scholarly journals Brain network dynamics are hierarchically organized in time

2017 ◽  
Vol 114 (48) ◽  
pp. 12827-12832 ◽  
Author(s):  
Diego Vidaurre ◽  
Stephen M. Smith ◽  
Mark W. Woolrich
Keyword(s):  
Large Scale ◽  
Temporal Dynamics ◽  
Brain Network ◽  
Specific Measure ◽  
Brain Areas ◽  
Neuronal Populations ◽  
Subject Specific ◽  
Large Scale Networks ◽  
Network Patterns ◽  
The Brain

The brain recruits neuronal populations in a temporally coordinated manner in task and at rest. However, the extent to which large-scale networks exhibit their own organized temporal dynamics is unclear. We use an approach designed to find repeating network patterns in whole-brain resting fMRI data, where networks are defined as graphs of interacting brain areas. We find that the transitions between networks are nonrandom, with certain networks more likely to occur after others. Further, this nonrandom sequencing is itself hierarchically organized, revealing two distinct sets of networks, or metastates, that the brain has a tendency to cycle within. One metastate is associated with sensory and motor regions, and the other involves areas related to higher order cognition. Moreover, we find that the proportion of time that a subject spends in each brain network and metastate is a consistent subject-specific measure, is heritable, and shows a significant relationship with cognitive traits.

scholarly journals Post-Stroke Cognitive Impairment: Pathophysiological Insights into Brain Disconnectome from Advanced Neuroimaging Analysis Techniques

2021 ◽  
Vol 23 (3) ◽  
pp. 297-311
Author(s):  
Jae-Sung Lim ◽  
Jae-Joong Lee ◽  
Choong-Wan Woo
Keyword(s):  
Cognitive Impairment ◽  
Large Scale ◽  
Imaging Techniques ◽  
Brain Network ◽  
Clinical Neurology ◽  
Post Stroke ◽  
Large Scale Networks ◽  
High Level ◽  
New Treatments ◽  
The Brain

The neurological symptoms of stroke have traditionally provided the foundation for functional mapping of the brain. However, there are many unresolved aspects in our understanding of cerebral activity, especially regarding high-level cognitive functions. This review provides a comprehensive look at the pathophysiology of post-stroke cognitive impairment in light of recent findings from advanced imaging techniques. Combining network neuroscience and clinical neurology, our research focuses on how changes in brain networks correlate with post-stroke cognitive prognosis. More specifically, we first discuss the general consequences of stroke lesions due to damage of canonical resting-state large-scale networks or changes in the composition of the entire brain. We also review emerging methods, such as lesion-network mapping and gradient analysis, used to study the aforementioned events caused by stroke lesions. Lastly, we examine other patient vulnerabilities, such as superimposed amyloid pathology and blood-brain barrier leakage, which potentially lead to different outcomes for the brain network compositions even in the presence of similar stroke lesions. This knowledge will allow a better understanding of the pathophysiology of post-stroke cognitive impairment and provide a theoretical basis for the development of new treatments, such as neuromodulation.

scholarly journals Cerebral cartography and connectomics

2015 ◽  
Vol 370 (1668) ◽  
pp. 20140173 ◽  
Author(s):  
Olaf Sporns
Keyword(s):  
Large Scale ◽  
Computational Models ◽  
Network Science ◽  
Temporal Dynamics ◽  
Functional Organization ◽  
Brain Connectivity ◽  
Hierarchical Organization ◽  
Large Scale Networks ◽  
Definition Of ◽  
The Brain

Cerebral cartography and connectomics pursue similar goals in attempting to create maps that can inform our understanding of the structural and functional organization of the cortex. Connectome maps explicitly aim at representing the brain as a complex network, a collection of nodes and their interconnecting edges. This article reflects on some of the challenges that currently arise in the intersection of cerebral cartography and connectomics. Principal challenges concern the temporal dynamics of functional brain connectivity, the definition of areal parcellations and their hierarchical organization into large-scale networks, the extension of whole-brain connectivity to cellular-scale networks, and the mapping of structure/function relations in empirical recordings and computational models. Successfully addressing these challenges will require extensions of methods and tools from network science to the mapping and analysis of human brain connectivity data. The emerging view that the brain is more than a collection of areas, but is fundamentally operating as a complex networked system, will continue to drive the creation of ever more detailed and multi-modal network maps as tools for on-going exploration and discovery in human connectomics.

A new method to predict anomaly in brain network based on graph deep learning

2020 ◽  
Vol 31 (6) ◽  
pp. 681-689
Author(s):  
Jalal Mirakhorli ◽  
Hamidreza Amindavar ◽  
Mojgan Mirakhorli
Keyword(s):  
Deep Learning ◽  
Large Scale ◽  
Brain Plasticity ◽  
Brain Network ◽  
High Order ◽  
Brain Diseases ◽  
Simultaneous Occurrence ◽  
Generative Adversarial Network ◽  
The Brain ◽  
Brain Connections

AbstractFunctional magnetic resonance imaging a neuroimaging technique which is used in brain disorders and dysfunction studies, has been improved in recent years by mapping the topology of the brain connections, named connectopic mapping. Based on the fact that healthy and unhealthy brain regions and functions differ slightly, studying the complex topology of the functional and structural networks in the human brain is too complicated considering the growth of evaluation measures. One of the applications of irregular graph deep learning is to analyze the human cognitive functions related to the gene expression and related distributed spatial patterns. Since a variety of brain solutions can be dynamically held in the neuronal networks of the brain with different activity patterns and functional connectivity, both node-centric and graph-centric tasks are involved in this application. In this study, we used an individual generative model and high order graph analysis for the region of interest recognition areas of the brain with abnormal connection during performing certain tasks and resting-state or decompose irregular observations. Accordingly, a high order framework of Variational Graph Autoencoder with a Gaussian distributer was proposed in the paper to analyze the functional data in brain imaging studies in which Generative Adversarial Network is employed for optimizing the latent space in the process of learning strong non-rigid graphs among large scale data. Furthermore, the possible modes of correlations were distinguished in abnormal brain connections. Our goal was to find the degree of correlation between the affected regions and their simultaneous occurrence over time. We can take advantage of this to diagnose brain diseases or show the ability of the nervous system to modify brain topology at all angles and brain plasticity according to input stimuli. In this study, we particularly focused on Alzheimer’s disease.

scholarly journals A Large-scale Comparison of Cortical and Subcortical Structural Segmentation Methods in Alzheimer’s Disease: a Statistical Approach

2020 ◽  
Author(s):  
Jafar Zamani ◽  
Ali Sadr ◽  
Amir-Homayoun Javadi
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Large Scale ◽  
Automated Segmentation ◽  
Resonance Imaging ◽  
Brain Areas ◽  
Segmentation Methods ◽  
Magnetic Resonance Imaging Mri ◽  
Underlying Mechanisms ◽  
The Brain

AbstractBackgroundAlzheimer’s disease (AD) is a neurodegenerative disease that leads to anatomical atrophy, as evidenced by magnetic resonance imaging (MRI). Automated segmentation methods are developed to help with the segmentation of different brain areas. However, their reliability has yet to be fully investigated. To have a more comprehensive understanding of the distribution of changes in AD, as well as investigating the reliability of different segmentation methods, in this study we compared volumes of cortical and subcortical brain segments, using automated segmentation methods in more than 60 areas between AD and healthy controls (HC).MethodsA total of 44 MRI images (22 AD and 22 HC, 50% females) were taken from the minimal interval resonance imaging in Alzheimer’s disease (MIRIAD) dataset. HIPS, volBrain, CAT and BrainSuite segmentation methods were used for the subfields of hippocampus, and the rest of the brain.ResultsWhile HIPS, volBrain and CAT showed strong conformity with the past literature, BrainSuite misclassified several brain areas. Additionally, the volume of the brain areas that successfully discriminated between AD and HC showed a correlation with mini mental state examination (MMSE) scores. The two methods of volBrain and CAT showed a very strong correlation. These two methods, however, did not correlate with BrainSuite.ConclusionOur results showed that automated segmentation methods HIPS, volBrain and CAT can be used in the classification of AD and HC. This is an indication that such methods can be used to inform researchers and clinicians of underlying mechanisms and progression of AD.

scholarly journals Amplification and Suppression of Distinct Brain-wide Activity Patterns by Catecholamines

2018 ◽  
Author(s):  
RL van den Brink ◽  
S Nieuwenhuis ◽  
TH Donner
Keyword(s):  
Spatial Distribution ◽  
Human Brain ◽  
Spatial Patterns ◽  
Large Scale ◽  
Network Dynamics ◽  
Brain Network ◽  
Heterogeneous Distribution ◽  
Neurotransmitter Systems ◽  
The Brain ◽  
Catecholamine Levels

ABSTRACTThe widely projecting catecholaminergic (norepinephrine and dopamine) neurotransmitter systems profoundly shape the state of neuronal networks in the forebrain. Current models posit that the effects of catecholaminergic modulation on network dynamics are homogenous across the brain. However, the brain is equipped with a variety of catecholamine receptors with distinct functional effects and heterogeneous density across brain regions. Consequently, catecholaminergic effects on brain-wide network dynamics might be more spatially specific than assumed. We tested this idea through the analysis of functional magnetic resonance imaging (fMRI) measurements performed in humans (19 females, 5 males) at ‘rest’ under pharmacological (atomoxetine-induced) elevation of catecholamine levels. We used a linear decomposition technique to identify spatial patterns of correlated fMRI signal fluctuations that were either increased or decreased by atomoxetine. This yielded two distinct spatial patterns, each expressing reliable and specific drug effects. The spatial structure of both fluctuation patterns resembled the spatial distribution of the expression of catecholamine receptor genes: α1 norepinephrine receptors (for the fluctuation pattern: placebo > atomoxetine), ‘D2-like’ dopamine receptors (pattern: atomoxetine > placebo), and β norepinephrine receptors (for both patterns, with correlations of opposite sign). We conclude that catecholaminergic effects on the forebrain are spatially more structured than traditionally assumed and at least in part explained by the heterogeneous distribution of various catecholamine receptors. Our findings link catecholaminergic effects on large-scale brain networks to low-level characteristics of the underlying neurotransmitter systems. They also provide key constraints for the development of realistic models of neuromodulatory effects on large-scale brain network dynamics.SIGNIFICANCE STATEMENTThe catecholamines norepinephrine and dopamine are an important class of modulatory neurotransmitters. Because of the widespread and diffuse release of these neuromodulators, it has commonly been assumed that their effects on neural interactions are homogenous across the brain. Here, we present results from the human brain that challenge this view. We pharmacologically increased catecholamine levels and imaged the effects on the spontaneous covariations between brain-wide fMRI signals at ‘rest’. We identified two distinct spatial patterns of covariations: one that was amplified and another that was suppressed by catecholamines. Each pattern was associated with the heterogeneous spatial distribution of the expression of distinct catecholamine receptor genes. Our results provide novel insights into the catecholaminergic modulation of large-scale human brain dynamics.

Large-scale plastic changes of the brain network in an animal model of neuropathic pain

NeuroImage ◽  
2014 ◽  
Vol 98 ◽  
pp. 203-215 ◽  
Author(s):  
Chang-Eop Kim ◽  
Yu Kyeong Kim ◽  
Geehoon Chung ◽  
Jae Min Jeong ◽  
Dong Soo Lee ◽  
...  
Keyword(s):  
Animal Model ◽  
Neuropathic Pain ◽  
Large Scale ◽  
Brain Network ◽  
Plastic Changes ◽  
The Brain

How small can the epileptogenic region be?

Neurology ◽  
2017 ◽  
Vol 88 (21) ◽  
pp. 2017-2019 ◽  
Author(s):  
Graeme D. Jackson ◽  
Mangor Pedersen ◽  
A. Simon Harvey
Keyword(s):  
Large Scale ◽  
Intractable Epilepsy ◽  
Brain Network ◽  
Local Connectivity ◽  
Whole Brain ◽  
Network Characteristics ◽  
Small Areas ◽  
Language Deficit ◽  
Large Scale Networks ◽  
Combine Information

Objective:To present a case that demonstrates that seizures and interictal disturbances can be driven by a small area of functionally abnormal cortex.Methods:Two novel functional MRI network analysis methods were used to supplement conventional seizure and lesion localization methods: (1) regional homogeneity to quantify local connectivity, or synchrony, with a resolution of less than 1 cm3 of cortex; and (2) small-worldness to combine information about whole brain network segregation and integration.Results:After a small corticectomy in the dominant supramarginal gyrus (13 × 7 × 6 mm) limited to the area of abnormal local connectivity, and smaller than the PET and SPECT abnormalities, the patient has been seizure-free for 3 years with no language deficit. Whole brain network characteristics normalized (small-worldness) to that of healthy controls.Conclusions:This case demonstrates that small areas of cortex may be highly epileptogenic, drive intractable epilepsy, and disrupt large-scale networks likely to be involved in core cognitive functions.

scholarly journals Early alterations of large-scale brain networks temporal dynamics in young children with autism

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Aurélie Bochet ◽  
Holger Franz Sperdin ◽  
Tonia Anahi Rihs ◽  
Nada Kojovic ◽  
Martina Franchini ◽  
...  
Keyword(s):  
Large Scale ◽  
Developmental Stages ◽  
Temporal Dynamics ◽  
Brain Networks ◽  
Autism Spectrum ◽  
Brain Network ◽  
Clustering Methods ◽  
Early Developmental Stages ◽  
Spatio Temporal ◽  
Early Developmental

AbstractAutism spectrum disorders (ASD) are associated with disruption of large-scale brain network. Recently, we found that directed functional connectivity alterations of social brain networks are a core component of atypical brain development at early developmental stages in ASD. Here, we investigated the spatio-temporal dynamics of whole-brain neuronal networks at a subsecond scale in 113 toddlers and preschoolers (66 with ASD) using an EEG microstate approach. We first determined the predominant microstates using established clustering methods. We identified five predominant microstate (labeled as microstate classes A–E) with significant differences in the temporal dynamics of microstate class B between the groups in terms of increased appearance and prolonged duration. Using Markov chains, we found differences in the dynamic syntax between several maps in toddlers and preschoolers with ASD compared to their TD peers. Finally, exploratory analysis of brain–behavioral relationships within the ASD group suggested that the temporal dynamics of some maps were related to conditions comorbid to ASD during early developmental stages.

scholarly journals Loss of integration of brain networks after sleep deprivation relates to the worsening of cognitive functions

2020 ◽  
Author(s):  
Pesoli Matteo ◽  
Rucco Rosaria ◽  
Liparoti Marianna ◽  
Lardone Anna ◽  
D’Aurizio Giula ◽  
...  
Keyword(s):  
Sleep Deprivation ◽  
Resting State ◽  
Large Scale ◽  
Brain Networks ◽  
Brain Network ◽  
Cognitive Tasks ◽  
Functional Networks ◽  
Long Sleep ◽  
Before And After ◽  
The Brain

AbstractThe topology of brain networks changes according to environmental demands and can be described within the framework of graph theory. We hypothesized that 24-hours long sleep deprivation (SD) causes functional rearrangements of the brain topology so as to impair optimal communication, and that such rearrangements relate to the performance in specific cognitive tasks, namely the ones specifically requiring attention. Thirty-two young men underwent resting-state MEG recording and assessments of attention and switching abilities before and after SD. We found loss of integration of brain network and a worsening of attention but not of switching abilities. These results show that brain network changes due to SD affect switching abilities, worsened attention and induce large-scale rearrangements in the functional networks.

scholarly journals Global connectivity of the frontoparietal cognitive control network is related to depression symptoms in the general population

2017 ◽  
Author(s):  
Douglas H. Schultz ◽  
Takuya Ito ◽  
Levi I. Solomyak ◽  
Richard H. Chen ◽  
Ravi D. Mill ◽  
...  
Keyword(s):  
Mental Health ◽  
Cognitive Control ◽  
Large Scale ◽  
Brain Network ◽  
Depression Symptoms ◽  
Control Network ◽  
Mental Health Symptoms ◽  
Health Symptoms ◽  
Cognitive Control Network ◽  
The Brain

ABSTRACTWe all vary in our mental health, even among people not meeting diagnostic criteria for mental illness. Understanding this individual variability may reveal factors driving the risk for mental illness, as well as factors driving sub-clinical problems that still adversely affect quality of life. To better understand the large-scale brain network mechanisms underlying this variability we examined the relationship between mental health symptoms and resting-state functional connectivity patterns in cognitive control systems. One such system is the frontoparietal cognitive control network (FPN). Changes in FPN connectivity may impact mental health by disrupting the ability to regulate symptoms in a goal-directed manner. Here we test the hypothesis that FPN dysconnectivity relates to mental health symptoms even among individuals who do not meet formal diagnostic criteria but may exhibit meaningful symptom variation. We found that depression symptoms severity negatively correlated with between-network global connectivity (BGC) of the FPN. This suggests that decreased connectivity between the FPN and the rest of the brain is related to increased depression symptoms in the general population. These findings complement previous clinical studies to support the hypothesis that global FPN connectivity contributes to the regulation of mental health symptoms across both health and disease.AUTHOR SUMMARYUnderstanding how large-scale network interactions in the brain contribute to (or serve a protective role against) mental health symptoms is an important step toward developing more effective mental health treatments. Here we test the hypothesis that cognitive control networks play an important role in mental health by being highly connected to other brain networks and able to serve as a feedback mechanism capable of regulating symptoms in a goal-directed manner. We found that the more well-connected the frontoparietal cognitive control network was to other networks in the brain the less depression symptoms were reported by participants. These results contribute to our understanding of how brain network interactions are related to mental health symptoms, even in individuals who have not been diagnosed with a disorder.

Sign in / Sign up

Export Citation Format

Share Document