scholarly journals Breakdown of Whole-brain Dynamics in Preterm-born Children

2019 ◽  
Vol 30 (3) ◽  
pp. 1159-1170 ◽  
Author(s):  
Nelly Padilla ◽  
Victor M Saenger ◽  
Tim J van Hartevelt ◽  
Henrique M Fernandes ◽  
Finn Lennartsson ◽  
...  
Keyword(s):  
Activity Patterns ◽  
Brain Magnetic Resonance Imaging ◽  
Imaging Data ◽  
Whole Brain ◽  
Rich Club ◽  
Extremely Preterm ◽  
The Rich ◽  
Preterm Born ◽  
Brain Reorganization ◽  
The Brain

Abstract The brain operates at a critical point that is balanced between order and disorder. Even during rest, unstable periods of random behavior are interspersed with stable periods of balanced activity patterns that support optimal information processing. Being born preterm may cause deviations from this normal pattern of development. We compared 33 extremely preterm (EPT) children born at < 27 weeks of gestation and 28 full-term controls. Two approaches were adopted in both groups, when they were 10 years of age, using structural and functional brain magnetic resonance imaging data. The first was using a novel intrinsic ignition analysis to study the ability of the areas of the brain to propagate neural activity. The second was a whole-brain Hopf model, to define the level of stability, desynchronization, or criticality of the brain. EPT-born children exhibited fewer intrinsic ignition events than controls; nodes were related to less sophisticated aspects of cognitive control, and there was a different hierarchy pattern in the propagation of information and suboptimal synchronicity and criticality. The largest differences were found in brain nodes belonging to the rich-club architecture. These results provide important insights into the neural substrates underlying brain reorganization and neurodevelopmental impairments related to prematurity.

scholarly journals Structural network maturation of the preterm human brain

2017 ◽  
Author(s):  
Tengda Zhao ◽  
Virendra Mishra ◽  
Tina Jeon ◽  
Minhui Ouyang ◽  
Qinmu Peng ◽  
...  
Keyword(s):  
Human Brain ◽  
Large Scale ◽  
Structural Connectivity ◽  
Rich Club ◽  
Brain Connectome ◽  
Brain Circuits ◽  
Edge Strength ◽  
The Rich ◽  
Preterm Born ◽  
The Brain

AbstractDuring the 3rd trimester, large-scale of neural circuits are formed in the human brain, resulting in the adult-like brain networks at birth. However, how the brain circuits develop into a highly efficient and segregated connectome during this period is unknown. We hypothesized that faster increases of connectivity efficiency and strength at the brain hubs and rich-club are critical for emergence of an efficient and segregated brain connectome. Here, using high resolution diffusion MRI of 77 preterm-born and term-born neonates scanned at 31-42 postmenstrual weeks (PMW), we constructed the structural connectivity matrices and performed graph-theory-based analyses. We found faster increases of nodal efficiency mainly at the brain hubs, distributed in primary sensorimotor regions, superior-middle frontal and posterior cingulate gyrus during 31-42PMW. The rich-club and within-module connections were characterized by higher rates of edge strength increases. Edge strength of short-range connections increased faster than that of long-range connections. The nodal efficiencies of the hubs predicted individual postmenstrual ages more accurately than those of non-hubs. Collectively, these findings revealed regionally differentiated maturation in the baby brain structural connectome and more rapid increases of the hub and rich-club connections, which underlie network segregation and differentiated brain function emergence.

scholarly journals The rich club of the brain in bipolar disorder

2015 ◽  
Vol 9 ◽  
Author(s):  
Lord Anton ◽  
Roberts Gloria ◽  
Breakspear Michael ◽  
Mitchell Phillip
Keyword(s):  
Bipolar Disorder ◽  
Rich Club ◽  
The Rich ◽  
The Brain

scholarly journals Whole-Brain Dynamics in Aging: Disruptions in Functional Connectivity and the Role of the Rich Club

2020 ◽  
Author(s):  
Anira Escrichs ◽  
Carles Biarnes ◽  
Josep Garre-Olmo ◽  
José Manuel Fernández-Real ◽  
Rafel Ramos ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Functional Network ◽  
Brain Dynamics ◽  
Whole Brain ◽  
Rich Club ◽  
Brain Functional Network ◽  
Age Related ◽  
Age Range ◽  
The Brain

Abstract Normal aging causes disruptions in the brain that can lead to cognitive decline. Resting-state functional magnetic resonance imaging studies have found significant age-related alterations in functional connectivity across various networks. Nevertheless, most of the studies have focused mainly on static functional connectivity. Studying the dynamics of resting-state brain activity across the whole-brain functional network can provide a better characterization of age-related changes. Here, we employed two data-driven whole-brain approaches based on the phase synchronization of blood-oxygen-level-dependent signals to analyze resting-state fMRI data from 620 subjects divided into two groups (middle-age group (n = 310); age range, 50–64 years versus older group (n = 310); age range, 65–91 years). Applying the intrinsic-ignition framework to assess the effect of spontaneous local activation events on local–global integration, we found that the older group showed higher intrinsic ignition across the whole-brain functional network, but lower metastability. Using Leading Eigenvector Dynamics Analysis, we found that the older group showed reduced ability to access a metastable substate that closely overlaps with the so-called rich club. These findings suggest that functional whole-brain dynamics are altered in aging, probably due to a deficiency in a metastable substate that is key for efficient global communication in the brain.

Configuration and dynamics of dominant inspiratory multineuronal activity patterns during eupnea and gasping generation in vitro

2021 ◽  
Author(s):  
Josué de Jesús Juárez-Vidales ◽  
Jesús Esteban Pérez-Ortega ◽  
Jonathan Julio Ismael Lorea-Hernández ◽  
Felipe A. Méndez-Salcido ◽  
Fernando Pena-Ortega
Keyword(s):  
Activity Patterns ◽  
Respiratory Neurons ◽  
Core Network ◽  
Population Activity ◽  
Scale Free ◽  
Rich Club ◽  
Wide Range ◽  
Multineuronal Activity ◽  
The Rich

The preBötzinger complex (preBötC), located within the ventral respiratory column, produces inspiratory bursts in varying degrees of synchronization/amplitude. This wide range of population burst patterns reflects the flexibility of the preBötC neurons, which is expressed in variations in the onset/offset times of their activations and their activity during the population bursts, with respiratory neurons exhibiting a large cycle-to-cycle timing jitter both at the population activity onset and at the population activity peak; suggesting that respiratory neurons are stochastically activated before and during the inspiratory bursts. However, it is still unknown whether this stochasticity is maintained while evaluating the coactivity of respiratory neuronal ensembles. Moreover, the preBötC topology also remains unknown. Here, by simultaneously recording tens of preBötC neurons and using coactivation analysis during the inspiratory periods, we found that the preBötC has a scale-free configuration (mixture of not many highly connected nodes -hubs- with abundant poorly connected elements) exhibiting the rich-club phenomenon (hubs more likely interconnected with each other). PreBötC neurons also produce multineuronal activity patterns (MAPs) that are highly stable and change during the hypoxia-induced reconfiguration. Moreover, preBötC contains a coactivating core network shared by all its MAPs. Finally, we found a distinctive pattern of sequential coactivation of core network neurons at the beginning of the inspiratory periods, indicating that, when evaluated at the multicellular level, the coactivation of respiratory neurons seems not to be stochastic.

scholarly journals Reduced rich-club connectivity is related to disability in primary progressive MS

2017 ◽  
Vol 4 (5) ◽  
pp. e375 ◽  
Author(s):  
Jan-Patrick Stellmann ◽  
Sibylle Hodecker ◽  
Bastian Cheng ◽  
Nadine Wanke ◽  
Kim Lea Young ◽  
...  
Keyword(s):  
Hand Function ◽  
Structural Connectivity ◽  
Rank Correlation ◽  
Insular Cortex ◽  
Healthy Controls ◽  
Imaging Data ◽  
Information Processing Speed ◽  
Primary Progressive ◽  
Rich Club ◽  
The Rich

Objective:To investigate whether the structural connectivity of the brain's rich-club organization is altered in patients with primary progressive MS and whether such changes to this fundamental network feature are associated with disability measures.Methods:We recruited 37 patients with primary progressive MS and 21 healthy controls for an observational cohort study. Structural connectomes were reconstructed based on diffusion-weighted imaging data using probabilistic tractography and analyzed with graph theory.Results:We observed the same topological organization of brain networks in patients and controls. Consistent with the originally defined rich-club regions, we identified superior frontal, precuneus, superior parietal, and insular cortex in both hemispheres as rich-club nodes. Connectivity within the rich club was significantly reduced in patients with MS (p = 0.039). The extent of reduced rich-club connectivity correlated with clinical measurements of mobility (Kendall rank correlation coefficient τ = −0.20, p = 0.047), hand function (τ = −0.26, p = 0.014), and information processing speed (τ = −0.20, p = 0.049).Conclusions:In patients with primary progressive MS, the fundamental organization of the structural connectome in rich-club and peripheral nodes was preserved and did not differ from healthy controls. The proportion of rich-club connections was altered and correlated with disability measures. Thus, the rich-club organization of the brain may be a promising network phenotype for understanding the patterns and mechanisms of neurodegeneration in MS.

scholarly journals Whole-central nervous system functional imaging in larval Drosophila

2015 ◽  
Vol 6 (1) ◽  
Author(s):  
William C. Lemon ◽  
Stefan R. Pulver ◽  
Burkhard Höckendorf ◽  
Katie McDole ◽  
Kristin Branson ◽  
...  
Keyword(s):  
Central Nervous System ◽  
Nervous System ◽  
Nerve Cord ◽  
Ventral Nerve Cord ◽  
Activity Patterns ◽  
Network Activity ◽  
Imaging Data ◽  
Spatiotemporal Resolution ◽  
Functional Connections ◽  
The Brain

Abstract Understanding how the brain works in tight concert with the rest of the central nervous system (CNS) hinges upon knowledge of coordinated activity patterns across the whole CNS. We present a method for measuring activity in an entire, non-transparent CNS with high spatiotemporal resolution. We combine a light-sheet microscope capable of simultaneous multi-view imaging at volumetric speeds 25-fold faster than the state-of-the-art, a whole-CNS imaging assay for the isolated Drosophila larval CNS and a computational framework for analysing multi-view, whole-CNS calcium imaging data. We image both brain and ventral nerve cord, covering the entire CNS at 2 or 5 Hz with two- or one-photon excitation, respectively. By mapping network activity during fictive behaviours and quantitatively comparing high-resolution whole-CNS activity maps across individuals, we predict functional connections between CNS regions and reveal neurons in the brain that identify type and temporal state of motor programs executed in the ventral nerve cord.

scholarly journals Dwelling quietly in the rich club: brain network determinants of slow cortical fluctuations

2015 ◽  
Vol 370 (1668) ◽  
pp. 20140165 ◽  
Author(s):  
Leonardo L. Gollo ◽  
Andrew Zalesky ◽  
R. Matthew Hutchison ◽  
Martijn van den Heuvel ◽  
Michael Breakspear
Keyword(s):  
Spatial Scales ◽  
Brain Regions ◽  
Brain Network ◽  
Morphological Properties ◽  
Brain Organization ◽  
Rich Club ◽  
Guiding Principle ◽  
The Rich ◽  
Cortical Regions ◽  
The Brain

For more than a century, cerebral cartography has been driven by investigations of structural and morphological properties of the brain across spatial scales and the temporal/functional phenomena that emerge from these underlying features. The next era of brain mapping will be driven by studies that consider both of these components of brain organization simultaneously—elucidating their interactions and dependencies. Using this guiding principle, we explored the origin of slowly fluctuating patterns of synchronization within the topological core of brain regions known as the rich club, implicated in the regulation of mood and introspection. We find that a constellation of densely interconnected regions that constitute the rich club (including the anterior insula, amygdala and precuneus) play a central role in promoting a stable, dynamical core of spontaneous activity in the primate cortex. The slow timescales are well matched to the regulation of internal visceral states, corresponding to the somatic correlates of mood and anxiety. In contrast, the topology of the surrounding ‘feeder’ cortical regions shows unstable, rapidly fluctuating dynamics likely to be crucial for fast perceptual processes. We discuss these findings in relation to psychiatric disorders and the future of connectomics.

scholarly journals A Deep Learning-Based Approach for Detection of Dementia from Brain Mri

2021 ◽  
Vol 23 (07) ◽  
pp. 516-529
Author(s):  
Reshma L ◽  
◽  
Sai Priya Nalluri ◽  
Priya R Sankpal ◽  
◽  
...  
Keyword(s):  
Deep Learning ◽  
Brain Mri ◽  
Brain Magnetic Resonance Imaging ◽  
Support Vector ◽  
Imaging Data ◽  
Diagnosis Of Dementia ◽  
User Friendly ◽  
Clustering Pattern ◽  
Medical Analysis ◽  
The Brain

In this paper, a user-friendly system has been developed which will provide the result of medical analysis of digital images like magnetization resonance of image scan of the brain for detection and classification of dementia. The small structural differences in the brain can slowly and gradually become a major disease like dementia. The progression of dementia can be slowed when identified early. Hence, this paper aims at developing a robust system for classification and identifying dementia at the earliest. The method used in this paper for initial disclosure and diagnosis of dementia is deep learning since it can give important results in a shorter period of time. Deep Learning methods such as K-means clustering, Pattern Recognition, and Multi-class Support Vector Machine (SVM) have been used to classify different stages of dementia. The goal of this study is to provide a user interface for deep learning-based dementia classification using brain magnetic resonance imaging data. The results show that the created method has an accuracy of 96% and may be utilized to detect people who have dementia or are in the early stages of dementia.

scholarly journals Whole-brain dynamics in aging: disruptions in functional connectivity and the role of the rich club

2020 ◽  
Author(s):  
Anira Escrichs ◽  
Carles Biarnes ◽  
Josep Garre-Olmo ◽  
José Manuel Fernández-Real ◽  
Rafel Ramos ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Resting State Fmri ◽  
Brain Dynamics ◽  
Whole Brain ◽  
Rich Club ◽  
Brain Functional Network ◽  
Age Related ◽  
Age Range ◽  
The Brain

AbstractNormal aging causes disruptions in the brain that can lead to cognitive decline. Resting-state fMRI studies have found significant age-related alterations in functional connectivity across various networks. Nevertheless, most of the studies have focused mainly on static functional connectivity. Studying the dynamics of resting-state brain activity across the whole-brain functional network can provide a better characterization of age-related changes. Here we employed two data-driven whole-brain approaches based on the phase synchronization of blood-oxygen-level-dependent (BOLD) signals to analyze resting-state fMRI data from 620 subjects divided into two groups (‘middle-age group’ (n=310); age range, 50-65 years vs. ‘older group’ (n=310); age range, 66-91 years). Applying the Intrinsic-Ignition Framework to assess the effect of spontaneous local activation events on local-global integration, we found that the older group showed higher intrinsic ignition across the whole-brain functional network, but lower metastability. Using Leading Eigenvector Dynamics Analysis, we found that the older group showed reduced ability to access a metastable substate that closely overlaps with the so-called rich club. These findings suggest that functional whole-brain dynamics are altered in aging, probably due to a deficiency in a metastable substate that is key for efficient global communication in the brain.

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