Your browser doesn't support javascript.
loading
Mostrar: 20 | 50 | 100
Resultados 1 - 20 de 20.342
Filtrar
1.
Chaos ; 32(6): 063131, 2022 Jun.
Artigo em Inglês | MEDLINE | ID: mdl-35778158

RESUMO

Despite the incredible complexity of our brains' neural networks, theoretical descriptions of neural dynamics have led to profound insights into possible network states and dynamics. It remains challenging to develop theories that apply to spiking networks and thus allow one to characterize the dynamic properties of biologically more realistic networks. Here, we build on recent work by van Meegen and Lindner who have shown that "rotator networks," while considerably simpler than real spiking networks and, therefore, more amenable to mathematical analysis, still allow one to capture dynamical properties of networks of spiking neurons. This framework can be easily extended to the case where individual units receive uncorrelated stochastic input, which can be interpreted as intrinsic noise. However, the assumptions of the theory do not apply anymore when the input received by the single rotators is strongly correlated among units. As we show, in this case, the network fluctuations become significantly non-Gaussian, which calls for reworking of the theory. Using a cumulant expansion, we develop a self-consistent analytical theory that accounts for the observed non-Gaussian statistics. Our theory provides a starting point for further studies of more general network setups and information transmission properties of these networks.


Assuntos
Modelos Neurológicos , Rede Nervosa , Potenciais de Ação/fisiologia , Rede Nervosa/fisiologia , Redes Neurais de Computação , Neurônios/fisiologia
2.
Annu Rev Neurosci ; 45: 533-560, 2022 07 08.
Artigo em Inglês | MEDLINE | ID: mdl-35803587

RESUMO

The neocortex is a complex neurobiological system with many interacting regions. How these regions work together to subserve flexible behavior and cognition has become increasingly amenable to rigorous research. Here, I review recent experimental and theoretical work on the modus operandi of a multiregional cortex. These studies revealed several general principles for the neocortical interareal connectivity, low-dimensional macroscopic gradients of biological properties across cortical areas, and a hierarchy of timescales for information processing. Theoretical work suggests testable predictions regarding differential excitation and inhibition along feedforward and feedback pathways in the cortical hierarchy. Furthermore, modeling of distributed working memory and simple decision-making has given rise to a novel mathematical concept, dubbed bifurcation in space, that potentially explains how different cortical areas, with a canonical circuit organization but gradients of biological heterogeneities, are able to subserve their respective (e.g., sensory coding versus executive control) functions in a modularly organized brain.


Assuntos
Neocórtex , Cognição/fisiologia , Função Executiva , Memória de Curto Prazo/fisiologia , Neocórtex/fisiologia , Rede Nervosa/fisiologia
3.
Artigo em Inglês | MEDLINE | ID: mdl-35849676

RESUMO

Rhythmic oscillation is crucial for information transmission and neural communication among different brain areas. Stochastic resonance (SR) can evoke different patterns of neural oscillation. However, the characteristics of network resonance and underlying dynamical mechanisms are still unclear. In this paper, a biological model of cortical network is established and its dynamical response to external periodic stimulation is investigated. We explore the oscillatory resonance of excitatory and inhibitory populations in cortical network. It is found that the intrinsic parameters of neural populations determine the extent of resonant activity, indicating that the firing rate exhibits coherent oscillation when the frequency of external stimulation is close to intrinsic frequency of neural population. In addition, the nonlinear dynamics of cortical network in oscillatory resonance can be represented by helical manifolds in low-dimensional state space. The geometry of neural manifolds reveals the periodic dynamics and state transition in oscillatory resonance. Moreover, time delay in chemical synapses can induce multiple resonances, which appear intermittently at integer multiples of the period of input signal. The dynamical response of neural population achieves maximal periodically, due to the transition of network states induced by time delay. Furthermore, mean-field theory is applied to analyze theoretical dynamic of cortical networks with time delay and demonstrate the effective transmission of stimulation information via oscillatory resonance in the brain. Consequently, the obtained results contribute to the improvement of neuromodulation for neurological disease from the viewpoint of the neural basis.


Assuntos
Modelos Neurológicos , Neurônios , Potenciais de Ação/fisiologia , Humanos , Rede Nervosa/fisiologia , Neurônios/fisiologia , Dinâmica não Linear , Sinapses/fisiologia
4.
Proc Natl Acad Sci U S A ; 119(27): e2116673119, 2022 07 05.
Artigo em Inglês | MEDLINE | ID: mdl-35776541

RESUMO

Adolescence is a time of profound changes in the physical wiring and function of the brain. Here, we analyzed structural and functional brain network development in an accelerated longitudinal cohort spanning 14 to 25 y (n = 199). Core to our work was an advanced in vivo model of cortical wiring incorporating MRI features of corticocortical proximity, microstructural similarity, and white matter tractography. Longitudinal analyses assessing age-related changes in cortical wiring identified a continued differentiation of multiple corticocortical structural networks in youth. We then assessed structure-function coupling using resting-state functional MRI measures in the same participants both via cross-sectional analysis at baseline and by studying longitudinal change between baseline and follow-up scans. At baseline, regions with more similar structural wiring were more likely to be functionally coupled. Moreover, correlating longitudinal structural wiring changes with longitudinal functional connectivity reconfigurations, we found that increased structural differentiation, particularly between sensory/unimodal and default mode networks, was reflected by reduced functional interactions. These findings provide insights into adolescent development of human brain structure and function, illustrating how structural wiring interacts with the maturation of macroscale functional hierarchies.


Assuntos
Desenvolvimento do Adolescente , Encéfalo , Conectoma , Adolescente , Encéfalo/fisiologia , Encéfalo/ultraestrutura , Estudos Transversais , Humanos , Imageamento por Ressonância Magnética , Rede Nervosa/fisiologia , Rede Nervosa/ultraestrutura
5.
Elife ; 112022 06 16.
Artigo em Inglês | MEDLINE | ID: mdl-35708741

RESUMO

Activity-dependent self-organization plays an important role in the formation of specific and stereotyped connectivity patterns in neural circuits. By combining neuronal cultures, and tools with approaches from network neuroscience and information theory, we can study how complex network topology emerges from local neuronal interactions. We constructed effective connectivity networks using a transfer entropy analysis of spike trains recorded from rat embryo dissociated hippocampal neuron cultures between 6 and 35 days in vitro to investigate how the topology evolves during maturation. The methodology for constructing the networks considered the synapse delay and addressed the influence of firing rate and population bursts as well as spurious effects on the inference of connections. We found that the number of links in the networks grew over the course of development, shifting from a segregated to a more integrated architecture. As part of this progression, three significant aspects of complex network topology emerged. In agreement with previous in silico and in vitro studies, a small-world architecture was detected, largely due to strong clustering among neurons. Additionally, the networks developed in a modular topology, with most modules comprising nearby neurons. Finally, highly active neurons acquired topological characteristics that made them important nodes to the network and integrators of modules. These findings leverage new insights into how neuronal effective network topology relates to neuronal assembly self-organization mechanisms.


Assuntos
Rede Nervosa , Neurônios , Animais , Entropia , Hipocampo , Rede Nervosa/fisiologia , Neurônios/fisiologia , Ratos , Sinapses/fisiologia
6.
PLoS Comput Biol ; 18(6): e1010215, 2022 06.
Artigo em Inglês | MEDLINE | ID: mdl-35714155

RESUMO

Characterizing metastable neural dynamics in finite-size spiking networks remains a daunting challenge. We propose to address this challenge in the recently introduced replica-mean-field (RMF) limit. In this limit, networks are made of infinitely many replicas of the finite network of interest, but with randomized interactions across replicas. Such randomization renders certain excitatory networks fully tractable at the cost of neglecting activity correlations, but with explicit dependence on the finite size of the neural constituents. However, metastable dynamics typically unfold in networks with mixed inhibition and excitation. Here, we extend the RMF computational framework to point-process-based neural network models with exponential stochastic intensities, allowing for mixed excitation and inhibition. Within this setting, we show that metastable finite-size networks admit multistable RMF limits, which are fully characterized by stationary firing rates. Technically, these stationary rates are determined as the solutions of a set of delayed differential equations under certain regularity conditions that any physical solutions shall satisfy. We solve this original problem by combining the resolvent formalism and singular-perturbation theory. Importantly, we find that these rates specify probabilistic pseudo-equilibria which accurately capture the neural variability observed in the original finite-size network. We also discuss the emergence of metastability as a stochastic bifurcation, which can be interpreted as a static phase transition in the RMF limits. In turn, we expect to leverage the static picture of RMF limits to infer purely dynamical features of metastable finite-size networks, such as the transition rates between pseudo-equilibria.


Assuntos
Modelos Neurológicos , Rede Nervosa , Potenciais de Ação/fisiologia , Rede Nervosa/fisiologia , Redes Neurais de Computação , Neurônios/fisiologia
7.
Neuroimage ; 258: 119364, 2022 09.
Artigo em Inglês | MEDLINE | ID: mdl-35690257

RESUMO

Even when subjects are at rest, it is thought that brain activity is organized into distinct brain states during which reproducible patterns are observable. Yet, it is unclear how to define or distinguish different brain states. A potential source of brain state variation is arousal, which may play a role in modulating functional interactions between brain regions. Here, we use simultaneous resting state functional magnetic resonance imaging (fMRI) and pupillometry to study the impact of arousal levels indexed by pupil area on the integration of large-scale brain networks. We employ a novel sparse dictionary learning-based method to identify hub regions participating in between-network integration stratified by arousal, by measuring k-hubness, the number (k) of functionally overlapping networks in each brain region. We show evidence of a brain-wide decrease in between-network integration and inter-subject variability at low relative to high arousal, with differences emerging across regions of the frontoparietal, default mode, motor, limbic, and cerebellum networks. State-dependent changes in k-hubness relate to the actual patterns of network integration within these hubs, suggesting a brain state transition from high to low arousal characterized by global synchronization and reduced network overlaps. We demonstrate that arousal is not limited to specific brain areas known to be directly associated with arousal regulation, but instead has a brain-wide impact that involves high-level between-network communications. Lastly, we show a systematic change in pairwise fMRI signal correlation structures in the arousal state-stratified data, and demonstrate that the choice of global signal regression could result in different conclusions in conventional graph theoretical analysis and in the analysis of k-hubness when studying arousal modulations. Together, our results suggest the presence of global and local effects of pupil-linked arousal modulations on resting state brain functional connectivity.


Assuntos
Encéfalo , Imageamento por Ressonância Magnética , Nível de Alerta/fisiologia , Encéfalo/diagnóstico por imagem , Encéfalo/fisiologia , Mapeamento Encefálico/métodos , Humanos , Imageamento por Ressonância Magnética/métodos , Rede Nervosa/diagnóstico por imagem , Rede Nervosa/fisiologia , Pupila/fisiologia
8.
Neuroimage ; 258: 119398, 2022 09.
Artigo em Inglês | MEDLINE | ID: mdl-35724856

RESUMO

The default mode network (DMN) has been theorized to participate in a range of social, cognitive, and affective functions. Yet, previous accounts do not consider how the DMN contributes to other brain regions depending on psychological context, thus rendering our understanding of DMN function incomplete. We addressed this gap by applying a novel network-based psychophysiological interaction (nPPI) analysis to the reward task within the Human Connectome Project. We first focused on the task-evoked responses of the DMN and other networks involving the prefrontal cortex, including the executive control network (salience network) and the left and right frontoparietal networks. Consistent with a host of prior studies, the DMN exhibited a relative decrease in activation during the task, while the other networks exhibited a relative increase during the task. Next, we used nPPI analyses to assess whether these networks exhibit task-dependent changes in connectivity with other brain regions. Strikingly, we found that the experience of reward enhances task-dependent connectivity between the DMN and the ventral striatum, an effect that was specific to the DMN. Surprisingly, the strength of DMN-VS connectivity was correlated with personality characteristics relating to openness. Taken together, these results advance models of DMN by demonstrating how it contributes to other brain systems during task performance and how those contributions relate to individual differences.


Assuntos
Conectoma , Estriado Ventral , Encéfalo/fisiologia , Mapeamento Encefálico , Conectoma/métodos , Rede de Modo Padrão , Humanos , Imageamento por Ressonância Magnética/métodos , Rede Nervosa/fisiologia , Recompensa , Estriado Ventral/diagnóstico por imagem
9.
Proc Natl Acad Sci U S A ; 119(24): e2204144119, 2022 06 14.
Artigo em Inglês | MEDLINE | ID: mdl-35666866

RESUMO

Despite the prevalence of stress, how brains reconfigure their multilevel, hierarchical functional organization in response to acute stress remains unclear. We examined changes in brain networks after social stress using whole-brain resting-state functional MRI (fMRI) by extending our recently published nested-spectral partition method, which quantified the functional balance between network segregation and integration. Acute stress was found to shift the brain into a more integrated and less segregated state, especially in frontal-temporal regions. Stress also stabilized brain states by reducing the variability of dynamic transition between segregated and integrated states. Transition frequency was associated with the change of cortisol, and transition variability was correlated with cognitive control. Our results show that brain networks tend to be more integrated and less variable after acute stress, possibly to enable efficient coping.


Assuntos
Mapeamento Encefálico , Rede Nervosa , Estresse Psicológico , Encéfalo/fisiologia , Mapeamento Encefálico/métodos , Imageamento por Ressonância Magnética/métodos , Rede Nervosa/fisiologia
10.
Proc Natl Acad Sci U S A ; 119(24): e2117234119, 2022 06 14.
Artigo em Inglês | MEDLINE | ID: mdl-35679342

RESUMO

Investigating neural interactions is essential to understanding the neural basis of behavior. Many statistical methods have been used for analyzing neural activity, but estimating the direction of network interactions correctly and efficiently remains a difficult problem. Here, we derive dynamical differential covariance (DDC), a method based on dynamical network models that detects directional interactions with low bias and high noise tolerance under nonstationarity conditions. Moreover, DDC scales well with the number of recording sites and the computation required is comparable to that needed for covariance. DDC was validated and compared favorably with other methods on networks with false positive motifs and multiscale neural simulations where the ground-truth connectivity was known. When applied to recordings of resting-state functional magnetic resonance imaging (rs-fMRI), DDC consistently detected regional interactions with strong structural connectivity in over 1,000 individual subjects obtained by diffusion MRI (dMRI). DDC is a promising family of methods for estimating connectivity that can be generalized to a wide range of dynamical models and recording techniques and to other applications where system identification is needed.


Assuntos
Encéfalo , Conectoma , Rede Nervosa , Encéfalo/fisiologia , Conectoma/métodos , Imagem de Difusão por Ressonância Magnética/métodos , Humanos , Rede Nervosa/fisiologia , Vias Neurais
11.
Neuroimage ; 258: 119359, 2022 09.
Artigo em Inglês | MEDLINE | ID: mdl-35680054

RESUMO

The search for an 'ideal' approach to investigate the functional connections in the human brain is an ongoing challenge for the neuroscience community. While resting-state functional magnetic resonance imaging (fMRI) has been widely used to study individual functional connectivity patterns, recent work has highlighted the benefits of collecting functional connectivity data while participants are exposed to naturalistic stimuli, such as watching a movie or listening to a story. For example, functional connectivity data collected during movie-watching were shown to predict cognitive and emotional scores more accurately than resting-state-derived functional connectivity. We have previously reported a tight link between resting-state functional connectivity and task-derived neural activity, such that the former successfully predicts the latter. In the current work we use data from the Human Connectome Project to demonstrate that naturalistic-stimulus-derived functional connectivity predicts task-induced brain activation maps more accurately than resting-state-derived functional connectivity. We then show that activation maps predicted using naturalistic stimuli are better predictors of individual intelligence scores than activation maps predicted using resting-state. We additionally examine the influence of naturalistic-stimulus type on prediction accuracy. Our findings emphasize the potential of naturalistic stimuli as a promising alternative to resting-state fMRI for connectome-based predictive modelling of individual brain activity and cognitive traits.


Assuntos
Conectoma , Imageamento por Ressonância Magnética , Encéfalo/fisiologia , Conectoma/métodos , Humanos , Inteligência , Estudos Longitudinais , Imageamento por Ressonância Magnética/métodos , Rede Nervosa/diagnóstico por imagem , Rede Nervosa/fisiologia
12.
Neuroscience ; 496: 38-51, 2022 08 01.
Artigo em Inglês | MEDLINE | ID: mdl-35643247

RESUMO

Neurons cultured on a multi-electrode array show not only spontaneous firing, but also network-specific burst firing, the latter of which develops into synchronous bursting. Such synchronous bursting can be suppressed by exposure to xenon (Xe) gas. To better understand such suppression of bursting by Xe, we investigate here whether signal transmission between neurons is also suppressed under these conditions. In these experiments, we apply a pulse electrical-stimulus to one electrode and observe the response signals within 10 ms at other active electrodes. When put under a sufficient Xe pressure, some response signals become delayed or vanish after disappearance of synchronous-bursts, particularly signals passing through multiple synaptic bonds. Such bonds have a high probability of having delayed or vanishing signals when the Xe pressure is above 0.3 MPa. The pressure dependence of the response ratio to the stimulus suggests that Xe suppresses multiple points of action simultaneously when suppressing synaptic signal transduction, as observed in the suppression of the synchronized bursting. In addition, we find that the signal that transmits not via synaptic bonding (axon conduction) is also suppressed under Xe gas pressures over 0.3 MPa. Therefore, we conclude that Xe-induced suppression of synchronized bursting is caused mainly by a decrease in the apparent number of active neurons that contribute to the neuronal network, a decrease due to inhibition of signal transmission via synaptic connections.


Assuntos
Rede Nervosa , Xenônio , Potenciais de Ação/fisiologia , Animais , Rede Nervosa/fisiologia , Neurônios , Ratos , Xenônio/farmacologia
13.
Sci Rep ; 12(1): 9656, 2022 06 10.
Artigo em Inglês | MEDLINE | ID: mdl-35688933

RESUMO

Tools to estimate brain connectivity offer the potential to enhance our understanding of brain functioning. The behavior of neuronal networks, including functional connectivity and induced connectivity changes by external stimuli, can be studied using models of cultured neurons. Cultured neurons tend to be active in groups, and pairs of neurons are said to be functionally connected when their firing patterns show significant synchronicity. Methods to infer functional connections are often based on pair-wise cross-correlation between activity patterns of (small groups of) neurons. However, these methods are not very sensitive to detect inhibitory connections, and they were not designed for use during stimulation. Maximum Entropy (MaxEnt) models may provide a conceptually different method to infer functional connectivity. They have the potential benefit to estimate functional connectivity during stimulation, and to infer excitatory as well as inhibitory connections. MaxEnt models do not involve pairwise comparison, but aim to capture probability distributions of sets of neurons that are synchronously active in discrete time bins. We used electrophysiological recordings from in vitro neuronal cultures on micro electrode arrays to investigate the ability of MaxEnt models to infer functional connectivity. Connectivity estimates provided by MaxEnt models correlated well with those obtained by conditional firing probabilities (CFP), an established cross-correlation based method. In addition, stimulus-induced connectivity changes were detected by MaxEnt models, and were of the same magnitude as those detected by CFP. Thus, MaxEnt models provide a potentially powerful new tool to study functional connectivity in neuronal networks.


Assuntos
Redes Neurais de Computação , Neurônios , Encéfalo/fisiologia , Entropia , Modelos Neurológicos , Rede Nervosa/fisiologia , Neurônios/fisiologia , Probabilidade
14.
Prog Neurobiol ; 215: 102287, 2022 08.
Artigo em Inglês | MEDLINE | ID: mdl-35533813

RESUMO

Persistent activity, the maintenance of neural activation over short periods of time in cortical networks, is widely thought to underlie the cognitive function of working memory. A large body of modeling studies has reproduced this kind of activity using cell assemblies with strengthened synaptic connections. However, almost all of these studies have considered persistent activity within networks with homogeneous neurons and synapses, making it difficult to judge the validity of such model results for cortical dynamics, which is based on highly heterogeneous neurons. Here, we consider persistent activity in a detailed, strongly data-driven network model of the prefrontal cortex with heterogeneous neuron and synapse parameters. Surprisingly, persistent activity could not be reproduced in this model without incorporating further constraints. We identified three factors that prevent successful persistent activity: heterogeneity in the cell parameters of interneurons, heterogeneity in the parameters of short-term synaptic plasticity and heterogeneity in the synaptic weights. We also discovered a general dynamic mechanism that prevents persistent activity in the presence of heterogeneities, namely a gradual drop-out of cell assembly neurons out of a bistable regime as input variability increases. Based on this mechanism, we found that persistent activity is recovered if heterogeneity is compensated, e.g., by a homeostatic plasticity mechanism. Cell assemblies shaped in this way may be potentially targeted by distinct inputs or become more responsive to specific tuning or spectral properties. Finally, we show that persistent activity in the model is robust against external noise, but the compensation of heterogeneities may prevent the dynamic generation of intrinsic in vivo-like irregular activity. These results may help informing the ongoing debate about the neural basis of working memory.


Assuntos
Modelos Neurológicos , Rede Nervosa , Potenciais de Ação/fisiologia , Humanos , Rede Nervosa/fisiologia , Neurônios/fisiologia , Córtex Pré-Frontal/fisiologia , Sinapses/fisiologia
15.
Brain Res Bull ; 185: 129-139, 2022 07.
Artigo em Inglês | MEDLINE | ID: mdl-35562013

RESUMO

The precise interplay between large-scale functional neural systems throughout the brain is essential for performance of cognitive processes. In this review we focus on the default mode network (DMN), one such functional network that is active during periods of quiet wakefulness and believed to be involved in introspection and planning. Abnormalities in DMN functional connectivity and activation appear across many neuropsychiatric disorders, including schizophrenia. Recent evidence suggests subcortical regions including the basal forebrain are functionally and structurally important for regulation of DMN activity. Within the basal forebrain, subregions like the ventral pallidum may influence DMN activity and the nucleus basalis of Meynert can inhibit switching between brain networks. Interactions between DMN and other functional networks including the medial frontoparietal network (default), lateral frontoparietal network (control), midcingulo-insular network (salience), and dorsal frontoparietal network (attention) are also discussed in the context of neuropsychiatric disorders. Several subtypes of basal forebrain neurons have been identified including basal forebrain parvalbumin-containing or somatostatin-containing neurons which can regulate cortical gamma band oscillations and DMN-like behaviors, and basal forebrain cholinergic neurons which might gate access to sensory information during reinforcement learning. In this review, we explore this evidence, discuss the clinical implications on neuropsychiatric disorders, and compare neuroanatomy in the human vs rodent DMN. Finally, we address technological advancements which could help provide a more complete understanding of modulation of DMN function and describe newly identified BF therapeutic targets that could potentially help restore DMN-associated functional deficits in patients with a variety of neuropsychiatric disorders.


Assuntos
Prosencéfalo Basal , Esquizofrenia , Encéfalo , Mapeamento Encefálico , Rede de Modo Padrão , Humanos , Imageamento por Ressonância Magnética , Rede Nervosa/diagnóstico por imagem , Rede Nervosa/fisiologia , Parvalbuminas , Vigília
16.
Neuroimage ; 257: 119279, 2022 08 15.
Artigo em Inglês | MEDLINE | ID: mdl-35577026

RESUMO

The human brain flexibly controls different cognitive behaviors, such as memory and attention, to satisfy contextual demands. Much progress has been made to reveal task-induced modulations in the whole-brain functional connectome, but we still lack a way to model context-dependent changes. Here, we present a novel connectome-to-connectome (C2C) transformation framework that enables us to model the brain's functional reorganization from one connectome state to another in response to specific task goals. Using functional magnetic resonance imaging data from the Human Connectome Project, we demonstrate that the C2C model accurately generates an individual's task-related connectomes from their task-free (resting-state) connectome with a high degree of specificity across seven different cognitive states. Moreover, the C2C model amplifies behaviorally relevant individual differences in the task-free connectome, thereby improving behavioral predictions with increased power, achieving similar performance with just a third of the subjects needed when relying on resting-state data alone. Finally, the C2C model reveals how the brain reorganizes between cognitive states. Our observations support the existence of reliable state-specific subsystems in the brain and demonstrate that we can quantitatively model how the connectome reconfigures to different cognitive states, enabling more accurate predictions of behavior with fewer subjects.


Assuntos
Conectoma , Atenção , Encéfalo/fisiologia , Cognição/fisiologia , Conectoma/métodos , Humanos , Imageamento por Ressonância Magnética/métodos , Rede Nervosa/diagnóstico por imagem , Rede Nervosa/fisiologia
17.
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi ; 39(2): 237-247, 2022 Apr 25.
Artigo em Chinês | MEDLINE | ID: mdl-35523544

RESUMO

Brain functional network changes over time along with the process of brain development, disease, and aging. However, most of the available measurements for evaluation of the difference (or similarity) between the individual brain functional networks are for charactering static networks, which do not work with the dynamic characteristics of the brain networks that typically involve a long-span and large-scale evolution over the time. The current study proposes an index for measuring the similarity of dynamic brain networks, named as dynamic network similarity (DNS). It measures the similarity by combining the "evolutional" and "structural" properties of the dynamic network. Four sets of simulated dynamic networks with different evolutional and structural properties (varying amplitude of changes, trend of changes, distribution of connectivity strength, range of connectivity strength) were generated to validate the performance of DNS. In addition, real world imaging datasets, acquired from 13 stroke patients who were treated by transcranial direct current stimulation (tDCS), were used to further validate the proposed method and compared with the traditional similarity measurements that were developed for static network similarity. The results showed that DNS was significantly correlated with the varying amplitude of changes, trend of changes, distribution of connectivity strength and range of connectivity strength of the dynamic networks. DNS was able to appropriately measure the significant similarity of the dynamics of network changes over the time for the patients before and after the tDCS treatments. However, the traditional methods failed, which showed significantly differences between the data before and after the tDCS treatments. The experiment results demonstrate that DNS may robustly measure the similarity of evolutional and structural properties of dynamic networks. The new method appears to be superior to the traditional methods in that the new one is capable of assessing the temporal similarity of dynamic functional imaging data.


Assuntos
Rede Nervosa , Estimulação Transcraniana por Corrente Contínua , Envelhecimento/fisiologia , Encéfalo/diagnóstico por imagem , Encéfalo/fisiologia , Mapeamento Encefálico , Humanos , Imageamento por Ressonância Magnética/métodos , Rede Nervosa/fisiologia , Estimulação Transcraniana por Corrente Contínua/métodos
18.
Neuropsychologia ; 172: 108269, 2022 07 29.
Artigo em Inglês | MEDLINE | ID: mdl-35595064

RESUMO

Increases in cardiovascular risks such as high blood pressure and low physical fitness have been independently associated with altered default mode network (DMN) activation patterns in healthy aging. However, cardiovascular risk is a multidimensional health problem. Therefore, we need to investigate multiple cardiovascular risk factors and their contributions to cognition and DMN activations in older adults, which has not yet been done. The current fMRI study examined contributions of two common modifiable cardiovascular risk factors (arterial stiffness and physical fitness) on DMN activations involved during random n-back, a task of executive functioning and working memory, in older adults. The results show that high cardiovascular risk of either increased arterial stiffness or decreased fitness independently contributed to worse task performance and reduced deactivations in two DMN regions: the anterior and posterior cingulate cortices. We then examined not only the potential interaction between the two risk factors, but also their additive (i.e., combined) effect on performance and DMN deactivations. A significant interaction between the two cardiovascular risk factors was observed on performance, with arterial stiffness moderating the relationship between physical fitness and random n-back accuracy. The additive effect of the two factors on task performance was driven by arterial stiffness. Arterial stiffness was also found to be the driving factor when the additive effect of the two risk factors was examined on DMN deactivations. However, in posterior cingulate cortex, a hub region of the DMN, the additive effect on its deactivation was significantly higher than the effect of each risk factor alone. These results suggest that the effects of cardiovascular risks on the aging brain are complicated and multi-dimensional, with arterial stiffness moderating or driving the combined effects on performance and anterior DMN deactivations, but physical fitness contributing additional effect to posterior DMN deactivation during executive functioning.


Assuntos
Doenças Cardiovasculares , Envelhecimento Saudável , Rigidez Vascular , Idoso , Encéfalo/fisiologia , Mapeamento Encefálico , Doenças Cardiovasculares/diagnóstico por imagem , Rede de Modo Padrão , Função Executiva/fisiologia , Fatores de Risco de Doenças Cardíacas , Humanos , Imageamento por Ressonância Magnética/métodos , Rede Nervosa/fisiologia , Fatores de Risco
19.
Adv Exp Med Biol ; 1359: 201-234, 2022.
Artigo em Inglês | MEDLINE | ID: mdl-35471541

RESUMO

For constructing neuronal network models computational neuroscientists have access to wide-ranging anatomical data that nevertheless tend to cover only a fraction of the parameters to be determined. Finding and interpreting the most relevant data, estimating missing values, and combining the data and estimates from various sources into a coherent whole is a daunting task. With this chapter we aim to provide guidance to modelers by describing the main types of anatomical data that may be useful for informing neuronal network models. We further discuss aspects of the underlying experimental techniques relevant to the interpretation of the data, list particularly comprehensive data sets, and describe methods for filling in the gaps in the experimental data. Such methods of "predictive connectomics" estimate connectivity where the data are lacking based on statistical relationships with known quantities. Exploiting organizational principles that link the plethora of data in a unifying framework can be useful for informing computational models. Besides overarching principles, we touch upon the most prominent features of brain organization that are likely to influence predicted neuronal network dynamics, with a focus on the mammalian cerebral cortex. Given the still existing need for modelers to navigate a complex data landscape full of holes and stumbling blocks, it is vital that the field of neuroanatomy is moving toward increasingly systematic data collection, representation, and publication.


Assuntos
Conectoma , Rede Nervosa , Animais , Encéfalo/fisiologia , Córtex Cerebral , Conectoma/métodos , Mamíferos , Rede Nervosa/fisiologia , Neurônios
20.
Sci Rep ; 12(1): 6303, 2022 04 15.
Artigo em Inglês | MEDLINE | ID: mdl-35428802

RESUMO

Everyday cognitive functioning is characterized by constant alternations between different modes of information processing, driven by constant fluctuations in environmental demands. At the neural level, this is realized through corresponding dynamic shifts in functional activation and network connectivity. A distinction is often made between resting and task processing and between task-negative and task-positive functional networks. The Default Mode Network (DMN) is classically considered as a resting state (i.e. task-negative) network, upregulated in the absence of cognitive demands. In contrast, task-positive networks have been labelled the Extrinsic Mode Network (EMN). We investigated changes in brain activation and functional network connectivity in an experimental situation of repeated alterations between levels of cognitive effort, following a block-design. Using fMRI and a classic Stroop paradigm, participants switched back and forth between periods of no effort (resting), low effort (word reading, i.e. automatic processing based on learned internal representations and rules) and high effort (color naming, i.e. cognitively controlled perceptual processing of specific features of external stimuli). Results showed an expected EMN-activation for task versus resting contrasts, and DMN-activation for rest versus task contrasts. The DMN was in addition more strongly activated during periods of low effort contrasted with high effort, suggesting a gradual up- and down-regulation of the DMN network, depending on the level of demand and the type of processing required. The often reported "anti-correlation" between DMN and EMN was strongest during periods of low effort, indicating intermittent contributions of both networks. Taken together, these results challenge the traditional view of the DMN as solely a task-negative network. Instead, both the EMN and DMN may contribute to low-effort cognitive processing. In contrast, periods of resting and high effort are dominated by the DMN and EMN, respectively.


Assuntos
Mapeamento Encefálico , Rede de Modo Padrão , Encéfalo/diagnóstico por imagem , Encéfalo/fisiologia , Cognição , Humanos , Imageamento por Ressonância Magnética/métodos , Rede Nervosa/diagnóstico por imagem , Rede Nervosa/fisiologia , Vias Neurais/fisiologia , Descanso/fisiologia
SELEÇÃO DE REFERÊNCIAS
DETALHE DA PESQUISA
...