scholarly journals How synchrony and metastable network dynamics are affected in fast and slow timescales with aging: Implication for Cognition

2021 ◽  
Author(s):  
Priyanka Chakraborty ◽  
Shubham Kumar ◽  
Amit Naskar ◽  
Arpan Banerjee ◽  
Dipanjan Roy
Keyword(s):  
Time Scales ◽  
Large Scale ◽  
Network Dynamics ◽  
Structural Connectivity ◽  
Brain Network ◽  
Conduction Delay ◽  
Whole Brain ◽  
Network Measures ◽  
Network Measure ◽  
Working Point

Both healthy and pathological aging exhibits gradual deterioration of structure but interestingly in healthy aging adults often maintains a high level of cognitive performance in a variety of cognitively demanding task till late age. What are the relevant network measures that could possibly track these dynamic changes which may be critically relevant for maintenance of cognitive functions through lifespan and how does these measures affected by the specific alterations in underlying anatomical connectivity till day remains an open question. In this work, we propose that whole-brain computational models are required to test the hypothesis that aging affects the brain network dynamics through two highly relevant network measures synchrony and metastability. Since aging entails complex processes involving multiple timescales we test the additional hypothesis that whether these two network measures remain invariant or exhibit different behavior in the fast and slow timescales respectively. The altered global synchrony and metastability with aging can be related to shifts in the dynamic working point of the system based on biophysical parameters e.g., time delay, and inter-areal coupling constrained by the underlying structural connectivity matrix.Using diffusion tensor imaging (DTI) data, we estimate structural connectivity (SC) of individual group of participants and obtain network level synchrony, metastability indexing network dynamics from resting state functional MRI data for both young and elderly participants in the age range of 18-89 years. Subsequently, we simulate a whole-brain Kuramoto model of coupled oscillators with appropriate conduction delay and interareal coupling strength to test the hypothesis of shifting of dynamic working point with age-associated alteration in network dynamics in both neural and ultraslow BOLD signal time scales. Specifically, we investigate the age-associated difference in metastable brain dynamics across large-scale neurocognitive brain networks e.g., salience network (SN), default mode network (DMN), and central executive network (CEN) to test spatio-temporal changes in default to executive coupling hypothesis with age. Interestingly, we find that the metastability of the SN increases substantially with age, whereas the metastability of the CEN and DMN networks do not substantially vary with age suggesting a clear role of conduction delay and global coupling in mediating altered dynamics in these networks. Moreover, our finding suggests that the metastability changes from slow to fast timescales confirming previous findings that variability of brain signals relates differently in slower and faster time scales with aging. However, synchrony remains invariant network measure across timescales and agnostic to the filtering of fast signals. Finally, we demonstrate both numerically and analytically long-range anatomical connections as oppose to shot-range or mid-range connection alterations is responsible for the overall neural difference in large-scale brain network dynamics captured by the network measure metastability. In summary, we propose a theoretical framework providing a systematic account of tracking age-associated variability and synchrony at multiple time scales across lifespan which may pave the way for developing dynamical theories of cognitive aging.

scholarly journals Network dynamics with BrainX3: a large-scale simulation of the human brain network with real-time interaction

2015 ◽  
Vol 9 ◽  
Author(s):  
Xerxes D. Arsiwalla ◽  
Riccardo Zucca ◽  
Alberto Betella ◽  
Enrique Martinez ◽  
David Dalmazzo ◽  
...  
Keyword(s):  
Human Brain ◽  
Real Time ◽  
Large Scale ◽  
Network Dynamics ◽  
Brain Network ◽  
Large Scale Simulation ◽  
Time Interaction

scholarly journals Dynamic Properties of Simulated Brain Network Models and Empirical Resting State Data

2018 ◽  
Author(s):  
Amrit Kashyap ◽  
Shella Keilholz
Keyword(s):  
Resting State ◽  
Dynamic Properties ◽  
Brain Activity ◽  
Temporal Structure ◽  
Structural Connectivity ◽  
Network Models ◽  
Resting State Fmri ◽  
Brain Network ◽  
Dynamical Analysis ◽  
Whole Brain

AbstractBrain Network Models have become a promising theoretical framework in simulating signals that are representative of whole brain activity such as resting state fMRI. However, it has been difficult to compare the complex brain activity between simulated and empirical data. Previous studies have used simple metrics that surmise coordination between regions such as functional connectivity, and we extend on this by using various different dynamical analysis tools that are currently used to understand resting state fMRI. We show that certain properties correspond to the structural connectivity input that is shared between the models, and certain dynamic properties relate more to the mathematical description of the Brain Network Model. We conclude that the dynamic properties that gauge more temporal structure rather than spatial coordination in the rs-fMRI signal seem to provide the largest contrasts between different BNMs and the unknown empirical dynamical system. Our results will be useful in constraining and developing more realistic simulations of whole brain activity.

scholarly journals A Neuroeconomic Framework for Creative Cognition

2017 ◽  
Author(s):  
Hause Lin ◽  
Oshin Vartanian
Keyword(s):  
Decision Making ◽  
Locus Coeruleus ◽  
Large Scale ◽  
Network Dynamics ◽  
Brain Network ◽  
Creative Cognition ◽  
Decision Making Processes

Neuroeconomics is the study of the neurobiological bases of subjective preferences and choices. We present a novel framework that synthesizes findings from the literatures on neuroeconomics and creativity to provide a neurobiological description of creative cognition. It proposes that value-based decision-making processes and activity in the locus coeruleus-norepinephrine (LC-NE) neuromodulatory system underlie creative cognition, as well as the large-scale brain network dynamics shown to be associated with creativity. This framework allows us to re-conceptualize creative cognition as driven by value-based decision making, in the process providing several falsifiable hypotheses that can further our understanding of creativity, decision making, and brain network dynamics.

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.

scholarly journals Microstructural deficits in the thalamus of major depressive disorder

2021 ◽  
Author(s):  
Pengfei Xu ◽  
Gangqiang Hou ◽  
Yuxuan Zhang ◽  
Yingli Zhang ◽  
Hui Ai ◽  
...  
Keyword(s):  
Major Depressive Disorder ◽  
Depressive Disorder ◽  
Large Scale ◽  
Gray Matter Volume ◽  
Structural Connectivity ◽  
Brain Network ◽  
Microstructural Properties ◽  
Major Depressive ◽  
T1 Relaxation ◽  
Structural Abnormalities

Macroscopic structural abnormalities in the thalamus and thalamic circuits have been shown to contribute to the neuropathology of major depressive disorder (MDD). However, cytoarchitectonic properties underlying these macroscopic abnormalities remain unknown. The purpose of this study was to identify systematic deficits of brain architecture in depression, from structural brain network organization to microstructural properties. A multi-modal neuroimaging approach including diffusion, anatomical and quantitative magnetic resonance imaging (MRI) was used to examine structural-related alternations in 56 MDD patients compared with 35 age- and sex-matched controls. Structural networks were constructed and analyzed using seed-based probabilistic tractography. Morphometric measurements, including cortical thickness and voxel-based morphometry (VBM), were evaluated across the whole brain. A conjunction analysis was then conducted to identify key regions showing common structural alternations across modalities. The microstructural properties, macromolecular tissue volume (MTV) and T1 relaxation times of identified key regions were then calculated. Results showed multiple alterations of structural connectivity within a set of subcortical areas and their connections to cortical regions in MDD patients. These subcortical regions included the putamen, thalamus and caudate, which are predominately involved in the limbic-cortical-striatal-pallidal-thalamic network (LCSPT). Structural connectivity was disrupted within and between large-scale networks, mainly including subcortical networks, default mode networks and salience/ventral attention networks. Consistently, these regions also exhibited widespread volume reductions in MDD patients, specifically the bilateral thalamus, left putamen and right caudate. Importantly, the microstructural properties, T1 relaxation time of left thalamus were increased and negatively correlated with its gray matter volume in MDD patients. The present work to date sheds light on the neuropathological disruptions of LCSPT circuit in MDD, providing the first multi-modal neuroimaging evidence for the macro-micro structural abnormalities of the thalamus in patients with MDD. These findings have implications in understanding the abnormal changes of brain structures across development of MDD.

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 Large-Scale Brain Network Dynamics Provide a Measure of Psychosis and Anxiety in 22q11.2 Deletion Syndrome

2019 ◽  
Vol 4 (10) ◽  
pp. 881-892 ◽  
Author(s):  
Daniela Zöller ◽  
Corrado Sandini ◽  
Fikret Işik Karahanoğlu ◽  
Maria Carmela Padula ◽  
Marie Schaer ◽  
...  
Keyword(s):  
Large Scale ◽  
Network Dynamics ◽  
22Q11.2 Deletion Syndrome ◽  
Brain Network ◽  
Deletion Syndrome ◽  
22Q11.2 Deletion

scholarly journals Stabilization of Brain Network Dynamics during Childhood and Adolescence is Associated with Gene Expressions

2021 ◽  
Author(s):  
Tianyuan Lei ◽  
Xuhong Liao ◽  
Xiaodan Chen ◽  
Tengda Zhao ◽  
Yuehua Xu ◽  
...  
Keyword(s):  
Large Scale ◽  
Expression Profiles ◽  
Brain Networks ◽  
Network Dynamics ◽  
Brain Network ◽  
Global Network ◽  
Association Cortex ◽  
Childhood And Adolescence ◽  
Gene Expressions ◽  
Age Related

AbstractFunctional brain networks require dynamic reconfiguration to support flexible cognitive function. However, the developmental principles shaping brain network dynamics remain poorly understood. Here, we report the longitudinal development of large-scale brain network dynamics during childhood and adolescence, and its connection with gene expression profiles. Using a multilayer network model, we show the temporally varying modular architecture of child brain networks, with higher network switching primarily in the association cortex and lower switching in the primary regions. This topographical profile exhibits progressive maturation, which manifests as reduced modular dynamics, particularly in the transmodal (e.g., default-mode and frontoparietal) and sensorimotor regions. These developmental refinements mediate age-related enhancements of global network segregation and are linked with the expression profiles of genes associated with the enrichment of ion transport and nucleobase-containing compound transport. These results highlight a progressive stabilization of brain dynamics, which expand our understanding of the neural mechanisms that underlie cognitive development.

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