scholarly journals Higher-order interaction of brain microstructural and functional connectome

2021 ◽  
Author(s):  
Hao Wang ◽  
Hui-Jun Wu ◽  
Yang-Yu Liu ◽  
Linyuan Lu
Keyword(s):  
Structure Function ◽  
Higher Order ◽  
Global Network ◽  
Order Structure ◽  
Sensory Cortices ◽  
Brain Microstructure ◽  
Function Relationship ◽  
The Individual ◽  
Local Circuits ◽  
The Brain

Despite a relatively fixed anatomical structure, the human brain can support rich cognitive functions, triggering particular interest in investigating structure-function relationships. Myelin is a vital brain microstructure marker, yet the individual microstructure-function relationship is poorly understood. Here, we explore the brain microstructure-function relationships using a higher-order framework. Global (network-level) higher-order microstructure-function relationships negatively correlate with male participants' personality scores and decline with aging. Nodal (node-level) higher-order microstructure-function relationships are not aligned uniformly throughout the brain, being stronger in association cortices and lower in sensory cortices, showing gender differences. Notably, higher-order microstructure-function relationships are maintained from the whole-brain to local circuits, which uncovers a compelling and straightforward principle of brain structure-function interactions. Additionally, targeted artificial attacks can disrupt these higher-order relationships, and the main results are robust against several factors. Together, our results increase the collective knowledge of higher-order structure- function interactions that may underlie cognition, individual differences, and aging.

scholarly journals Network-based approaches to quantify multicellular development

2017 ◽  
Vol 14 (135) ◽  
pp. 20170484 ◽  
Author(s):  
Matthew D. B. Jackson ◽  
Salva Duran-Nebreda ◽  
George W. Bassel
Keyword(s):  
Structure Function ◽  
Spatial Relations ◽  
Higher Order ◽  
Cellular Interaction ◽  
Cellular Organization ◽  
Multicellular Development ◽  
Cell Organization ◽  
Function Relationship ◽  
And Function ◽  
The Relationship

Multicellularity and cellular cooperation confer novel functions on organs following a structure–function relationship. How regulated cell migration, division and differentiation events generate cellular arrangements has been investigated, providing insight into the regulation of genetically encoded patterning processes. Much less is known about the higher-order properties of cellular organization within organs, and how their functional coordination through global spatial relations shape and constrain organ function. Key questions to be addressed include: why are cells organized in the way they are? What is the significance of the patterns of cellular organization selected for by evolution? What other configurations are possible? These may be addressed through a combination of global cellular interaction mapping and network science to uncover the relationship between organ structure and function. Using this approach, global cellular organization can be discretized and analysed, providing a quantitative framework to explore developmental processes. Each of the local and global properties of integrated multicellular systems can be analysed and compared across different tissues and models in discrete terms. Advances in high-resolution microscopy and image analysis continue to make cellular interaction mapping possible in an increasing variety of biological systems and tissues, broadening the further potential application of this approach. Understanding the higher-order properties of complex cellular assemblies provides the opportunity to explore the evolution and constraints of cell organization, establishing structure–function relationships that can guide future organ design.

scholarly journals Distinct structure-function relationships across cortical regions and connectivity scales in the rat brain

2019 ◽  
Author(s):  
Milou Straathof ◽  
Michel R.T. Sinke ◽  
Theresia J.M. Roelofs ◽  
Erwin L.A. Blezer ◽  
R. Angela Sarabdjitsingh ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Structure Function ◽  
Rat Brain ◽  
Resting State ◽  
Structural Connectivity ◽  
Distinct Structure ◽  
Macro Scale ◽  
Function Relationship ◽  
The Brain ◽  
Meso Scale

AbstractAn improved understanding of the structure-function relationship in the brain is necessary to know to what degree structural connectivity underpins abnormal functional connectivity seen in many disorders. We integrated high-field resting-state fMRI-based functional connectivity with high-resolution macro-scale diffusion-based and meso-scale neuronal tracer-based structural connectivity, to obtain an accurate depiction of the structure-function relationship in the rat brain. Our main goal was to identify to what extent structural and functional connectivity strengths are correlated, macro- and meso-scopically, across the cortex. Correlation analyses revealed a positive correspondence between functional connectivity and macro-scale diffusion-based structural connectivity, but no correspondence between functional connectivity and meso-scale neuronal tracer-based structural connectivity. Locally, strong functional connectivity was found in two well-known resting-state networks: the sensorimotor and default mode network. Strong functional connectivity within these networks coincided with strong short-range intrahemispheric structural connectivity, but with weak heterotopic interhemispheric and long-range intrahemispheric structural connectivity. Our study indicates the importance of combining measures of connectivity at distinct hierarchical levels to accurately determine connectivity across networks in the healthy and diseased brain. Distinct structure-function relationships across the brain can explain the organization of networks and may underlie variations in the impact of structural damage on functional networks and behavior.

scholarly journals Distinct structure-function relationships across cortical regions and connectivity scales in the rat brain

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Milou Straathof ◽  
Michel R. T. Sinke ◽  
Theresia J. M. Roelofs ◽  
Erwin L. A. Blezer ◽  
R. Angela Sarabdjitsingh ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Structure Function ◽  
Rat Brain ◽  
Resting State ◽  
Structural Connectivity ◽  
Distinct Structure ◽  
Macro Scale ◽  
Function Relationship ◽  
The Brain ◽  
Meso Scale

AbstractAn improved understanding of the structure-function relationship in the brain is necessary to know to what degree structural connectivity underpins abnormal functional connectivity seen in disorders. We integrated high-field resting-state fMRI-based functional connectivity with high-resolution macro-scale diffusion-based and meso-scale neuronal tracer-based structural connectivity, to obtain an accurate depiction of the structure-function relationship in the rat brain. Our main goal was to identify to what extent structural and functional connectivity strengths are correlated, macro- and meso-scopically, across the cortex. Correlation analyses revealed a positive correspondence between functional and macro-scale diffusion-based structural connectivity, but no significant correlation between functional connectivity and meso-scale neuronal tracer-based structural connectivity. Zooming in on individual connections, we found strong functional connectivity in two well-known resting-state networks: the sensorimotor and default mode network. Strong functional connectivity within these networks coincided with strong short-range intrahemispheric structural connectivity, but with weak heterotopic interhemispheric and long-range intrahemispheric structural connectivity. Our study indicates the importance of combining measures of connectivity at distinct hierarchical levels to accurately determine connectivity across networks in the healthy and diseased brain. Although characteristics of the applied techniques may affect where structural and functional networks (dis)agree, distinct structure-function relationships across the brain could also have a biological basis.

Novel Cognitive Functions Arise at the Convergence of Macroscale Gradients

2021 ◽  
pp. 1-16
Author(s):  
Heejung Jung ◽  
Tor D. Wager ◽  
R. McKell Carter
Keyword(s):  
Brain Regions ◽  
Higher Order ◽  
Hierarchical Organization ◽  
Functional Modules ◽  
Cortical Areas ◽  
Future Space ◽  
Close Proximity ◽  
Sensory Cortices ◽  
Developmental Characteristics ◽  
The Brain

Abstract Functions in higher-order brain regions are the source of extensive debate. Although past trends have been to describe the brain—especially posterior cortical areas—in terms of a set of functional modules, a new emerging paradigm focuses on the integration of proximal functions. In this review, we synthesize emerging evidence that a variety of novel functions in the higher-order brain regions are due to convergence: convergence of macroscale gradients brings feature-rich representations into close proximity, presenting an opportunity for novel functions to arise. Using the TPJ as an example, we demonstrate that convergence is enabled via three properties of the brain: (1) hierarchical organization, (2) abstraction, and (3) equidistance. As gradients travel from primary sensory cortices to higher-order brain regions, information becomes abstracted and hierarchical, and eventually, gradients meet at a point maximally and equally distant from their sensory origins. This convergence, which produces multifaceted combinations, such as mentalizing another person's thought or projecting into a future space, parallels evolutionary and developmental characteristics in such regions, resulting in new cognitive and affective faculties.

Consciousness in Animals

2019 ◽  
pp. 47-63
Author(s):  
Mark Rowlands
Keyword(s):  
Mental State ◽  
Higher Order ◽  
Strongly Correlated ◽  
The Individual ◽  
Low Amplitude ◽  
The Brain

An animal is phenomenally conscious if there is something it is like to be that animal. There are excellent scientific reasons for thinking that many animals are phenomenally conscious. In humans, consciousness is strongly correlated with widespread, relatively fast, low-amplitude interactions in the thalamocortical region of the brain. When the brains of many animals are examined, precisely this sort of activity in these areas is found. The primary philosophical objection to the idea that animals are phenomenally conscious is based on the higher-order thought (HOT) model of consciousness, according to which mental state is conscious when, and only when, the individual who has it is conscious of it. The HOT account suffers from a number of fatal difficulties.

scholarly journals Highlighting the Structure-Function Relationship of the Brain with the Ising Model and Graph Theory

2014 ◽  
Vol 2014 ◽  
pp. 1-14 ◽  
Author(s):  
T. K. Das ◽  
P. M. Abeyasinghe ◽  
J. S. Crone ◽  
A. Sosnowski ◽  
S. Laureys ◽  
...  
Keyword(s):  
Ising Model ◽  
Structure Function ◽  
Cognitive Task ◽  
Distribution Patterns ◽  
Kuramoto Model ◽  
Global Dynamics ◽  
Brain Organization ◽  
Function Relationship ◽  
The Kuramoto Model ◽  
The Brain

With the advent of neuroimaging techniques, it becomes feasible to explore the structure-function relationships in the brain. When the brain is not involved in any cognitive task or stimulated by any external output, it preserves important activities which follow well-defined spatial distribution patterns. Understanding the self-organization of the brain from its anatomical structure, it has been recently suggested to model the observed functional pattern from the structure of white matter fiber bundles. Different models which study synchronization (e.g., the Kuramoto model) or global dynamics (e.g., the Ising model) have shown success in capturing fundamental properties of the brain. In particular, these models can explain the competition between modularity and specialization and the need for integration in the brain. Graphing the functional and structural brain organization supports the model and can also highlight the strategy used to process and organize large amount of information traveling between the different modules. How the flow of information can be prevented or partially destroyed in pathological states, like in severe brain injured patients with disorders of consciousness or by pharmacological induction like in anaesthesia, will also help us to better understand how global or integrated behavior can emerge from local and modular interactions.

scholarly journals Biologically plausible learning in a deep recurrent spiking network

2019 ◽  
Author(s):  
David Rotermund ◽  
Klaus R. Pawelzik
Keyword(s):  
Large Scale ◽  
Fundamental Property ◽  
Learning Rule ◽  
Spike Timing ◽  
Spiking Neurons ◽  
Global Network ◽  
Large Network ◽  
Deep Networks ◽  
Local Circuits ◽  
The Brain

AbstractArtificial deep convolutional networks (DCNs) meanwhile beat even human performance in challenging tasks. Recently DCNs were shown to also predict real neuronal responses. Their relevance for understanding the neuronal networks in the brain, however, remains questionable. In contrast to the unidirectional architecture of DCNs neurons in cortex are recurrently connected and exchange signals by short pulses, the action potentials. Furthermore, learning in the brain is based on local synaptic mechanisms, in stark contrast to the global optimization methods used in technical deep networks. What is missing is a similarly powerful approach with spiking neurons that employs local synaptic learning mechanisms for optimizing global network performance. Here, we present a framework consisting of mutually coupled local circuits of spiking neurons. The dynamics of the circuits is derived from first principles to optimally encode their respective inputs. From the same global objective function a local learning rule is derived that corresponds to spike-timing dependent plasticity of the excitatory inter-circuit synapses. For deep networks built from these circuits self-organization is based on the ensemble of inputs while for supervised learning the desired outputs are applied in parallel as additional inputs to output layers.Generality of the approach is shown with Boolean functions and its functionality is demonstrated with an image classification task, where networks of spiking neurons approach the performance of their artificial cousins. Since the local circuits operate independently and in parallel, the novel framework not only meets a fundamental property of the brain but also allows for the construction of special hardware. We expect that this will in future enable investigations of very large network architectures far beyond current DCNs, including also large scale models of cortex where areas consisting of many local circuits form a complex cyclic network.

The influence of coherent structures and microfronts on scaling laws using global and local transforms

1994 ◽  
Vol 260 ◽  
pp. 247-270 ◽  
Author(s):  
L. Mahrt ◽  
J. F. Howell
Keyword(s):  
Energy Transfer ◽  
Structure Function ◽  
Coherent Structures ◽  
Scaling Laws ◽  
Fourier Spectrum ◽  
Random Phase ◽  
Higher Order ◽  
Basis Set ◽  
Small Scale ◽  
Order Structure

This study examines the influence of coherent structures and attendant microfronts on scaling laws. Toward this goal, we analyse atmospheric observations of turbulence collected 45 m above a flat surface during the Lammefjord Experiment in Denmark. These observations represent more than 40 hours of nearly stationary strong wind conditions and include more than 1600 samples of the main coherent structures. These samples occupy about 40% of the total record and explain the majority of the Reynolds stress.To study the dependence of the scaling laws on the choice of basis set, the time series of velocity fluctuations are decomposed into Fourier modes, the local Haar basis set and eigenvectors of the lagged covariance matrix. The three decompositions are compared by formulating joint projections. The decompositions are first applied to the samples of phased-locked coherent structures centred about eddy microfronts. The eigenvector decomposition is able to partially separate the small-scale variances due to the coherent eddy microfronts from that due to the small-scale structure with random phase. In the Fourier spectrum, both of these contributions to the variance appear together at the higher wavenumbers and their individual contributions cannot be separated. This effect is relatively minor for the scale distribution of energy but exerts an important influence on higher-moment statistics. Deviations from the −$\frac53$ scaling are observed to be slight and depend on choice of basis set.The microfronts strongly influence the higher-order statistics such as the sixth-order structure function traditionally used to estimate the energy transfer variance. The intermittency of fine-scale structure, energy transfer variance and dissipation are not completely characterized by random phase, as often assumed, but are partly associated with microfronts characterized by systematic phase with respect to the main transporting eddies. These conclusions are supported by both the higher-order structure function and the higher-order Haar transform.The Fourier and Haar spectra are also computed for the entire record. The peak of the Haar energy spectrum occurs at smaller scales than those of the Fourier spectrum. The Haar transform is local and emphasizes the width of the events. The Fourier spectrum peaks at the scale of the main periodicity, if it exists, which includes the spacing between the events.

scholarly journals Time-resolved structure-function coupling in brain networks

2021 ◽  
Author(s):  
Zhen-Qi Liu ◽  
Bertha Vazquez-Rodriguez ◽  
R. Nathan Spreng ◽  
Boris Bernhardt ◽  
Richard F. Betzel ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Structure Function ◽  
Insular Cortex ◽  
Dynamic Structure ◽  
Time Resolved ◽  
Informative Feature ◽  
Dorsal Attention Network ◽  
Function Relationship ◽  
The Relationship ◽  
The Brain

The relationship between structural and functional connectivity in the brain is a key question in systems neuroscience. Modern accounts assume a single global structure-function relationship that persists over time. Here we show that structure-function coupling is dynamic and regionally heterogeneous. We use a temporal unwrapping procedure to identify moment-to-moment co-fluctuations in neural activity, and reconstruct time-resolved structure-function coupling patterns. We find that patterns of dynamic structure-function coupling are highly organized across the cortex. These patterns reflect cortical hierarchies, with stable coupling in unimodal and transmodal cortex, and dynamic coupling in intermediate regions, particularly in insular cortex (salience network) and frontal eye fields (dorsal attention network). Finally, we show that the variability of structure-function coupling is shaped by the distribution of connection lengths. The time-varying coupling of structural and functional connectivity points towards an informative feature of the brain that may reflect how cognitive functions are flexibly deployed and implemented.

scholarly journals What We Know About the Brain Structure–Function Relationship

2018 ◽  
Vol 8 (4) ◽  
pp. 39 ◽  
Author(s):  
Karla Batista-García-Ramó ◽  
Caridad Fernández-Verdecia
Keyword(s):  
Structure Function ◽  
Brain Structure ◽  
Structure Function Relationship ◽  
Function Relationship ◽  
The Brain
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