Estimation of global and local complexities of brain networks: A random walks approach

The complexity of brain activity has been observed at many spatial scales and has been proposed to differentiate between mental states and disorders. Here we introduced a new measure of (global) network complexity, constructed as the sum of the complexities of its nodes (i.e., local complexity). The complexity of each node is obtained by comparing the sample entropy of the time series generated by the movement of a random walker on the network resulting from removing the node and its connections, with the sample entropy of the time series obtained from a regular lattice (ordered state) and a random network (disordered state). We studied the complexity of fMRI-based resting-state networks. We found that positively correlated (pos) networks comprising only the positive functional connections have higher complexity than anticorrelation (neg) networks (comprising the negative connections) and the network consisting of the absolute value of all connections (abs). We also observed a significant correlation between complexity and the strength of functional connectivity in the pos network. Our results suggest that the pos network is related to the information processing in the brain and that functional connectivity studies should analyze pos and neg networks separately instead of the abs network, as is commonly done.

Download Full-text

Estimation of Global and Local Complexities of Brain Networks: A Random Walks Approach

10.1101/733725 ◽

2019 ◽

Author(s):

Roberto C. Sotero ◽

Lazaro M. Sanchez-Rodriguez ◽

Narges Moradi

Keyword(s):

Time Series ◽

Functional Connectivity ◽

Brain Activity ◽

Spatial Scales ◽

Mental States ◽

Global Scale ◽

Sample Entropy ◽

Local Scale ◽

Functional Connections ◽

Local Complexity

AbstractThe complexity of brain activity has been observed at many spatial scales and there exists increasing evidence supporting its use in differentiating between mental states and disorders. Here we proposed a new measure of network (global) complexity that is constructed as the sum of the complexities of its nodes (i.e, local complexity). The local complexity of each node is regarded as an index that compares the sample entropy of the time series generated by the movement of a random walker on the network resulting from removing the node and its connections, with the sample entropy of the time series obtained from a regular lattice (the ordered state) and an Erdös-Renyi network (disordered state). We studied the complexity of fMRI-based resting-state functional networks. We found that positively correlated, or “pos”, network comprising only the positive functional connections has higher complexity than the anticorrelation (“neg”) network (comprising the negative functional connections) and the network consisting of the absolute value of all connections (“abs”). We also found a significant correlation between complexity and the strength of functional connectivity. For the pos network this correlation is significantly weaker at the local scale compared to the global scale, whereas for the neg network the link is stronger at the local scale than at the global scale, but still weaker than for the pos network. Our results suggest that the pos network is related to the information processing in the brain and should be used for functional connectivity analysis instead of the abs network as is usually done.

Download Full-text

Abnormal interhemispheric functional connectivity in patients with strabismic amblyopia: a resting-state fMRI study using voxel-mirrored homotopic connectivity

BMC Ophthalmology ◽

10.1186/s12886-021-02015-0 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Shuang Zhang ◽

Gui-Ping Gao ◽

Wen-Qing Shi ◽

Biao Li ◽

Qi Lin ◽

...

Keyword(s):

Functional Connectivity ◽

Receiver Operating Characteristic ◽

Operating Characteristic ◽

Brain Activity ◽

Characteristic Curve ◽

Clinical Manifestations ◽

Resting State Fmri ◽

Diagnostic Value ◽

Functional Connections ◽

Receiver Operating

Abstract Background Previous studies have demonstrated that strabismus amblyopia can result in markedly brain function alterations. However, the differences in spontaneous brain activities of strabismus amblyopia (SA) patients still remain unclear. Therefore, the current study intended to employthe voxel-mirrored homotopic connectivity (VMHC) method to investigate the intrinsic brain activity changes in SA patients. Purpose To investigate the changes in cerebral hemispheric functional connections in patients with SA and their relationship with clinical manifestations using the VMHC method. Material and methods In the present study, a total of 17 patients with SA (eight males and nine females) and 17 age- and weight-matched healthy control (HC) groups were enrolled. Based on the VMHC method, all subjects were examined by functional magnetic resonance imaging. The functional interaction between cerebral hemispheres was directly evaluated. The Pearson’s correlation test was used to analyze the clinical features of patients with SA. In addition, their mean VMHC signal values and the receiver operating characteristic curve were used to distinguish patients with SA and HC groups. Results Compared with HC group, patients with SA had higher VMHC values in bilateral cingulum ant, caudate, hippocampus, and cerebellum crus 1. Moreover, the VMHC values of some regions were positively correlated with some clinical manifestations. In addition, receiver operating characteristic curves presented higher diagnostic value in these areas. Conclusion SA subjects showed abnormal brain interhemispheric functional connectivity in visual pathways, which might give some instructive information for understanding the neurological mechanisms of SA patients.

Download Full-text

Computerized physical and cognitive training improves the functional architecture of the brain in adults with Down syndrome: A network science EEG study

Network Neuroscience ◽

10.1162/netn_a_00177 ◽

2020 ◽

pp. 1-21

Author(s):

Alexandra Anagnostopoulou ◽

Charis Styliadis ◽

Panagiotis Kartsidis ◽

Evangelia Romanopoulou ◽

Vasiliki Zilidou ◽

...

Keyword(s):

Down Syndrome ◽

Functional Connectivity ◽

Cognitive Training ◽

Resting State ◽

Network Architecture ◽

Right Hemisphere ◽

Random Network ◽

Brain Activity ◽

Hierarchical Organization ◽

The Brain

Understanding the neuroplastic capacity of people with Down syndrome (PwDS) can potentially reveal the causal relationship between aberrant brain organization and phenotypic characteristics. We used resting-state EEG recordings to identify how a neuroplasticity-triggering training protocol relates to changes in the functional connectivity of the brain’s intrinsic cortical networks. Brain activity of 12 PwDS before and after a 10-week protocol of combined physical and cognitive training was statistically compared to quantify changes in directed functional connectivity in conjunction with psychosomatometric assessments. PwDS showed increased connectivity within the left hemisphere and from left-to-right hemisphere, as well as increased physical and cognitive performance. Our findings reveal a strong adaptive neuroplastic reorganization as a result of the training that leads to a less-random network with a more pronounced hierarchical organization. Our results go beyond previous findings by indicating a transition to a healthier, more efficient, and flexible network architecture, with improved integration and segregation abilities in the brain of PwDS. Resting-state electrophysiological brain activity is used here for the first time to display meaningful relationships to underlying Down syndrome processes and outcomes of importance in a translational inquiry. This trial is registered with ClinicalTrials.gov Identifier NCT04390321.

Download Full-text

Joint estimation of neural sources and their functional connections from MEG data

10.1101/2020.10.04.325563 ◽

2020 ◽

Author(s):

Narayan Puthanmadam Subramaniyam ◽

Filip Tronarp ◽

Simo Särkkä ◽

Lauri Parkkonen

Keyword(s):

Time Series ◽

Functional Connectivity ◽

Structural Connectivity ◽

Identification Problem ◽

Joint Estimation ◽

Bayesian Filtering ◽

Sequential Approach ◽

Connectivity Structure ◽

Saem Algorithm ◽

Functional Connections

AbstractCurrent techniques to estimate directed functional connectivity from magnetoencephalography (MEG) signals involve two sequential steps; 1) Estimation of the sources and their amplitude time series from the MEG data by solving the inverse problem, and 2) fitting a multivariate autoregressive (MVAR) model to these time series for the estimation of AR coefficients, which reflect the directed interactions between the sources. However, such a sequential approach is not optimal since i) source estimation algorithms typically assume that the sources are independent, ii) the information provided by the connectivity structure is not used to inform the estimation of source amplitudes, and iii) the limited spatial resolution of source estimates often leads to spurious connectivity due to spatial leakage.Here, we present an algorithm to jointly estimate the source and connectivity parameters using Bayesian filtering, which does not require anatomical constraints in form of structural connectivity or a-priori specified regions-of-interest. By formulating a state-space model for the locations and amplitudes of a given number of sources, we show that estimation of functional connectivity can be reduced to a system identification problem. We derive a solution to this problem using a variant of the expectation–maximization (EM) algorithm known as stochastic approximation EM (SAEM).Compared to the traditional two-step approach, the joint approach using the SAEM algorithm provides a more accurate reconstruction of connectivity parameters, which we show with a connectivity benchmark simulation as well as with an electrocorticography-based simulation of MEG data. Using real MEG responses to visually presented faces in 16 subjects, we also demonstrate that our method gives source and connectivity estimates that are both physiologically plausible and largely consistent across subjects. In conclusion, the proposed joint-estimation approach based on the SAEM algorithm outperforms the traditional two-step approach in determining functional connectivity structure in MEG data.

Download Full-text

Time Course of Changes in Brain Activity and Functional Connectivity Associated With Long-Term Adaptation to a Rotational Transformation

Journal of Neurophysiology ◽

10.1152/jn.00984.2004 ◽

2005 ◽

Vol 93 (4) ◽

pp. 2254-2262 ◽

Cited By ~ 47

Author(s):

Valeria Della-Maggiore ◽

Anthony R. McIntosh

Keyword(s):

Functional Connectivity ◽

Sensorimotor Cortex ◽

Time Course ◽

Brain Activity ◽

Visuomotor Adaptation ◽

Middle Temporal Gyrus ◽

Middle Temporal ◽

Functional Connections ◽

Rotational Transformation

The purpose of this study was to examine the time course of changes in cerebral activity and functional connectivity during long-term adaptation to a visuomotor transformation. Positron emission tomography was used to measure changes in brain activity as subjects tracked a target under the influence of a rotational transformation that distorted visual feedback. The experiment was 1 week long and consisted of two scanning sessions (obtained on days 2 and 7), aimed at examining early and late stages of learning. On average, visuomotor adaptation was achieved within 3 days. During early stages of adaptation, better performance was associated with greater activity in brain areas related to attention including bilateral dorso- and ventrolateral prefrontal cortices, frontal eye fields, and the human homologue of area MT. However, as adaptation proceeded, improvements in performance were associated with greater activity in motor regions such as the left (contralateral) sensorimotor cortex, bilateral anterior cerebellum, left cingulate motor area, right putamen, and a nonmotor region within the middle temporal gyrus. This learning-specific shift in brain activity was associated with a progressive change in the functional connectivity of these regions toward the end of the first session. Interestingly, only the functional connections between the anterior cerebellum, left middle temporal gyrus, and left sensorimotor cortex remained strong once visuomotor adaptation was achieved. Our findings suggest that visuomotor adaptation is not only reflected in persistent changes in activity in motor-related regions, but also in the strengthening and maintenance of specific functional connections.

Download Full-text

Genetically defined cellular correlates of the baseline brain MRI signal

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1808121115 ◽

2018 ◽

Vol 115 (41) ◽

pp. E9727-E9736 ◽

Cited By ~ 12

Author(s):

Jie Wen ◽

Manu S. Goyal ◽

Serguei V. Astafiev ◽

Marcus E. Raichle ◽

Dmitriy A. Yablonskiy

Keyword(s):

Functional Connectivity ◽

Human Brain ◽

Brain Activity ◽

Brain Mri ◽

Blood Oxygenation ◽

Brain Atlas ◽

Functional Connections ◽

Fmri Signal ◽

Useful Signal

fMRI revolutionized neuroscience by allowing in vivo real-time detection of human brain activity. While the nature of the fMRI signal is understood as resulting from variations in the MRI signal due to brain-activity-induced changes in the blood oxygenation level (BOLD effect), these variations constitute a very minor part of a baseline MRI signal. Hence, the fundamental (and not addressed) questions are how underlying brain cellular composition defines this baseline MRI signal and how a baseline MRI signal relates to fMRI. Herein we investigate these questions by using a multimodality approach that includes quantitative gradient recalled echo (qGRE), volumetric and functional connectivity MRI, and gene expression data from the Allen Human Brain Atlas. We demonstrate that in vivo measurement of the major baseline component of a GRE signal decay rate parameter (R2t*) provides a unique genetic perspective into the cellular constituents of the human cortex and serves as a previously unidentified link between cortical tissue composition and fMRI signal. Data show that areas of the brain cortex characterized by higher R2t* have high neuronal density and have stronger functional connections to other brain areas. Interestingly, these areas have a relatively smaller concentration of synapses and glial cells, suggesting that myelinated cortical axons are likely key cortical structures that contribute to functional connectivity. Given these associations, R2t* is expected to be a useful signal in assessing microstructural changes in the human brain during development and aging in health and disease.

Download Full-text

Act Natural: Functional Connectivity from Naturalistic Stimuli fMRI Outperforms Resting-State in Predicting Brain Activity

10.1101/2021.11.01.466749 ◽

2021 ◽

Author(s):

Shachar Gal ◽

Yael Coldham ◽

Michal Bernstein-Eliav ◽

Ido Tavor

Keyword(s):

Functional Connectivity ◽

Resting State ◽

Brain Activity ◽

Predictive Modelling ◽

Resting State Fmri ◽

Resting State Functional Connectivity ◽

Promising Alternative ◽

Functional Connections ◽

Human Connectome Project ◽

Intelligence Scores

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.

Download Full-text

Modulations of local synchrony over time lead to resting-state functional connectivity in a parsimonious large-scale brain model

10.1101/2021.01.20.427443 ◽

2021 ◽

Author(s):

Oscar Portoles ◽

Yuzhen Qin ◽

Jonathan Hadida ◽

Mark Woolrich ◽

Ming Cao ◽

...

Keyword(s):

Functional Connectivity ◽

Resting State ◽

Large Scale ◽

Brain Activity ◽

Brain Regions ◽

Global Network ◽

Resting State Functional Connectivity ◽

Time Resolved ◽

Networks Structure ◽

Over Time

AbstractBiophysical models of large-scale brain activity are a fundamental tool for understanding the mechanisms underlying the patterns observed with neuroimaging. These models combine a macroscopic description of the within- and between-ensemble dynamics of neurons within a single architecture. A challenge for these models is accounting for modulations of within-ensemble synchrony over time. Such modulations in local synchrony are fundamental for modeling behavioral tasks and resting-state activity. Another challenge comes from the difficulty in parametrizing large scale brain models which hinders researching principles related with between-ensembles differences. Here we derive a parsimonious large scale brain model that can describe fluctuations of local synchrony. Crucially, we do not reduce within-ensemble dynamics to macroscopic variables first, instead we consider within and between-ensemble interactions similarly while preserving their physiological differences. The dynamics of within-ensemble synchrony can be tuned with a parameter which manipulates local connectivity strength. We simulated resting-state static and time-resolved functional connectivity of alpha band envelopes in models with identical and dissimilar local connectivities. We show that functional connectivity emerges when there are high fluctuations of local and global synchrony simultaneously (i.e. metastable dynamics). We also show that for most ensembles, leaning towards local asynchrony or synchrony correlates with the functional connectivity with other ensembles, with the exception of some regions belonging to the default-mode network.Author summaryHere we present and evaluate a parsimonious model of large-scale brain activity. The model represents the brain as a network-of-networks structure. The sub-networks describe the neural activity within a brain region, and the global network encodes interactions between brain regions. Unlike other models, it capture progressive changes of local synchrony and local dynamics can be tuned with one parameter. Therefore the model could be used not only to model resting-state, but also behavioural tasks. Furthermore, we describe a simple framework that can deal with the arduous task of identifying global and local parameters.

Download Full-text