scholarly journals Trzy wymiary użytecznego miasta

1970 ◽  
Vol 42 ◽  
pp. 219-231
Author(s):  
Maciej Błaszak Maciej Błaszak ◽  
Artur Fojud Artur Fojud
Keyword(s):  
Neural Networks ◽  
Brain Networks ◽  
Physical Space ◽  
Central Executive ◽  
Three Dimensions ◽  
Default Mode ◽  
The City ◽  
The Brain

The paper analyzes three dimensions of the usable city: experiential, functional and rational. These dimensions are connected with three types of mental experiences, respectively, sensational, perceptional and conceptual, and with three neural networks: salience, central executive and default mode. It is argued that sensations refer to the physical space of the city, perceptions to the functional niche of the city, and concepts to the values implemented in the brain and recognized in the urban objects during their usage. The notion of the usable city is tightly connected with the notion of the happy city: three brain networks computing the information about dimensions of the usable city generate three parameters of happiness: pleasure, satisfaction, and one’s potential realization.

CNTM-01. Evaluating Traditional and Non-Traditional Eloquent Areas in Patients with Brain Tumors: Large-scale Network Analysis Using a Machine Learning-Based Platform

2021 ◽  
Vol 23 (Supplement_6) ◽  
pp. vi224-vi224
Author(s):  
Alexis Morell ◽  
Daniel Eichberg ◽  
Ashish Shah ◽  
Evan Luther ◽  
Victor Lu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Brain Tumors ◽  
Large Scale ◽  
Diffusion Tensor ◽  
Brain Networks ◽  
Tumor Resection ◽  
Central Executive ◽  
Default Mode ◽  
Eloquent Areas ◽  
Neurologic Deficits

Abstract BACKGROUND Developing mapping tools that allow identification of traditional or non-traditional eloquent areas is necessary to minimize the risk of postoperative neurologic deficits. The objective of our study is to evaluate the use of a novel cloud-based platform that uses machine learning to identify cerebral networks in patients with brain tumors. METHODS We retrospectively included all adult patients who underwent surgery for brain tumor resection or thermal ablation at our Institution between the 16th of February and the 15th of May of 2021. Pre and postoperative contrast-enhanced MRI with T1-weighted and high-resolution Diffusion Tensor Imaging (DTI) sequences were uploaded into the Quicktome platform. After processing the data, we categorized the integrity of seven large-scale brain networks: sensorimotor, visual, ventral attention, central executive, default mode, dorsal attention and limbic. Affected networks were correlated with pre and postoperative clinical data, including neurologic deficits. RESULTS Thirty-five (35) patients were included in the study. The average age of the sample was 63.2 years, and 51.4% (n=18) were females. The most affected network was the central executive network (40%), followed by the dorsal attention and default mode networks (31.4%), while the least affected were the visual (11%) and ventral attention networks (17%). Patients with preoperative deficits showed a significantly higher number of altered networks before the surgery (p=0.021), compared to patients without deficits. In addition, we found that patients without neurologic deficits had an average of 2.06 large-scale networks affected, with 75% of them not being related to traditional eloquent areas as the sensorimotor, language or visual circuits. CONCLUSIONS The Quicktome platform is a practical tool that allows automatic visualization of large-scale brain networks in patients with brain tumors. Although further studies are needed, it may assist in the surgical management of traditional and non-traditional eloquent areas.

scholarly journals Disrupted controlling mechanism of salience network on default-mode network and central-executive network in schizophrenia

2021 ◽  
Author(s):  
Ganesh B. Chand ◽  
Deepa S. Thakuri ◽  
Bhavin Soni
Keyword(s):  
Neural Networks ◽  
Default Mode Network ◽  
Central Executive ◽  
Causal Modeling ◽  
Salience Network ◽  
Healthy Controls ◽  
Interaction Patterns ◽  
Default Mode ◽  
Controlling Mechanism ◽  
Central Executive Network

AbstractNeuroimaging studies suggest that the human brain consists of intrinsically organized large-scale neural networks. Among those networks, the interplay among default-mode network (DMN), salience network (SN), and central-executive network (CEN)has been widely employed to understand the functional interaction patterns in health and diseases. This triple network model suggests that SN causally controls DMN and CEN in healthy individuals. This interaction is often referred to as the dynamic controlling mechanism of SN. However, such interactions are not well understood in individuals with schizophrenia. In this study, we leveraged resting state functional magnetic resonance imaging (fMRI) data of schizophrenia (n = 67) and healthy controls (n = 81) to evaluate the functional interactions among DMN, SN, and CEN using dynamical causal modeling. In healthy controls, our analyses replicated previous findings that SN regulates DMN and CEN activities (Mann-Whitney U test; p < 10−8). In schizophrenia, however, our analyses revealed the disrupted SN-based controlling mechanism on DMN and CEN (Mann-Whitney U test; p < 10−16). These results indicate that the disrupted controlling mechanism of SN on two other neural networks may be a candidate neuroimaging phenotype in schizophrenia.

Abstraction Through the Lens of Neuroscience

2021 ◽  
Vol 58 (2) ◽  
pp. 6-18
Author(s):  
Valentin A. Bazhanov ◽  
Keyword(s):  
Neural Networks ◽  
Brain Networks ◽  
The Other ◽  
Activity Type ◽  
Other Hand ◽  
The Subject ◽  
The One ◽  
Concrete Concepts ◽  
The Brain ◽  
Different Levels

The interpretation of the abstraction process and the use of various abstractions are consistent with the trends associated with the naturalistic turn in modern cognitive and neural studies. Logic of dealing with abstractions presupposes not only acts of digress from the insignificant details of the object, but also the replenishment of the image due to idealization, endowing the object with properties that are absent from it. Thus, abstraction expresses not only the activity of the subject but the fact of “locking” this activity on a certain kind of ontology as well. The latter, in the spirit of I. Kant’s apriorism, is a function of epistemological attitudes and the nature of the subject's activity. Therefore, in the context of modern neuroscience, we can mean the transcendentalism of activity type. An effective tool for comprehension of abstractions making and development is a metaphor, which, on the one hand, allows submerge the object of analysis into a more or less familiar context, and on the other hand, it may produce new abstractions. Naturalistic tendencies manifested in the fact that empirically established abstractions activate certain neural brain networks, and abstract and concrete concepts are "processed" by various parts of the brain. If we keep in mind the presence of different levels abstractions then not only neural networks but even individual neurons (called “conceptual”) can be excited. The excitation of neural networks is associated with understanding the meaning of some concepts, but at the same time, the activity of these networks presupposes the "dissection" of reality due to a certain angle, determined in the general case by goals, attitudes and concrete practices of the subject.

Recent Methodology in Connectionist Systems

2011 ◽  
pp. 94-106
Author(s):  
Ana B. Porto Pazos ◽  
Alberto Alvarellos González ◽  
Alejandro Pazos Sierra
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Glial Cells ◽  
Brain Networks ◽  
Classification Problems ◽  
Biological Phenomena ◽  
Artificial Neurons ◽  
Artificial Neural ◽  
The Brain

The Artificial NeuroGlial Networks, which try to imitate the neuroglial brain networks, appeared in order to process the information by means of artificial systems based on biological phenomena. They are not only made of artificial neurons, like the artificial neural networks, but also they are made of elements which try to imitate glial cells. An important glial role related with the processing of the brain information has been recently discovered but, as the functioning of the biological neuroglial networks is not exactly known, it is necessary to test several and different possibilities for creating Artificial NeuroGlial Networks. This chapter shows the functioning methodology of the Artificial NeuroGlial Networks and the application of a possible implementation of artificial glia to classification problems.

scholarly journals Brain Networks Associated With COVID-19 Risk: Data From 3662 Participants

2021 ◽  
Vol 5 ◽  
pp. 247054702110667
Author(s):  
Chadi G. Abdallah
Keyword(s):  
Impulse Control ◽  
Brain Networks ◽  
Default Mode ◽  
Behavioral Traits ◽  
Brain Connectome ◽  
Increased Risk ◽  
Machine Learning Approach ◽  
Risk Of Exposure ◽  
The Uk ◽  
The Brain

Background Our behavioral traits, and subsequent actions, could affect the risk of exposure to the coronavirus disease of 2019 (COVID-19). The current study aimed to determine whether unique brain networks are associated with the COVID-19 infection risk. Methods This research was conducted using the UK Biobank Resource. Functional magnetic resonance imaging scans in a cohort of general population (n = 3662) were used to compute the whole-brain functional connectomes. A network-informed machine learning approach was used to identify connectome and nodal fingerprints that are associated with positive COVID-19 status during the pandemic up to February fourth, 2021. Results The predictive models successfully identified 6 fingerprints that were associated with COVID-19 positive, compared to negative status (all p values < 0.005). Overall, lower integration across the brain modules and increased segregation, as reflected by internal within module connectivity, were associated with higher infection rates. More specifically, COVID-19 positive status was associated with 1) reduced connectivity between the central executive and ventral salience, as well as between the dorsal salience and default mode networks; 2) increased internal connectivity within the default mode, ventral salience, subcortical and sensorimotor networks; and 3) increased connectivity between the ventral salience, subcortical and sensorimotor networks. Conclusion Individuals are at increased risk of COVID-19 infections if their brain connectome is consistent with reduced connectivity in the top-down attention and executive networks, along with increased internal connectivity in the introspective and instinctive networks. These identified risk networks could be investigated as target for treatment of illnesses with impulse control deficits.

Principal States of Dynamic Functional Connectivity Reveal the Link Between Resting-State and Task-State Brain: An fMRI Study

2018 ◽  
Vol 28 (07) ◽  
pp. 1850002 ◽  
Author(s):  
Lin Cheng ◽  
Yang Zhu ◽  
Junfeng Sun ◽  
Lifu Deng ◽  
Naying He ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Spatial Patterns ◽  
Resting State ◽  
Network Architecture ◽  
Brain Networks ◽  
Temporal Patterns ◽  
Blood Oxygenation ◽  
Default Mode ◽  
The Brain ◽  
And Task

Task-related reorganization of functional connectivity (FC) has been widely investigated. Under classic static FC analysis, brain networks under task and rest have been demonstrated a general similarity. However, brain activity and cognitive process are believed to be dynamic and adaptive. Since static FC inherently ignores the distinct temporal patterns between rest and task, dynamic FC may be more a suitable technique to characterize the brain’s dynamic and adaptive activities. In this study, we adopted [Formula: see text]-means clustering to investigate task-related spatiotemporal reorganization of dynamic brain networks and hypothesized that dynamic FC would be able to reveal the link between resting-state and task-state brain organization, including broadly similar spatial patterns but distinct temporal patterns. In order to test this hypothesis, this study examined the dynamic FC in default-mode network (DMN) and motor-related network (MN) using Blood-Oxygenation-Level-Dependent (BOLD)-fMRI data from 26 healthy subjects during rest (REST) and a hand closing-and-opening (HCO) task. Two principal FC states in REST and one principal FC state in HCO were identified. The first principal FC state in REST was found similar to that in HCO, which appeared to represent intrinsic network architecture and validated the broadly similar spatial patterns between REST and HCO. However, the second FC principal state in REST with much shorter “dwell time” implied the transient functional relationship between DMN and MN during REST. In addition, a more frequent shifting between two principal FC states indicated that brain network dynamically maintained a “default mode” in the motor system during REST, whereas the presence of a single principal FC state and reduced FC variability implied a more temporally stable connectivity during HCO, validating the distinct temporal patterns between REST and HCO. Our results further demonstrated that dynamic FC analysis could offer unique insights in understanding how the brain reorganizes itself during rest and task states, and the ways in which the brain adaptively responds to the cognitive requirements of tasks.

How the Brain Makes Potential

2017 ◽  
pp. 168-198
Author(s):  
Ken Richardson
Keyword(s):  
Neural Networks ◽  
Individual Differences ◽  
Brain Networks ◽  
Neural System ◽  
Dynamical Processes ◽  
Brain Functions ◽  
Unpredictable Environments ◽  
The Brain

Chapter 6 describes how a “neural” system of intelligence emerged as more changeable environments were encountered. It contrasts the traditional mechanical and computational metaphors of brain functions (largely based on ideological preconceptions) with the emerging concepts of dynamical processes in neural networks. Only the latter can deal with rapidly changing, unpredictable environments. The chapter goes on to critique efforts to relate individual differences in IQ to differences in brain networks using MRI scanning and related methods.

scholarly journals A new era for executive function research: On the transition from centralized to distributed executive functioning

2020 ◽  
Author(s):  
Nicolas Zink ◽  
Sebastian Markett ◽  
Agatha Lenartowicz
Keyword(s):  
Brain Connectivity ◽  
Brain Networks ◽  
Brain Regions ◽  
Central Executive ◽  
Control Mechanisms ◽  
Control Functions ◽  
Wide Range ◽  
Spatially Distributed ◽  
Higher Cognitive Functions ◽  
The Brain

“Executive functions” (EFs) is an umbrella term for higher cognitive functions such as working memory, inhibition, and cognitive flexibility. These functions refer to dissociable mechanisms that are also intricately related, justifying the view of EF as a unitary mental faculty. One of the most challenging theoretical problems in this field of research has been to explain how the wide range of cognitive processes subsumed as EFs are controlled without an all-powerful but ill-defined central executive in the brain. Efforts to localize control mechanisms in circumscribed brain regions have not led to breakthrough in understanding how the brain controls and regulates itself, and no single brain system underlying a ‘central executive’ has yet been identified. We discuss how a distributed control network view can help to refine our understanding of the neurophysiological mechanisms underlying EFs. In this view, executive control functions are realized by spatially distributed brain networks, thus precluding the need for a modular central executive. We further discuss how graph-theory driven analysis of brain networks offers a unique lens on this problem by providing a reference frame to study brain connectivity in EFs in a holistic way and how neuroscience network research endeavors to investigate clinical neuropathology of disrupted EFs.

scholarly journals Probable nature of higher-dimensional symmetries underlying mammalian grid-cell activity patterns

eLife ◽  
2015 ◽  
Vol 4 ◽  
Author(s):  
Alexander Mathis ◽  
Martin B Stemmler ◽  
Andreas VM Herz
Keyword(s):  
Marine Mammals ◽  
Compound Eye ◽  
Activity Patterns ◽  
Cell Activity ◽  
Physical Space ◽  
Grid Cell ◽  
Three Dimensions ◽  
Face Centered Cubic ◽  
Higher Dimensional ◽  
The Brain

Lattices abound in nature—from the crystal structure of minerals to the honey-comb organization of ommatidia in the compound eye of insects. These arrangements provide solutions for optimal packings, efficient resource distribution, and cryptographic protocols. Do lattices also play a role in how the brain represents information? We focus on higher-dimensional stimulus domains, with particular emphasis on neural representations of physical space, and derive which neuronal lattice codes maximize spatial resolution. For mammals navigating on a surface, we show that the hexagonal activity patterns of grid cells are optimal. For species that move freely in three dimensions, a face-centered cubic lattice is best. This prediction could be tested experimentally in flying bats, arboreal monkeys, or marine mammals. More generally, our theory suggests that the brain encodes higher-dimensional sensory or cognitive variables with populations of grid-cell-like neurons whose activity patterns exhibit lattice structures at multiple, nested scales.

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