scholarly journals Brain Laterality Dynamics Support Human Cognition

2021 ◽  
Author(s):  
Xinran Wu ◽  
Xiang-Zhen Kong ◽  
Deniz Vatansever ◽  
Zhaowen Liu ◽  
Kai Zhang ◽  
...  
Keyword(s):  
Time Series ◽  
Dynamic Balance ◽  
Structural Connectivity ◽  
Positive Association ◽  
Hemispheric Lateralization ◽  
Human Cognition ◽  
Brain Lateralization ◽  
Brain Organization ◽  
Time Resolved ◽  
Human Connectome Project

Hemispheric lateralization constitutes a core architectural principle of human brain organization underlying cognition, often argued to represent a stable, trait-like feature. However, emerging evidence underlines the inherently dynamic nature of brain networks, in which time-resolved alterations in functional lateralization remain uncharted. Integrating dynamic network approaches with the concept of hemispheric laterality, we map the spatiotemporal architecture of whole-brain lateralization in a large sample of high-quality resting-state fMRI data (N=991, Human Connectome Project). We reveal distinct laterality dynamics across lower-order sensorimotor systems and higher-order associative networks. Specifically, we expose two aspects of the laterality dynamics: laterality fluctuations, defined as the standard deviation of laterality time series, and laterality reversal, referring to the number of zero-crossings in laterality time series. These two measures are associated with moderate and extreme changes in laterality over time, respectively. While laterality fluctuations depict positive association with language function and cognitive flexibility, laterality reversal shows a negative association with the same neurocognitive factors. These opposing interactions indicate a dynamic balance between intra- and inter-hemispheric communication, i.e., segregation and integration of information across hemispheres. Furthermore, in their time-resolved laterality index, the default-mode and language networks correlate negatively with visual/sensorimotor and attention networks, indicating flexible while parallel processing capabilities that are linked to better out-of-scanner cognitive performance. Finally, the laterality dynamics correlate with regional metabolism and structural connectivity and showed significant heritability. Our results provide insights into the adaptive nature of the lateralized brain and new perspectives for future studies of human cognition, genetics and brain disorders.

scholarly journals Timescales of spontaneous fMRI fluctuations relate to structural connectivity in the brain

2019 ◽  
Author(s):  
John Fallon ◽  
Phil Ward ◽  
Linden Parkes ◽  
Stuart Oldham ◽  
Aurina Arnatkevic̆iūtė ◽  
...  
Keyword(s):  
Resting State ◽  
Structural Connectivity ◽  
Resting State Fmri ◽  
Information Storage ◽  
Brain Organization ◽  
Temporal Properties ◽  
Temporal Features ◽  
Human Connectome Project ◽  
The Brain ◽  
Connectivity Strength

AbstractIntrinsic timescales of activity fluctuations vary hierarchically across the brain. This variation reflects a broad gradient of functional specialization in information storage and processing, with integrative association areas displaying slower timescales that are thought to reflect longer temporal processing windows. The organization of timescales is associated with cognitive function, distinctive between individuals, and disrupted in disease, but we do not yet understand how the temporal properties of activity dynamics are shaped by the brain’s underlying structural-connectivity network. Using resting-state fMRI and diffusion MRI data from 100 healthy individuals from the Human Connectome Project, here we show that the timescale of resting-state fMRI dynamics increases with structural-connectivity strength, matching recent results in the mouse brain. Our results hold at the level of individuals, are robust to parcellation schemes, and are conserved across a range of different timescale-related statistics. We establish a comprehensive BOLD dynamical signature of structural connectivity strength by comparing over 6000 time-series features, highlighting a range of new temporal features for characterizing BOLD dynamics, including measures of stationarity and symbolic motif frequencies. Our findings indicate a conserved property of mouse and human brain organization in which a brain region’s spontaneous activity fluctuations are closely related to their surrounding structural scaffold.

scholarly journals Timescales of spontaneous fMRI fluctuations relate to structural connectivity in the brain

2020 ◽  
Vol 4 (3) ◽  
pp. 788-806 ◽  
Author(s):  
John Fallon ◽  
Phillip G. D. Ward ◽  
Linden Parkes ◽  
Stuart Oldham ◽  
Aurina Arnatkevičiūtė ◽  
...  
Keyword(s):  
Resting State ◽  
Structural Connectivity ◽  
Resting State Fmri ◽  
Information Storage ◽  
Brain Organization ◽  
Temporal Properties ◽  
Temporal Features ◽  
Human Connectome Project ◽  
The Brain ◽  
Connectivity Strength

Intrinsic timescales of activity fluctuations vary hierarchically across the brain. This variation reflects a broad gradient of functional specialization in information storage and processing, with integrative association areas displaying slower timescales that are thought to reflect longer temporal processing windows. The organization of timescales is associated with cognitive function, distinctive between individuals, and disrupted in disease, but we do not yet understand how the temporal properties of activity dynamics are shaped by the brain’s underlying structural connectivity network. Using resting-state fMRI and diffusion MRI data from 100 healthy individuals from the Human Connectome Project, here we show that the timescale of resting-state fMRI dynamics increases with structural connectivity strength, matching recent results in the mouse brain. Our results hold at the level of individuals, are robust to parcellation schemes, and are conserved across a range of different timescale- related statistics. We establish a comprehensive BOLD dynamical signature of structural connectivity strength by comparing over 6,000 time series features, highlighting a range of new temporal features for characterizing BOLD dynamics, including measures of stationarity and symbolic motif frequencies. Our findings indicate a conserved property of mouse and human brain organization in which a brain region’s spontaneous activity fluctuations are closely related to their surrounding structural scaffold.

scholarly journals The visual word form area (VWFA) is part of both language and attention circuitry

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Lang Chen ◽  
Demian Wassermann ◽  
Daniel A. Abrams ◽  
John Kochalka ◽  
Guillermo Gallardo-Diez ◽  
...  
Keyword(s):  
Spatial Attention ◽  
Visual Processing ◽  
Structural Connectivity ◽  
Visual Word ◽  
Word Form ◽  
Imaging Data ◽  
Brain Organization ◽  
General Role ◽  
Visual Word Form Area ◽  
Human Connectome Project

AbstractWhile predominant models of visual word form area (VWFA) function argue for its specific role in decoding written language, other accounts propose a more general role of VWFA in complex visual processing. However, a comprehensive examination of structural and functional VWFA circuits and their relationship to behavior has been missing. Here, using high-resolution multimodal imaging data from a large Human Connectome Project cohort (N = 313), we demonstrate robust patterns of VWFA connectivity with both canonical language and attentional networks. Brain-behavior relationships revealed a striking pattern of double dissociation: structural connectivity of VWFA with lateral temporal language network predicted language, but not visuo-spatial attention abilities, while VWFA connectivity with dorsal fronto-parietal attention network predicted visuo-spatial attention, but not language abilities. Our findings support a multiplex model of VWFA function characterized by distinct circuits for integrating language and attention, and point to connectivity-constrained cognition as a key principle of human brain organization.

scholarly journals Functional connectivity of fMRI using differential covariance predicts structural connectivity and behavioral reaction times

2021 ◽  
Author(s):  
Yusi Chen ◽  
Qasim Bukhari ◽  
Tiger Wutu Lin ◽  
Terrence J Sejnowski
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Functional Connectivity ◽  
Resting State ◽  
Structural Connectivity ◽  
Reaction Times ◽  
Resonance Imaging ◽  
Brain Areas ◽  
Human Connectome Project ◽  
Wide Range

Recordings from resting state functional magnetic resonance imaging (rs-fMRI) reflect the influence of pathways between brain areas. A wide range of methods have been proposed to measure this functional connectivity (FC), but the lack of ''ground truth'' has made it difficult to systematically validate them. Most measures of FC produce connectivity estimates that are symmetrical between brain areas. Differential covariance (dCov) is an algorithm for analyzing FC with directed graph edges. Applied to synthetic datasets, dCov-FC was more effective than covariance and partial correlation in reducing false positive connections and more accurately matching the underlying structural connectivity. When we applied dCov-FC to resting state fMRI recordings from the human connectome project (HCP) and anesthetized mice, dCov-FC accurately identified strong cortical connections from diffusion Magnetic Resonance Imaging (dMRI) in individual humans and viral tract tracing in mice. In addition, those HCP subjects whose rs-fMRI were more integrated, as assessed by a graph-theoretic measure, tended to have shorter reaction times in several behavioral tests. Thus, dCov-FC was able to identify anatomically verified connectivity that yielded measures of brain integration causally related to behavior.

scholarly journals BUDGETARY PROJECTION IN THE SYSTEM OF FINANCIAL AND ECONOMIC REGULATION OF SOCIAL PROCESSES

2020 ◽  
Vol 6 (1) ◽  
pp. 130
Author(s):  
Igor Chugunov ◽  
Valentina Makohon
Keyword(s):  
Time Series ◽  
Fiscal Policy ◽  
Dynamic Balance ◽  
Social Processes ◽  
System Stability ◽  
Policy Outcomes ◽  
Institutional Changes ◽  
Economic Regulation ◽  
Macroeconomic Stability

The purpose of the article is to reveal the role of budgetary projection in the system of financial and economic regulation of social processes within the framework of improving the efficiency of fiscal policy intended to macroeconomic stability maintenance in both countries with transformational and advanced economies. The comparative and factorial methods allowed to developthe features of the institutional environment of the budgetary progection methodology, to identify approaches for its improvement. Methodology. Substantiation of the role of budget forecasting in the system of financial and economic regulation of social processes, determination of provisions for improving its methodology is based on generalized and systematic approaches that are applied in both developed and transformational economies. An analysis of the stages of the process and the budgetary projection methods evaluation, that are used in different countries, have been carried out. Results showed that the efficient budgetary projection methodology is the basis for sound fiscal policy. The development of realistic budgetary projections facilitates justified management decisions aimed at ensuring the country financial firmness. Devia-tions from budget revenues from the projected indicators do not make it possible to achieve certain fiscal policy outcomes and, accordingly, cause a budget cut. In order to develop realistic budgetary projections, a welldesigned and coherent database is needed for all time series, necessary to analyze and project budget revenues. Time series of key determinants affecting the budget revenues level should be available at different frequencies (monthly, quarterly, annually). Where data reflecting similar economic processes by different revenue sources are available, any differences between them shall be determined by reference to their coverage and methodology. Practical implications. Budgetary projections are the basis for the formation of effective fiscal policy and the benchmark of the reproduction process. Adequate level of justification for budget projection will help to provide a dynamic balance of budgetary indicators and the budgetary system stability. Institutional changes to the budgetary projection methodology should be made on the basis of taking into account the dynamic interrelation of budgetary and macroeconomic indicators. The remarkable task here is the development of an economic and mathematical model based on the assessment of the national economy capabilities by reference to the assessment of macroeconomic proportions and the corresponding social and economic conditions of social production. Value/ originality. Developing the budgetary projection approaches in the context of improvement of the fiscal policy efficiency is an important precondition for ensuring macroeconomic stability. In order to increase the budget projection justifiability, it is advisable to make institutional changes to its methodology. Based on the methioned above, the article reveals the essence and role of the budgetary projection in the system of financial and economic regulation of social processes in the context of improving the fiscal policy effectiveness aimed at macroeconomic stability maintenance; approaches to improving the budgetary projection methodology have been identified, and it has been determined that the soundness and feasibility of budgetary projection are the basis for effective fiscal policy. The predictability of budgetary criteria, budgetary architectonics contribute to improving the efficiency of transformations in the public finance system.

scholarly journals Group-Level Ranking-Based Hubness Analysis of Human Brain Connectome Reveals Significant Interhemispheric Asymmetry and Intraparcel Heterogeneities

2021 ◽  
Vol 15 ◽  
Author(s):  
Sahin Hanalioglu ◽  
Siyar Bahadir ◽  
Ilkay Isikay ◽  
Pinar Celtikci ◽  
Emrah Celtikci ◽  
...  
Keyword(s):  
Human Brain ◽  
Diffusion Imaging ◽  
Structural Connectivity ◽  
Population Level ◽  
Similar Distribution ◽  
Group Level ◽  
Imaging Data ◽  
Brain Connectome ◽  
Human Connectome Project ◽  
Intuitive Method

Objective: Graph theory applications are commonly used in connectomics research to better understand connectivity architecture and characterize its role in cognition, behavior and disease conditions. One of the numerous open questions in the field is how to represent inter-individual differences with graph theoretical methods to make inferences for the population. Here, we proposed and tested a simple intuitive method that is based on finding the correlation between the rank-ordering of nodes within each connectome with respect to a given metric to quantify the differences/similarities between different connectomes.Methods: We used the diffusion imaging data of the entire HCP-1065 dataset of the Human Connectome Project (HCP) (n = 1,065 subjects). A customized cortical subparcellation of HCP-MMP atlas (360 parcels) (yielding a total of 1,598 ROIs) was used to generate connectivity matrices. Six graph measures including degree, strength, coreness, betweenness, closeness, and an overall “hubness” measure combining all five were studied. Group-level ranking-based aggregation method (“measure-then-aggregate”) was used to investigate network properties on population level.Results: Measure-then-aggregate technique was shown to represent population better than commonly used aggregate-then-measure technique (overall rs: 0.7 vs 0.5). Hubness measure was shown to highly correlate with all five graph measures (rs: 0.88–0.99). Minimum sample size required for optimal representation of population was found to be 50 to 100 subjects. Network analysis revealed a widely distributed set of cortical hubs on both hemispheres. Although highly-connected hub clusters had similar distribution between two hemispheres, average ranking values of homologous parcels of two hemispheres were significantly different in 71% of all cortical parcels on group-level.Conclusion: In this study, we provided experimental evidence for the robustness, limits and applicability of a novel group-level ranking-based hubness analysis technique. Graph-based analysis of large HCP dataset using this new technique revealed striking hemispheric asymmetry and intraparcel heterogeneities in the structural connectivity of the human brain.

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.

scholarly journals Slow Cortical Waves via Cyclicity

2021 ◽  
Author(s):  
Ivan Abraham ◽  
Bahar Shahsavarani ◽  
Ben Zimmerman ◽  
Fatima Husain ◽  
yuliy baryshnikov
Keyword(s):  
Time Series ◽  
Dynamic Structure ◽  
Resting State Fmri ◽  
Brain Regions ◽  
Fine Grained ◽  
Human Connectome Project ◽  
Fmri Time Series ◽  
The Brain ◽  
Cortical Waves ◽  
Temporal Ordering

Fine-grained information about dynamic structure of cortical networks is crucial in unpacking brain function. Here,we introduced a novel analytical method to characterize the dynamic interaction between distant brain regions,based on cyclicity analysis, and applied it to data from the Human Connectome Project. Resting-state fMRI time series are aperiodic and, hence, lack a base frequency. Cyclicity analysis, which is time-reparametrization invariant, is effective in recovering dynamic temporal ordering of such time series along a circular trajectory without assuming any time scale. Our analysis detected the propagation of slow cortical waves across thebrain with consistent shifts in lead-lag relationships between specific brain regions. We also observed short bursts of strong temporal ordering that dominated overall lead-lag relationships between pairs of regions in the brain, which were modulated by tasks. Our results suggest the possible role played by slow waves of ordered information between brain regions that underlie emergent cognitive function.

scholarly journals Effective Degrees of Freedom of the Pearson’s Correlation Coefficient under Autocorrelation

2018 ◽  
Author(s):  
Soroosh Afyouni ◽  
Stephen M. Smith ◽  
Thomas E. Nichols
Keyword(s):  
Time Series ◽  
Correlation Coefficient ◽  
Degrees Of Freedom ◽  
Brain Regions ◽  
Resting State Functional Connectivity ◽  
Pearson’S Correlation ◽  
Individual Level ◽  
Human Connectome Project ◽  
Pearson's Correlation ◽  
The Impact

AbstractThe dependence between pairs of time series is commonly quantified by Pearson’s correlation. However, if the time series are themselves dependent (i.e. exhibit temporal autocorrelation), the effective degrees of freedom (EDF) are reduced, the standard error of the sample correlation coefficient is biased, and Fisher’s transformation fails to stabilise the variance. Since fMRI time series are notoriously autocorrelated, the issue of biased standard errors – before or after Fisher’s transformation – becomes vital in individual-level analysis of resting-state functional connectivity (rsFC) and must be addressed anytime a standardized Z-score is computed. We find that the severity of autocorrelation is highly dependent on spatial characteristics of brain regions, such as the size of regions of interest and the spatial location of those regions. We further show that the available EDF estimators make restrictive assumptions that are not supported by the data, resulting in biased rsFC inferences that lead to distorted topological descriptions of the connectome on the individual level. We propose a practical “xDF” method that accounts not only for distinct autocorrelation in each time series, but instantaneous and lagged cross-correlation. We find the xDF correction varies substantially over node pairs, indicating the limitations of global EDF corrections used previously. In addition to extensive synthetic and real data validations, we investigate the impact of this correction on rsFC measures in data from the Young Adult Human Connectome Project, showing that accounting for autocorrelation dramatically changes fundamental graph theoretical measures relative to no correction.

scholarly journals A Connectomic Atlas of the Human Cerebrum—Chapter 8: The Posterior Cingulate Cortex, Medial Parietal Lobe, and Parieto-Occipital Sulcus

2018 ◽  
Vol 15 (suppl_1) ◽  
pp. S350-S371 ◽  
Author(s):  
Cordell M Baker ◽  
Joshua D Burks ◽  
Robert G Briggs ◽  
Andrew K Conner ◽  
Chad A Glenn ◽  
...  
Keyword(s):  
Cerebral Cortex ◽  
Functional Connectivity ◽  
Functional Significance ◽  
Parietal Lobe ◽  
Structural Connectivity ◽  
Cingulate Cortex ◽  
Posterior Cingulate Cortex ◽  
Posterior Cingulate ◽  
Human Connectome Project

ABSTRACT In this supplement, we build on work previously published under the Human Connectome Project. Specifically, we seek to show a comprehensive anatomic atlas of the human cerebrum demonstrating all 180 distinct regions comprising the cerebral cortex. The location, functional connectivity, and structural connectivity of these regions are outlined, and where possible a discussion is included of the functional significance of these areas. In part 8, we specifically address regions relevant to the posterior cingulate cortex, medial parietal lobe, and the parieto-occipital sulcus.

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