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 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 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 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’.

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.

The Prognosis in Insanity

1883 ◽  
Vol 29 (126) ◽  
pp. 188-205
Author(s):  
D. G. Thomson
Keyword(s):  
Complete Resolution ◽  
Brain Activity ◽  
The State ◽  
Point Of View ◽  
Whole Brain ◽  
Increased Activity ◽  
The Brain ◽  
Abnormal Tissue

Mental Exaltation, Mania.—The question of the Prognosis in Mental Exaltation—Mania—in its various forms, is a far more debatable and uncertain matter than in melancholia. The symptoms in melancholia being of a negative character due to a lowering or suspension of brain activity, we do not look for all those diversities, endless varieties and aspects which we may find in mania, be it simple, acute, or chronic. Generally there is an increased vitality, a state of hyperæsthesia, an increase in the activity of the brain, generally of the whole brain, and we must believe that these states will not so easily end in complete resolution as the condition of merely depressed action, or rather no action, which obtains in melancholia—I mean in melancholia generally, and not those states of acute melancholia which are supposed to be closely allied to the state which in other brains and under other subjective circumstances would give rise to mania from a pathological point of view. If this increased activity does not rapidly terminate in resolution, one of two things must occur—either exhaustion or atrophy, resulting in death or dementia, will supervene, or abnormal tissue will invade or replace healthy nerve paths or areas, and chronic aberration of mind ensue.

scholarly journals Distributed affective space represents multiple emotion categories across the brain

2017 ◽  
Author(s):  
Heini Saarimäki ◽  
Lara Farzaneh Ejtehadian ◽  
Enrico Glerean ◽  
liro P. Jääskeläinen ◽  
Patrik Vuilleumier ◽  
...  
Keyword(s):  
Functional Organization ◽  
Brain Activity ◽  
Premotor Cortex ◽  
Subjective Feeling ◽  
Neural Activation ◽  
Functional Magnetic Resonance ◽  
Activation Patterns ◽  
Discrete Emotion ◽  
Subcortical Regions ◽  
The Brain

The functional organization of human emotion systems as well as their neuroanatomical basis and segregation in the brain remains unresolved. Here we used pattern classification and hierarchical clustering to reveal and characterize the organization of discrete emotion categories in the human brain. We induced 14 emotions (6 “basic”, such as fear and anger; and 8 “non-basic”, such as shame and gratitude) and a neutral state in participants using guided mental imagery while their brain activity was measured with functional magnetic resonance imaging (fMRI). Twelve out of 14 emotions could be reliably classified from the fMRI signals. All emotions engaged a multitude of brain areas, primarily in midline cortices including anterior and posterior cingulate and precuneus, in subcortical regions, and in motor regions including cerebellum and premotor cortex. Similarity of subjective emotional experiences was associated with similarity of the corresponding neural activation patterns. We conclude that the emotions included in the study have discrete neural bases characterized by specific, distributed activation patterns in widespread cortical and subcortical circuits, and highlight both overlaps and differences in the locations of these for each emotion. Locally differentiated engagement of these globally shared circuits defines the unique neural fingerprint activity pattern and the corresponding subjective feeling associated with each emotion.

scholarly journals Perturbations in dynamical models of whole-brain activity dissociate between the level and stability of consciousness

2021 ◽  
Vol 17 (7) ◽  
pp. e1009139
Author(s):  
Yonatan Sanz Perl ◽  
Carla Pallavicini ◽  
Ignacio Pérez Ipiña ◽  
Athena Demertzi ◽  
Vincent Bonhomme ◽  
...  
Keyword(s):  
Computational Models ◽  
Brain Activity ◽  
Relevant Information ◽  
Complete Characterization ◽  
Brain State ◽  
Whole Brain ◽  
Brain Injured ◽  
Level Of Consciousness ◽  
The Stability ◽  
Injured Patients

Consciousness transiently fades away during deep sleep, more stably under anesthesia, and sometimes permanently due to brain injury. The development of an index to quantify the level of consciousness across these different states is regarded as a key problem both in basic and clinical neuroscience. We argue that this problem is ill-defined since such an index would not exhaust all the relevant information about a given state of consciousness. While the level of consciousness can be taken to describe the actual brain state, a complete characterization should also include its potential behavior against external perturbations. We developed and analyzed whole-brain computational models to show that the stability of conscious states provides information complementary to their similarity to conscious wakefulness. Our work leads to a novel methodological framework to sort out different brain states by their stability and reversibility, and illustrates its usefulness to dissociate between physiological (sleep), pathological (brain-injured patients), and pharmacologically-induced (anesthesia) loss of consciousness.

scholarly journals Searching through functional space reveals distributed visual, auditory, and semantic coding in the human brain

2020 ◽  
Author(s):  
Sreejan Kumar ◽  
Cameron T. Ellis ◽  
Thomas O’Connell ◽  
Marvin M Chun ◽  
Nicholas B. Turk-Browne
Keyword(s):  
Human Brain ◽  
Language Processing ◽  
Brain Function ◽  
Computational Models ◽  
Brain Activity ◽  
Functional Space ◽  
Semantic Features ◽  
Brain Functions ◽  
Auditory Features ◽  
The Brain

AbstractThe extent to which brain functions are localized or distributed is a foundational question in neuroscience. In the human brain, common fMRI methods such as cluster correction, atlas parcellation, and anatomical searchlight are biased by design toward finding localized representations. Here we introduce the functional searchlight approach as an alternative to anatomical searchlight analysis, the most commonly used exploratory multivariate fMRI technique. Functional searchlight removes any anatomical bias by grouping voxels based only on functional similarity and ignoring anatomical proximity. We report evidence that visual and auditory features from deep neural networks and semantic features from a natural language processing model are more widely distributed across the brain than previously acknowledged. This approach provides a new way to evaluate and constrain computational models with brain activity and pushes our understanding of human brain function further along the spectrum from strict modularity toward distributed representation.

Gene-culture interaction on human behavior and the brain

2017 ◽  
Author(s):  
Shihui Han
Keyword(s):  
Human Behavior ◽  
Functional Organization ◽  
Brain Activity ◽  
Receptor Gene ◽  
Individual Development ◽  
Genetic Studies ◽  
Oxytocin Receptor Gene ◽  
New Approach ◽  
Self Reflection ◽  
The Brain

Chapter 7 reviews empirical findings that allow consideration of biological and environmental influences on human behavior from an evolutionary perspective (e.g., gene-culture coevolution) and from a perspective of individual development (e.g., gene-culture interaction). It also reviews imaging genetic studies that link genes with brain functional organization. It introduces a cultural neuroscience paradigm for investigating genetic influences on the coupling of brain activity and culture by presenting two studies that examined how serotonin transporter functional polymorphism and oxytocin receptor gene moderate the association between interdependence and brain activities involved in self-reflection and empathy. These studies illustrate a new approach to understanding the manner with which culture interacts with gene to shape human brain activity.

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