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

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.

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

2020 ◽  
Vol 4 (3) ◽  
pp. 575-594
Author(s):  
Roberto C. Sotero ◽  
Lazaro M. Sanchez-Rodriguez ◽  
Narges Moradi ◽  
Mehdy Dousty
Keyword(s):  
Time Series ◽  
Functional Connectivity ◽  
Random Network ◽  
Brain Activity ◽  
Spatial Scales ◽  
Mental States ◽  
Sample Entropy ◽  
Global Network ◽  
Random Walker ◽  
Functional Connections

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.

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

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.

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

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.

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

2005 ◽  
Vol 93 (4) ◽  
pp. 2254-2262 ◽  
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.

scholarly journals Genetically defined cellular correlates of the baseline brain MRI signal

2018 ◽  
Vol 115 (41) ◽  
pp. E9727-E9736 ◽  
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.

scholarly journals The map of biodiversity: From local to global scales

2018 ◽  
Author(s):  
Maria Anton Pardo
Keyword(s):  
Environmental Variables ◽  
Environmental Heterogeneity ◽  
Spatial Scales ◽  
Global Scale ◽  
Local Scale ◽  
Habitat Size ◽  
Diversity Patterns ◽  
Regional Species Pool ◽  
Species Area ◽  
Species Area Relationship

Species richness is not homogeneous in space and it normally presents differences when comparing among different sites. These differences often respond to gradients in one or several factors which create biodiversity patterns in space and are scale-dependent. At a local scale, diversity patterns depend on the habitat size (species-area relationship), the productivity, the environmental harshness, the frequency and intensity of disturbance, or the regional species pool. Regional diversity may be influenced by environmental heterogeneity (increasing dissimilarity), although it could act also at smaller or larger spatial scales, and the connectivity among habitats. Finally, at a global scale, diversity patterns are found with the latitude, the altitude or the depth, although these factors are surrogates or one or several environmental variables (productivity, area, isolation, or harshness).

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

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.

scholarly journals Impacto do aumento de CO2 nas Precipitações do estado de Pernambuco

2021 ◽  
Vol 14 (3) ◽  
pp. 1828-1839
Author(s):  
Josiclêda Domiciano Galvincio
Keyword(s):  
Climate Change ◽  
Water Balance ◽  
Hydrological Cycle ◽  
Spatial Scales ◽  
Global Scale ◽  
Local Scale ◽  
The State ◽  
Atmospheric Co2 ◽  
Hydrological Response ◽  
Climatic Characteristics

Seeking to mitigate the effects of climate change requires knowing the current situation and the possible consequences of the increase in CO2 in the atmosphere in different areas of knowledge and spatial scales. On a global scale, studies are already widespread. At the regional and local scale, these studies still need to be advanced. Given the above, this study aims to assess the impacts of atmospheric CO2 on rainfall in the state of Pernambuco. Five municipalities in the state were chosen, Recife, Garanhuns, Serra Talhada, Belém do São Francisco and Petrolina. These municipalities represent the mesoregions of the state, which are related to their climatic characteristics. Several statistical methods were applied to identify the relationships between variables and how strong these relationships are, correlation and multivariate. The SUPER-System of Hydrological Response Units for Pernambuco was used to discuss the results with the water balance of Pernambuco basins. The results show that Pernambuco will have two impacting conditions with an increase in atmospheric CO2, Agreste and Litoral, an increase in precipitation and Sertão, a decrease in precipitation. The strongest impact being the Agreste. Considering the SUPER, it was possible to see that around 60% of Pernambuco's precipitation is transformed into evapotranspiration. It is known that the increase in CO2 impacts plant evapotranspiration. Thus, it is possible to state that not only changes in precipitation will impact water resources, but also evapotranspiration, which are important variables in the hydrological cycle of Pernambuco basins.

scholarly journals 8-Day and Daily Maximum and Minimum Air Temperature Estimation via Machine Learning Method on a Climate Zone to Global Scale

2021 ◽  
Vol 13 (12) ◽  
pp. 2355
Author(s):  
Linglin Zeng ◽  
Yuchao Hu ◽  
Rui Wang ◽  
Xiang Zhang ◽  
Guozhang Peng ◽  
...  
Keyword(s):  
Spatial Scale ◽  
Air Temperature ◽  
Spatial Scales ◽  
Model Performance ◽  
Global Scale ◽  
Scale Model ◽  
Validation Dataset ◽  
Daily Maximum ◽  
Climate Zone ◽  
Temporal And Spatial

Air temperature (Ta) is a required input in a wide range of applications, e.g., agriculture. Land Surface Temperature (LST) products from Moderate Resolution Imaging Spectroradiometer (MODIS) are widely used to estimate Ta. Previous studies of these products in Ta estimation, however, were generally applied in small areas and with a small number of meteorological stations. This study designed both temporal and spatial experiments to estimate 8-day and daily maximum and minimum Ta (Tmax and Tmin) on three spatial scales: climate zone, continental and global scales from 2009 to 2018, using the Random Forest (RF) method based on MODIS LST products and other auxiliary data. Factors contributing to the relation between LST and Ta were determined based on physical models and equations. Temporal and spatial experiments were defined by the rules of dividing the training and validation datasets for the RF method, in which the stations selected in the training dataset were all included or not in the validation dataset. The RF model was first trained and validated on each spatial scale, respectively. On a global scale, model accuracy with a determination coefficient (R2) > 0.96 and root mean square error (RMSE) < 1.96 °C and R2 > 0.95 and RMSE < 2.55 °C was achieved for 8-day and daily Ta estimations, respectively, in both temporal and spatial experiments. Then the model was trained and cross-validated on each spatial scale. The results showed that the data size and station distribution of the study area were the main factors influencing the model performance at different spatial scales. Finally, the spatial patterns of the model performance and variable importance were analyzed. Both daytime and nighttime LST had a significant contribution in the 8-day Tmax estimation on all the three spatial scales; while their contribution in daily Tmax estimation varied over different continents or climate zones. This study was expected to improve our understanding of Ta estimation in terms of accuracy variations and influencing variables on different spatial and temporal scales. The future work mainly includes identifying underlying mechanisms of estimation errors and the uncertainty sources of Ta estimation from a local to a global scale.

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