scholarly journals Individual Topological Analysis of Synchronization-Based Brain Connectivity

2020 ◽  
Vol 10 (9) ◽  
pp. 3275
Author(s):  
Angela Lombardi ◽  
Nicola Amoroso ◽  
Domenico Diacono ◽  
Alfonso Monaco ◽  
Sabina Tangaro ◽  
...  
Keyword(s):  
Time Series ◽  
Functional Connectivity ◽  
Brain Connectivity ◽  
Topological Analysis ◽  
The Other ◽  
Modular Organization ◽  
Connectivity Matrix ◽  
Functional Coupling ◽  
Macro Scale ◽  
Fmri Time Series

Functional connectivity analysis aims at assessing the strength of functional coupling between the signal responses in distinct brain areas. Usually, functional magnetic resonance imaging (fMRI) time series connections are estimated through zero-lag correlation metrics that quantify the statistical similarity between pairs of regions or spectral measures that assess synchronization at a frequency band of interest. Here, we explored the application of a new metric to assess the functional synchronization in phase space between fMRI time series in a resting state. We applied a complete topological analysis to the resulting connectivity matrix to uncover both the macro-scale organization of the brain and detect the most important nodes. The synchronization metric is also compared with Pearson’s correlation coefficient and spectral coherence to highlight similarities and differences between the topologies of the three functional networks. We found that the individual topological organization of the resulting synchronization-based connectivity networks shows a finer modular organization than that identified with the other two metrics and a low overlap with the modular partitions of the other two networks suggesting that the derived topological information is not redundant and could be potentially integrated to provide a multi-scale description of functional connectivity.

scholarly journals A Novel Synchronization-Based Approach for Functional Connectivity Analysis

Complexity ◽  
2017 ◽  
Vol 2017 ◽  
pp. 1-12 ◽  
Author(s):  
Angela Lombardi ◽  
Sabina Tangaro ◽  
Roberto Bellotti ◽  
Alessandro Bertolino ◽  
Giuseppe Blasi ◽  
...  
Keyword(s):  
Time Series ◽  
Functional Connectivity ◽  
Functional Coupling ◽  
Healthy Population ◽  
Brain Organization ◽  
Functional Brain ◽  
Novel Approach ◽  
Functional Brain Networks ◽  
Fmri Time Series ◽  
Functional Brain Organization

Complex network analysis has become a gold standard to investigate functional connectivity in the human brain. Popular approaches for quantifying functional coupling between fMRI time series are linear zero-lag correlation methods; however, they might reveal only partial aspects of the functional links between brain areas. In this work, we propose a novel approach for assessing functional coupling between fMRI time series and constructing functional brain networks. A phase space framework is used to map couples of signals exploiting their cross recurrence plots (CRPs) to compare the trajectories of the interacting systems. A synchronization metric is extracted from the CRP to assess the coupling behavior of the time series. Since the functional communities of a healthy population are expected to be highly consistent for the same task, we defined functional networks of task-related fMRI data of a cohort of healthy subjects and applied a modularity algorithm in order to determine the community structures of the networks. The within-group similarity of communities is evaluated to verify whether such new metric is robust enough against noise. The synchronization metric is also compared with Pearson’s correlation coefficient and the detected communities seem to better reflect the functional brain organization during the specific task.

scholarly journals Thresholding Functional Connectivity Matrices to Recover the Topological Properties of Large-Scale Neuronal Networks

2021 ◽  
Vol 15 ◽  
Author(s):  
Alessio Boschi ◽  
Martina Brofiga ◽  
Paolo Massobrio
Keyword(s):  
Functional Connectivity ◽  
Large Scale ◽  
Neuronal Networks ◽  
Brain Connectivity ◽  
Structural Connectivity ◽  
Synthetic Data ◽  
Connectivity Matrix ◽  
Causal Connection ◽  
Topological Properties ◽  
Functional Connections

The identification of the organization principles on the basis of the brain connectivity can be performed in terms of structural (i.e., morphological), functional (i.e., statistical), or effective (i.e., causal) connectivity. If structural connectivity is based on the detection of the morphological (synaptically mediated) links among neurons, functional and effective relationships derive from the recording of the patterns of electrophysiological activity (e.g., spikes, local field potentials). Correlation or information theory-based algorithms are typical routes pursued to find statistical dependencies and to build a functional connectivity matrix. As long as the matrix collects the possible associations among the network nodes, each interaction between the neuron i and j is different from zero, even though there was no morphological, statistical or causal connection between them. Hence, it becomes essential to find and identify only the significant functional connections that are predictive of the structural ones. For this reason, a robust, fast, and automatized procedure should be implemented to discard the “noisy” connections. In this work, we present a Double Threshold (DDT) algorithm based on the definition of two statistical thresholds. The main goal is not to lose weak but significant links, whose arbitrary exclusion could generate functional networks with a too small number of connections and altered topological properties. The algorithm allows overcoming the limits of the simplest threshold-based methods in terms of precision and guaranteeing excellent computational performances compared to shuffling-based approaches. The presented DDT algorithm was compared with other methods proposed in the literature by using a benchmarking procedure based on synthetic data coming from the simulations of large-scale neuronal networks with different structural topologies.

scholarly journals Multivariate Brain Functional Connectivity Through Regularized Estimators

2020 ◽  
Vol 14 ◽  
Author(s):  
Raymond Salvador ◽  
Norma Verdolini ◽  
Beatriz Garcia-Ruiz ◽  
Esther Jiménez ◽  
Salvador Sarró ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Random Forest ◽  
Temporal Dynamics ◽  
Brain Connectivity ◽  
Brain Activity ◽  
The Other ◽  
Linear Functions ◽  
Other Hand ◽  
Age Related ◽  
Global Brain

Functional connectivity analyses are typically based on matrices containing bivariate measures of covariability, such as correlations. Although this has been a fruitful approach, it may not be the optimal strategy to fully explore the complex associations underlying brain activity. Here, we propose extending connectivity to multivariate functions relating to the temporal dynamics of a region with the rest of the brain. The main technical challenges of such an approach are multidimensionality and its associated risk of overfitting or even the non-uniqueness of model solutions. To minimize these risks, and as an alternative to the more common dimensionality reduction methods, we propose using two regularized multivariate connectivity models. On the one hand, simple linear functions of all brain nodes were fitted with ridge regression. On the other hand, a more flexible approach to avoid linearity and additivity assumptions was implemented through random forest regression. Similarities and differences between both methods and with simple averages of bivariate correlations (i.e., weighted global brain connectivity) were evaluated on a resting state sample of N = 173 healthy subjects. Results revealed distinct connectivity patterns from the two proposed methods, which were especially relevant in the age-related analyses where both ridge and random forest regressions showed significant patterns of age-related disconnection, almost completely absent from the much less sensitive global brain connectivity maps. On the other hand, the greater flexibility provided by the random forest algorithm allowed detecting sex-specific differences. The generic framework of multivariate connectivity implemented here may be easily extended to other types of regularized models.

scholarly journals Ridding fMRI data of motion-related influences: Removal of signals with distinct spatial and physical bases in multiecho data

2018 ◽  
Vol 115 (9) ◽  
pp. E2105-E2114 ◽  
Author(s):  
Jonathan D. Power ◽  
Mark Plitt ◽  
Stephen J. Gotts ◽  
Prantik Kundu ◽  
Valerie Voon ◽  
...  
Keyword(s):  
Time Series ◽  
Functional Connectivity ◽  
Blood Oxygen Level Dependent ◽  
Critical Element ◽  
Blood Oxygen Level ◽  
Brain Organization ◽  
Whole Brain ◽  
Decay Properties ◽  
Signal Decay ◽  
Fmri Time Series

“Functional connectivity” techniques are commonplace tools for studying brain organization. A critical element of these analyses is to distinguish variance due to neurobiological signals from variance due to nonneurobiological signals. Multiecho fMRI techniques are a promising means for making such distinctions based on signal decay properties. Here, we report that multiecho fMRI techniques enable excellent removal of certain kinds of artifactual variance, namely, spatially focal artifacts due to motion. By removing these artifacts, multiecho techniques reveal frequent, large-amplitude blood oxygen level-dependent (BOLD) signal changes present across all gray matter that are also linked to motion. These whole-brain BOLD signals could reflect widespread neural processes or other processes, such as alterations in blood partial pressure of carbon dioxide (pCO2) due to ventilation changes. By acquiring multiecho data while monitoring breathing, we demonstrate that whole-brain BOLD signals in the resting state are often caused by changes in breathing that co-occur with head motion. These widespread respiratory fMRI signals cannot be isolated from neurobiological signals by multiecho techniques because they occur via the same BOLD mechanism. Respiratory signals must therefore be removed by some other technique to isolate neurobiological covariance in fMRI time series. Several methods for removing global artifacts are demonstrated and compared, and were found to yield fMRI time series essentially free of motion-related influences. These results identify two kinds of motion-associated fMRI variance, with different physical mechanisms and spatial profiles, each of which strongly and differentially influences functional connectivity patterns. Distance-dependent patterns in covariance are nearly entirely attributable to non-BOLD artifacts.

scholarly journals Undirected graphs of frequency-dependent functional connectivity in whole brain networks

2005 ◽  
Vol 360 (1457) ◽  
pp. 937-946 ◽  
Author(s):  
Raymond Salvador ◽  
John Suckling ◽  
Christian Schwarzbauer ◽  
Ed Bullmore
Keyword(s):  
Time Series ◽  
Functional Connectivity ◽  
Brain Networks ◽  
Partial Coherence ◽  
Association Cortex ◽  
Undirected Graphs ◽  
Long Distance ◽  
Whole Brain ◽  
Low Frequencies ◽  
Fmri Time Series

We explored properties of whole brain networks based on multivariate spectral analysis of human functional magnetic resonance imaging (fMRI) time-series measured in 90 cortical and subcortical subregions in each of five healthy volunteers studied in the (no-task) resting state. We note that undirected graphs representing conditional independence between multivariate time-series can be more readily approached in the frequency domain than the time domain. Estimators of partial coherency and normalized partial mutual information ϕ , an integrated measure of partial coherence over an arbitrary frequency band, are applied. Using these tools, we replicate the prior observations that bilaterally homologous brain regions tend to be strongly connected and functional connectivity is generally greater at low frequencies [0.0004, 0.1518 Hz]. We also show that long-distance intrahemispheric connections between regions of prefrontal and parietal cortex were more salient at low frequencies than at frequencies greater than 0.3 Hz, whereas many local or short-distance connections, such as those comprising segregated dorsal and ventral paths in posterior cortex, were also represented in the graph of high-frequency connectivity. We conclude that the partial coherency spectrum between a pair of human brain regional fMRI time-series depends on the anatomical distance between regions: long-distance (greater than 7 cm) edges represent conditional dependence between bilaterally symmetric neocortical regions, and between regions of prefrontal and parietal association cortex in the same hemisphere, are predominantly subtended by low-frequency components.

scholarly journals New graph-theoretical-multimodal approach using temporal and structural correlations reveal disruption in the thalamo-cortical network in patients with schizophrenia

2018 ◽  
Author(s):  
Paolo Finotteli ◽  
Caroline Garcia Forlim ◽  
Paolo Dulio ◽  
Leonie Klock ◽  
Alessia Pini ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Brain Connectivity ◽  
Brain Networks ◽  
Structural Connectivity ◽  
Network Models ◽  
Brain Regions ◽  
Cortical Network ◽  
Future Research ◽  
Connectivity Matrix

Schizophrenia has been understood as a network disease with altered functional and structural connectivity in multiple brain networks compatible to the extremely broad spectrum of psychopathological, cognitive and behavioral symptoms in this disorder. When building brain networks, functional and structural networks are typically modelled independently: functional network models are based on temporal correlations among brain regions, whereas structural network models are based on anatomical characteristics. Combining both features may give rise to more realistic and reliable models of brain networks. In this study, we applied a new flexible graph-theoretical-multimodal model called FD (F, the functional connectivity matrix, and D, the structural matrix) to construct brain networks combining functional, structural and topological information of MRI measurements (structural and resting state imaging) to patients with schizophrenia (N=35) and matched healthy individuals (N=41). As a reference condition, the traditional pure functional connectivity (pFC) analysis was carried out. By using the FD model, we found disrupted connectivity in the thalamo-cortical network in schizophrenic patients, whereas the pFC model failed to extract group differences after multiple comparison correction. We interpret this observation as evidence that the FD model is superior to conventional connectivity analysis, by stressing relevant features of the whole brain connectivity including functional, structural and topological signatures. The FD model can be used in future research to model subtle alterations of functional and structural connectivity resulting in pronounced clinical syndromes and major psychiatric disorders. Lastly, FD is not limited to the analysis of resting state fMRI, and can be applied to EEG, MEG etc.

scholarly journals Disrupted modular organization of primary sensory brain areas in schizophrenics

2017 ◽  
Author(s):  
Cécile Bordier ◽  
Carlo Nicolini ◽  
Angelo Bifone
Keyword(s):  
Functional Connectivity ◽  
Speech Processing ◽  
Resting State ◽  
Brain Connectivity ◽  
Neural Substrates ◽  
Modular Organization ◽  
Resolution Limit ◽  
Resting State Functional Connectivity ◽  
Cross Sectional ◽  
Higher Cognitive Functions

AbstractAbnormal brain resting-state functional connectivity has been consistently observed in patients affected by Schizophrenia (SCZ) using functional MRI and other neuroimaging methods. Graph theoretical methods provide a framework to investigate these defective functional interactions and their effects on the modular organization of brain connectivity networks. A few studies have shown abnormal distribution of connectivity within and between functional modules, an indication of imbalanced functional segregation ad integration in SCZ patients. However, no major alterations in the modular structure of functional connectivity networks in patients have been reported, and unambiguous identification of the neural substrates involved remains elusive. Recently, it has been demonstrated that current modularity analysis methods suffer from a fundamental and severe resolution limit, as they fail to detect features that are smaller than a scale determined by the size of the entire connectivity network. This resolution limit is likely to have hampered the ability to resolve differences between patients and controls in previous cross-sectional studies. Here, we apply a novel, resolution limit-free approach to study the modular organization of resting state functional connectivity networks in a large cohort of SCZ patients, and in matched healthy controls. Leveraging these important methodological advances, we find new evidence of substantial fragmentation and reorganization involving primary sensory, auditory and visual areas in SCZ patients. Conversely, frontal and prefrontal areas, typically associated with higher cognitive functions, appear to be largely unaffected, with changes selectively involving language and speech processing areas. Our findings provide support to the hypothesis that cognitive dysfunction in SCZ may arise from deficits occurring already at early stages of sensory processing.

scholarly journals Morphometric and Functional Brain Connectivity Differentiates Chess Masters from Amateur Players

2020 ◽  
Author(s):  
Harish RaviPrakash ◽  
Syed Muhammad Anwar ◽  
Nadia M. Biassou ◽  
Ulas Bagci
Keyword(s):  
Magnetic Resonance ◽  
Functional Connectivity ◽  
Spatial Reasoning ◽  
Medical Condition ◽  
Brain Connectivity ◽  
Magnetic Resonance Images ◽  
Support Vector ◽  
Connectivity Matrix ◽  
Chess Players ◽  
The Brain

ABSTRACTA common task in brain image analysis includes diagnosis of a certain medical condition wherein groups of healthy controls and diseased subjects are analyzed and compared. On the other hand, for two groups of healthy participants with different proficiency in a certain skill, a distinctive analysis of the brain function remains a challenging problem. In this study, we develop new computational tools to explore the functional and anatomical differences that could exist between the brain of healthy individuals identified on the basis of different levels of task experience/proficiency. Towards this end, we look at a dataset of amateur and professional chess players, where we utilize resting-state functional magnetic resonance images to generate functional connectivity (FC) information. In addition, we utilize T1-weighted magnetic resonance imaging to estimate morphometric connectivity (MC) information. We combine functional and anatomical features into a new connectivity matrix, which we term as the functional morphometric similarity connectome (FMSC). Since, both the FC and MC information is susceptible to redundancy, the size of this information is reduced using statistical feature selection. We employ off-the-shelf machine learning classifier, support vector machine, for both single- and multi-modality classifications. From our experiments, we establish that the saliency and ventral attention network of the brain is functionally and anatomically different between two groups of healthy subjects (chess players). We argue that, since chess involves many aspects of higher order cognition such as systematic thinking and spatial reasoning and the identified network is task-positive to cognition tasks requiring a response, our results are valid and supporting the feasibility of the proposed computational pipeline. Moreover, we quantitatively validate an existing neuroscience hypothesis that learning a certain skill could cause a change in the brain (functional connectivity and anatomy) and this can be tested via our novel FMSC algorithm.

scholarly journals Topographic Shifts in Functional Connectivity and Reduced Lateralization in 16p11.2 Deletion Carriers: A Genetics-First Approach to Understanding Autism

2020 ◽  
Author(s):  
ABID Y QURESHI ◽  
Jared A. Nielsen ◽  
Jorge Sepulcre
Keyword(s):  
Functional Connectivity ◽  
Large Scale ◽  
Brain Connectivity ◽  
Brain Regions ◽  
Language Ability ◽  
Hemispheric Lateralization ◽  
Functional Coupling ◽  
Resting State Functional Connectivity ◽  
Hierarchical Processing ◽  
Large Scale Networks

Abstract Background: The study of autism has been confounded by genetic and etiologic heterogeneity. The current study utilized a genetics-first approach to investigate the underlying neurobiology of autism by studying individuals with copy number variation at 16p11.2. Our aim was to investigate the prevailing theories of brain connectivity in autism – specifically, in regard to (1) distributed brain networks, (2) local and distant connectivity, and (3) functional lateralization. Methods: We analyzed resting-state functional connectivity MRI acquired in 26 carriers of a 16p11.2 deletion and 42 age-matched control participants. We also compared the functional connectivity metrics with measures of language ability, IQ, and social behavior.Results: We do not find widespread disruption of canonical large-scale networks in the deletion carriers. Nor do we find quantitative differences in the degree of local and distant connections. Instead, we discover unique connections in 16p11.2 deletion carriers that are not present in the control participants. Specifically, functional coupling between auditory cortex and regions of the default network is present only in the deletion carriers. In addition to the topographic shifts in functional connectivity, we observe reduced right hemispheric lateralization in the deletion carriers and less left hemispheric lateralization in individuals with poorer language ability.Conclusions: Links or connections between primary sensory areas and higher-order association areas violate fundamental large-scale circuit properties by functionally connecting brain regions specialized in local (hierarchical) processing with those that specialize in distant (parallel) processing. Aberrant hemispheric lateralization and connections between auditory and default networks may underlie difficulties with language and social interactions.

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