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 Task-related effective connectivity reveals that the cortical rich club gates cortex-wide communication

2017 ◽  
Author(s):  
Mario Senden ◽  
Niels Reuter ◽  
Martijn P. van den Heuvel ◽  
Rainer Goebel ◽  
Gustavo Deco ◽  
...  
Keyword(s):  
Information Exchange ◽  
Effective Connectivity ◽  
Functional Integration ◽  
Brain Regions ◽  
Rich Club ◽  
State Dependent ◽  
Gating Mechanism ◽  
The Rich ◽  
Cortical Regions ◽  
Peripheral Regions

AbstractHigher cognition may require the globally coordinated integration of specialized brain regions into functional networks. A collection of structural cortical hubs - referred to as the rich club - has been hypothesized to support task-specific functional integration. In the present paper, we use a whole-cortex model to estimate directed interactions between 68 cortical regions from fMRI activity for four different tasks (reflecting different cognitive domains) and resting state. We analyze the state-dependent input and output effective connectivity of the structural rich club and relate these to whole-cortex dynamics and network reconfigurations. We find that the cortical rich club exhibits an increase in outgoing effective connectivity during task performance as compared to rest while incoming connectivity remains constant. Increased outgoing connectivity targets a sparse set of peripheral regions with specific regions strongly overlapping between tasks. At the same time, community detection analyses reveal massive reorganizations of interactions among peripheral regions, including those serving as target of increased rich club output. This suggests that while peripheral regions may play a role in several tasks, their concrete interplay might nonetheless be task-specific. Furthermore, we observe that whole-cortex dynamics are faster during task as compared to rest. The decoupling effects usually accompanying faster dynamics appear to be counteracted by the increased rich club outgoing effective connectivity. Together our findings speak to a gating mechanism of the rich club that supports fast-paced information exchange among relevant peripheral regions in a task-specific and goal-directed fashion, while constantly listening to the whole network.

scholarly journals Adolescent tuning of association cortex in human structural brain networks

2017 ◽  
Author(s):  
František Váša ◽  
Jakob Seidlitz ◽  
Rafael Romero-Garcia ◽  
Kirstie J. Whitaker ◽  
Gideon Rosenthal ◽  
...  
Keyword(s):  
Sharp Decrease ◽  
Brain Regions ◽  
Brain Network ◽  
Association Cortex ◽  
Structural Correlation ◽  
Age Related ◽  
Cortical Regions ◽  
Structural Networks ◽  
The Brain ◽  
Age Related Changes

AbstractMotivated by prior data on local cortical shrinkage and intracortical myelination, we predicted age-related changes in topological organisation of cortical structural networks during adolescence. We estimated structural correlation from magnetic resonance imaging measures of cortical thickness at 308 regions in a sample of N=297 healthy participants, aged 14-24 years. We used a novel sliding-window analysis to measure age-related changes in network attributes globally, locally and in the context of several community partitions of the network. We found that the strength of structural correlation generally decreased as a function of age. Association cortical regions demonstrated a sharp decrease in nodal degree (hubness) from 14 years, reaching a minimum at approximately 19 years, and then levelling off or even slightly increasing until 24 years. Greater and more prolonged age-related changes in degree of cortical regions within the brain network were associated with faster rates of adolescent cortical myelination and shrinkage. The brain regions that demonstrated the greatest age-related changes were concentrated within prefrontal modules. We conclude that human adolescence is associated with biologically plausible changes in structural imaging markers of brain network organization, consistent with the concept of tuning or consolidating anatomical connectivity between frontal cortex and the rest of the connectome.

scholarly journals Frequency-dependent organization of the brain’s functional network through delayed-interactions

2019 ◽  
Author(s):  
Abolfazl Ziaeemehr ◽  
Mina Zarei ◽  
Alireza Valizadeh ◽  
Claudio R. Mirasso
Keyword(s):  
Spatial Scales ◽  
Brain Regions ◽  
Brain Network ◽  
Modular Structure ◽  
Brain Dynamics ◽  
Theoretical Argument ◽  
Frequency Bands ◽  
Global Synchrony ◽  
Asynchronous State ◽  
The Brain

AbstractThe structure of the brain network shows modularity at multiple spatial scales. The effect of the modular structure on the brain dynamics has been the focus of several studies in recent years but many aspects remain to be explored. For example, it is not well-known how the delays in the transmission of signals between the neurons and the brain regions, interact with the modular structure to determine the brain dynamics. In this paper, we show an important impact of the delays on the collective dynamics of the brain network with modular structure; that is, the degree of the synchrony between different brain regions is dependent on the frequency. In particular, we show that increasing the frequency the network transits from a global synchrony state to an asynchronous state, through a transition region over which the local synchrony inside the modules is stronger than the global synchrony. When the delays are dependent on the distance between the nodes, the modular structure of different spatial scales appears in the correlation matrix over different specific frequency bands, so that, finer spatial modular structure reveal in higher frequency bands. The results are justified by a simple theoretical argument and elaborated by simulations on several simplified modular networks and the connectome with different spatial resolutions.

scholarly journals Physical positioning markedly enhances brain transduction after intrathecal AAV9 infusion

2018 ◽  
Vol 4 (11) ◽  
pp. eaau9859 ◽  
Author(s):  
Michael J. Castle ◽  
Yuhsiang Cheng ◽  
Aravind Asokan ◽  
Mark H. Tuszynski
Keyword(s):  
Gene Expression ◽  
Gene Delivery ◽  
Neurological Disorders ◽  
Fold Increase ◽  
Brain Regions ◽  
Intrathecal Administration ◽  
Simple Method ◽  
Virus Serotype ◽  
Cortical Regions ◽  
The Brain

Several neurological disorders may benefit from gene therapy. However, even when using the lead vector candidate for intrathecal administration, adeno-associated virus serotype 9 (AAV9), the strength and distribution of gene transfer to the brain are inconsistent. On the basis of preliminary observations that standard intrathecal AAV9 infusions predominantly drive reporter gene expression in brain regions where gravity might cause cerebrospinal fluid to settle, we tested the hypothesis that counteracting vector “settling” through animal positioning would enhance vector delivery to the brain. When rats are either inverted in the Trendelenburg position or continuously rotated after intrathecal AAV9 infusion, we find (i) a significant 15-fold increase in the number of transduced neurons, (ii) a marked increase in gene delivery to cortical regions, and (iii) superior animal-to-animal consistency of gene expression. Entorhinal, prefrontal, frontal, parietal, hippocampal, limbic, and basal forebrain neurons are extensively transduced: 95% of transduced cells are neurons, and greater than 70% are excitatory. These findings provide a novel and simple method for broad gene delivery to the cortex and are of substantial relevance to translational programs for neurological disorders, including Alzheimer’s disease and related dementias, stroke, and traumatic brain injury.

scholarly journals A Computational Network Control Theory Analysis of Depression Symptoms

2018 ◽  
Vol 1 ◽  
Author(s):  
Yoed N. Kenett ◽  
Roger E. Beaty ◽  
John D. Medaglia
Keyword(s):  
Control Theory ◽  
Diffusion Tensor ◽  
Brain Regions ◽  
Brain Network ◽  
Network Control ◽  
Depression Symptoms ◽  
Theory Approach ◽  
Imaging Data ◽  
The Brain ◽  
Subclinical Depression

AbstractRumination and impaired inhibition are considered core characteristics of depression. However, the neurocognitive mechanisms that contribute to these atypical cognitive processes remain unclear. To address this question, we apply a computational network control theory approach to structural brain imaging data acquired via diffusion tensor imaging in a large sample of participants, to examine how network control theory relates to individual differences in subclinical depression. Recent application of this theory at the neural level is built on a model of brain dynamics, which mathematically models patterns of inter-region activity propagated along the structure of an underlying network. The strength of this approach is its ability to characterize the potential role of each brain region in regulating whole-brain network function based on its anatomical fingerprint and a simplified model of node dynamics. We find that subclinical depression is negatively related to higher integration abilities in the right anterior insula, replicating and extending previous studies implicating atypical switching between the default mode and Executive Control Networks in depression. We also find that subclinical depression is related to the ability to “drive” the brain system into easy to reach neural states in several brain regions, including the bilateral lingual gyrus and lateral occipital gyrus. These findings highlight brain regions less known in their role in depression, and clarify their roles in driving the brain into different neural states related to depression symptoms.

scholarly journals A Graphlet-Based Topological Characterization of the Resting-State Network in Healthy People

2021 ◽  
Vol 15 ◽  
Author(s):  
Paolo Finotelli ◽  
Carlo Piccardi ◽  
Edie Miglio ◽  
Paolo Dulio
Keyword(s):  
Resting State ◽  
Brain Regions ◽  
Brain Network ◽  
Induced Subgraphs ◽  
Topological Characterization ◽  
Network Information ◽  
The Subject ◽  
Euclidean Distances ◽  
The Brain

In this paper, we propose a graphlet-based topological algorithm for the investigation of the brain network at resting state (RS). To this aim, we model the brain as a graph, where (labeled) nodes correspond to specific cerebral areas and links are weighted connections determined by the intensity of the functional magnetic resonance imaging (fMRI). Then, we select a number of working graphlets, namely, connected and non-isomorphic induced subgraphs. We compute, for each labeled node, its Graphlet Degree Vector (GDV), which allows us to associate a GDV matrix to each one of the 133 subjects of the considered sample, reporting how many times each node of the atlas “touches” the independent orbits defined by the graphlet set. We focus on the 56 independent columns (i.e., non-redundant orbits) of the GDV matrices. By aggregating their count all over the 133 subjects and then by sorting each column independently, we obtain a sorted node table, whose top-level entries highlight the nodes (i.e., brain regions) most frequently touching each of the 56 independent graphlet orbits. Then, by pairwise comparing the columns of the sorted node table in the top-k entries for various values of k, we identify sets of nodes that are consistently involved with high frequency in the 56 independent graphlet orbits all over the 133 subjects. It turns out that these sets consist of labeled nodes directly belonging to the default mode network (DMN) or strongly interacting with it at the RS, indicating that graphlet analysis provides a viable tool for the topological characterization of such brain regions. We finally provide a validation of the graphlet approach by testing its power in catching network differences. To this aim, we encode in a Graphlet Correlation Matrix (GCM) the network information associated with each subject then construct a subject-to-subject Graphlet Correlation Distance (GCD) matrix based on the Euclidean distances between all possible pairs of GCM. The analysis of the clusters induced by the GCD matrix shows a clear separation of the subjects in two groups, whose relationship with the subject characteristics is investigated.

Graph Theory-Based Brain Network Connectivity Analysis and Classification of Alzheimer’s Disease

2021 ◽  
Author(s):  
A. Thushara ◽  
C. Ushadevi Amma ◽  
Ansamma John
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Graph Theory ◽  
Neurodegenerative Disorder ◽  
Brain Networks ◽  
Network Connectivity ◽  
Brain Regions ◽  
Brain Network ◽  
Fiber Tracts ◽  
The Brain

Alzheimer’s Disease (AD) is basically a progressive neurodegenerative disorder associated with abnormal brain networks that affect millions of elderly people and degrades their quality of life. The abnormalities in brain networks are due to the disruption of White Matter (WM) fiber tracts that connect the brain regions. Diffusion-Weighted Imaging (DWI) captures the brain’s WM integrity. Here, the correlation betwixt the WM degeneration and also AD is investigated by utilizing graph theory as well as Machine Learning (ML) algorithms. By using the DW image obtained from Alzheimer’s Disease Neuroimaging Initiative (ADNI) database, the brain graph of each subject is constructed. The features extracted from the brain graph form the basis to differentiate between Mild Cognitive Impairment (MCI), Control Normal (CN) and AD subjects. Performance evaluation is done using binary and multiclass classification algorithms and obtained an accuracy that outperforms the current top-notch DWI-based studies.

Songs to Syntax

2011 ◽  
Vol 5 (4) ◽  
pp. 22-32 ◽  
Author(s):  
Robert C. Berwick
Keyword(s):  
Auditory Perception ◽  
Convergent Evolution ◽  
Vocal Learning ◽  
Brain Regions ◽  
Auditory Memory ◽  
Central Component ◽  
Common Descent ◽  
Sensory Modalities ◽  
The Rich ◽  
The Brain

Language comprises a central component of a complex that is sometimes called “the human capacity.” This complex seems to have crystallized fairly recently among a small group in East Africa of whom people are all descendants. Common descent has been important in the evolution of the brain, such that avian and mammalian brains may be largely homologous, particularly in the case of brain regions involved in auditory perception, vocalization and auditory memory. There has been convergent evolution of the capacity for auditory-vocal learning, and possibly for structuring of external vocalizations, such that apes lack the abilities that are shared between songbirds and humans. Language’s recent evolutionary origin suggests that the computational machinery underlying syntax arose via the introduction of a single, simple, combinatorial operation. Further, the relation of a simple combinatorial syntax to the sensory-motor and thought systems reveals language to be asymmetric in design: while it precisely matches the representations required for inner mental thought, acting as the “glue” that binds together other internal cognitive and sensory modalities, at the same time it poses computational difficulties for externalization, that is, parsing and speech or signed production. Despite this mismatch, language syntax leads directly to the rich cognitive array that marks us as a symbolic species.

scholarly journals The Cross-Modal Effects of Sensory Deprivation on Spatial and Temporal Processes in Vision and Audition: A Systematic Review on Behavioral and Neuroimaging Research since 2000

2019 ◽  
Vol 2019 ◽  
pp. 1-21 ◽  
Author(s):  
Laura Bell ◽  
Lisa Wagels ◽  
Christiane Neuschaefer-Rube ◽  
Janina Fels ◽  
Raquel E. Gur ◽  
...  
Keyword(s):  
Systematic Review ◽  
Temporal Processing ◽  
Neural Plasticity ◽  
Sensory Deprivation ◽  
Brain Regions ◽  
Behavioral Performance ◽  
Brain Organization ◽  
Processing Resources ◽  
The Cross ◽  
The Brain

One of the most significant effects of neural plasticity manifests in the case of sensory deprivation when cortical areas that were originally specialized for the functions of the deprived sense take over the processing of another modality. Vision and audition represent two important senses needed to navigate through space and time. Therefore, the current systematic review discusses the cross-modal behavioral and neural consequences of deafness and blindness by focusing on spatial and temporal processing abilities, respectively. In addition, movement processing is evaluated as compiling both spatial and temporal information. We examine whether the sense that is not primarily affected changes in its own properties or in the properties of the deprived modality (i.e., temporal processing as the main specialization of audition and spatial processing as the main specialization of vision). References to the metamodal organization, supramodal functioning, and the revised neural recycling theory are made to address global brain organization and plasticity principles. Generally, according to the reviewed studies, behavioral performance is enhanced in those aspects for which both the deprived and the overtaking senses provide adequate processing resources. Furthermore, the behavioral enhancements observed in the overtaking sense (i.e., vision in the case of deafness and audition in the case of blindness) are clearly limited by the processing resources of the overtaking modality. Thus, the brain regions that were previously recruited during the behavioral performance of the deprived sense now support a similar behavioral performance for the overtaking sense. This finding suggests a more input-unspecific and processing principle-based organization of the brain. Finally, we highlight the importance of controlling for and stating factors that might impact neural plasticity and the need for further research into visual temporal processing in deaf subjects.

scholarly journals Altered neural activity in brain cough suppression networks in cigarette smokers

2019 ◽  
Vol 54 (3) ◽  
pp. 1900362 ◽  
Author(s):  
Ayaka Ando ◽  
Stuart B. Mazzone ◽  
Michael J. Farrell
Keyword(s):  
Prefrontal Cortex ◽  
Neural Circuits ◽  
Brain Regions ◽  
Smoking Behaviour ◽  
Brain Network ◽  
Functional Brain Imaging ◽  
Cigarette Smokers ◽  
Dorsolateral Prefrontal ◽  
Cortical Regions ◽  
Airway Irritation

Cough is important for airway defence, and studies in healthy animals and humans have revealed multiple brain networks intimately involved in the perception of airway irritation, cough induction and cough suppression. Changes in cough sensitivity and/or the ability to suppress cough accompany pulmonary pathologies, suggesting a level of plasticity is possible in these central neural circuits. However, little is known about how persistent inputs from the lung might modify the brain processes regulating cough.In the present study, we used human functional brain imaging to investigate the central neural responses that accompany an altered cough sensitivity in cigarette smokers.In nonsmokers, inhalation of the airway irritant capsaicin induced a transient urge-to-cough associated with the activation of a distributed brain network that included sensory, prefrontal and motor cortical regions. Cigarette smokers demonstrated significantly higher thresholds for capsaicin-induced urge-to-cough, consistent with a reduced sensitivity to airway irritation. Intriguingly, this was accompanied by increased activation in brain regions known to be involved in both cough sensory processing (primary sensorimotor cortex) and cough suppression (dorsolateral prefrontal cortex and the midbrain nucleus cuneiformis). Activations in the prefrontal cortex were highest among participants with the least severe smoking behaviour, whereas those in the midbrain correlated with more severe smoking behaviour.These outcomes suggest that smoking-induced sensitisation of central cough neural circuits is offset by concurrently enhanced central suppression. Furthermore, central suppression mechanisms may evolve with the severity of smoke exposure, changing from initial prefrontal inhibition to more primitive midbrain processes as exposure increases.

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