks of the brain

2008 ◽  
Vol 31 (4) ◽  
pp. 398-399
Author(s):  
James E. Swain ◽  
John D. Swain
Keyword(s):  
Mental Illness ◽  
Cognitive Skills ◽  
Computational Models ◽  
Brain Activity ◽  
Childhood Onset ◽  
Hyperactivity Disorder ◽  
Depression Disorders ◽  
Complex Cognitive Skills ◽  
The Brain ◽  
Relational Priming

AbstractIf connectionist computational models explain the acquisition of complex cognitive skills, errors in such models would also help explain unusual brain activity such as in creativity – as well as in mental illness, including childhood onset problems with social behaviors in autism, the inability to maintain focus in attention deficit and hyperactivity disorder (ADHD), and the lack of motivation of depression disorders.

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