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 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 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 Multimodal Classification of Mild Traumatic Brain Injury Reveals Local Coupling Between Structural and Functional Connectomes

2021 ◽  
Author(s):  
Ajay Peddada ◽  
Kevin Holly ◽  
Tejaswi D Sudhakar ◽  
Christina Ledbetter ◽  
Christopher E. Talbot ◽  
...  
Keyword(s):  
White Matter ◽  
Functional Connectivity ◽  
Temporal Lobe ◽  
Resting State ◽  
Classification Accuracy ◽  
Structural Connectivity ◽  
Brain Regions ◽  
Group Differences ◽  
Functional Connections ◽  
White Matter Tractography

Background: Following mild traumatic brain injury (mTBI) compromised white matter structural integrity can result in alterations in functional connectivity of large-scale brain networks and may manifest in functional deficit including cognitive dysfunction . Advanced magnetic resonance neuroimaging techniques, specifically diffusion tensor imaging (DTI) and resting state functional magnetic resonance imaging (rs-fMRI), have demonstrated an increased sensitivity for detecting microstructural changes associated with mTBI. Identification of novel imaging biomarkers can facilitate early detection of these changes for effective treatment. In this study, we hypothesize that feature selection combining both structural and functional connectivity increases classification accuracy. Methods: 16 subjects with mTBI and 20 healthy controls underwent both DTI and resting state functional imaging. Structural connectivity matrices were generated from white matter tractography from DTI sequences. Functional connectivity was measured through pairwise correlations of rs-fMRI between brain regions. Features from both DTI and rs-fMRI were selected by identifying five brain regions with the largest group differences and were used to classify the generated functional and structural connectivity matrices, respectively. Classification was performed using linear support vector machines and validated with leave-one-out cross validation. Results: Group comparisons revealed increased functional connectivity in the temporal lobe and cerebellum as well as decreased structural connectivity in the temporal lobe. After training on structural connections only, a maximum classification accuracy of 78% was achieved when structural connections were selected based on their corresponding functional connectivity group differences. After training on functional connections only, a maximum classification accuracy of 69% was achieved when functional connections were selected based on their structural connectivity group differences. After training on both structural and functional connections, a maximum classification accuracy of 69% was achieved when connections were selected based on their structural connectivity. Conclusions: Our multimodal approach to ROI selection achieves at highest, a classification accuracy of 78%. Our results also implicate the temporal lobe in the pathophysiology of mTBI. Our findings suggest that white matter tractography can serve as a robust biomarker for mTBI when used in tandem with resting state functional connectivity.

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 Independent contributions of structural and functional connectivity: evidence from a stroke model

2021 ◽  
pp. 1-45
Author(s):  
Lynsey M Keator ◽  
Grigori Yourganov ◽  
Alexandra Basilakos ◽  
Argye E Hillis ◽  
Gregory Hickok ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Left Hemisphere ◽  
Language Impairment ◽  
Diffusion Tensor ◽  
Structural Connectivity ◽  
Ventral Stream ◽  
Speech Comprehension ◽  
Resting State Functional Connectivity ◽  
Stream Network ◽  
Functional Connections

Abstract Altered functional connectivity is related to severity of language impairment in poststroke aphasia. However, it is not clear whether this finding specifically reflects loss of functional coherence, or more generally, is related to decreased structural connectivity due to cortical necrosis. The aim of the current study was to investigate this issue by factoring out structural connectivity from functional connectivity measures and then relating the residual data to language performance post-stroke. Ninety-seven participants with a history of stroke were assessed using language impairment measures (Auditory Verbal Comprehension and Spontaneous Speech scores from the Western Aphasia Battery-Revised) and MRI (structural, diffusion tensor imaging, and resting state functional connectivity). We analyzed the association between functional connectivity and language and controlled for multiple potential neuroanatomical confounders, namely structural connectivity. We identified functional connections within the left-hemisphere ventral stream where decreased functional connectivity, independent of structural connectivity, was associated with speech comprehension impairment. These connections exist in frontotemporal andtemporoparietal regions. Our results suggest poor speech comprehension in aphasia is at least partially caused by loss of cortical synchrony in a left hemisphere ventral stream network and is not only reflective of localized necrosis or structural connectivity.

scholarly journals Benchmarking functional connectivity by the structure and geometry of the human brain

2021 ◽  
Author(s):  
Zhen-Qi Liu ◽  
Richard F. Betzel ◽  
Bratislav Misic
Keyword(s):  
Functional Connectivity ◽  
Structural Connectivity ◽  
Brain Regions ◽  
Underlying Structure ◽  
Functional Connections ◽  
Co Activation ◽  
Natural Frame ◽  
Structural Connections ◽  
Electrical Impulses ◽  
The Brain

The brain’s structural connectivity supports the propagation of electrical impulses, manifesting as patterns of co-activation, termed functional connectivity. Functional connectivity emerges from the underlying sparse structural connections, particularly through poly-synaptic communication. As a result, functional connections between brain regions without direct structural links are numerous, but their organization is not completely understood. Here we investigate the organization of functional connections without direct structural links. We develop a simple, data-driven method to benchmark functional connections with respect to their underlying structural and geometric embedding. We then use this method to re-weigh and re-express functional connectivity. We find evidence of unexpectedly strong functional connectivity within the canonical intrinsic networks of the brain. We also find unexpectedly strong functional connectivity at the apex of the unimodal-transmodal hierarchy. Our results suggest that both phenomena – functional modules and functional hierarchies – emerge from functional interactions that transcend the underlying structure and geometry. These findings also potentially explain recent reports that structural and functional connectivity gradually diverge in transmodal cortex. Collectively, we show how structural connectivity and geometry can be used as a natural frame of reference with which to study functional connectivity patterns in the brain.

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 Regional abnormality of functional connectivity is associated with clinical manifestations in individuals with intractable focal epilepsy

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Yasuo Nakai ◽  
Hiroki Nishibayashi ◽  
Tomohiro Donishi ◽  
Masaki Terada ◽  
Naoyuki Nakao ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Focal Epilepsy ◽  
Clinical Manifestations ◽  
Eigenvector Centrality ◽  
Functional Connections ◽  
Duration Of Illness ◽  
Healthy Control ◽  
Matched Data ◽  
Clinical Conditions ◽  
Age And Sex

AbstractWe explored regional functional connectivity alterations in intractable focal epilepsy brains using resting-state functional MRI. Distributions of the network parameters (corresponding to degree and eigenvector centrality) measured at each brain region for all 25 patients were significantly different from age- and sex-matched control data that were estimated by a healthy control dataset (n = 582, 18–84 years old). The number of abnormal regions whose parameters exceeded the mean + 2 SD of age- and sex-matched data for each patient were associated with various clinical parameters such as the duration of illness and seizure severity. Furthermore, abnormal regions for each patient tended to have functional connections with each other (mean ± SD = 58.6 ± 20.2%), the magnitude of which was negatively related to the quality of life. The abnormal regions distributed within the default mode network with significantly higher probability (p < 0.05) in 7 of 25 patients. We consider that the detection of abnormal regions by functional connectivity analysis using a large number of control datasets is useful for the numerical assessment of each patient’s clinical conditions, although further study is necessary to elucidate etiology-specific abnormalities.

scholarly journals An Extended Time Series Algorithm for Modal Identification from Nonstationary Ambient Response Data Only

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Chang-Sheng Lin ◽  
Dar-Yun Chiang ◽  
Tse-Chuan Tseng
Keyword(s):  
Time Series ◽  
Arma Model ◽  
Identification Problem ◽  
Nonstationary Time Series ◽  
Modal Identification ◽  
Ambient Vibration ◽  
Time Varying ◽  
Varying Parameters ◽  
Response Data ◽  
Time Varying Parameters

Modal Identification is considered from response data of structural systems under nonstationary ambient vibration. The conventional autoregressive moving average (ARMA) algorithm is applicable to perform modal identification, however, only for stationary-process vibration. The ergodicity postulate which has been conventionally employed for stationary processes is no longer valid in the case of nonstationary analysis. The objective of this paper is therefore to develop modal-identification techniques based on the nonstationary time series for linear systems subjected to nonstationary ambient excitation. Nonstationary ARMA model with time-varying parameters is considered because of its capability of resolving general nonstationary problems. The parameters of moving averaging (MA) model in the nonstationary time-series algorithm are treated as functions of time and may be represented by a linear combination of base functions and therefore can be used to solve the identification problem of time-varying parameters. Numerical simulations confirm the validity of the proposed modal-identification method from nonstationary ambient response data.

scholarly journals Detecting Determinism in Time Series with Complex Networks Constructed Using a Compression Algorithm

2018 ◽  
Vol 28 (13) ◽  
pp. 1850165
Author(s):  
Débora C. Corrêa ◽  
David M. Walker ◽  
Michael Small
Keyword(s):  
Time Series ◽  
Complex Networks ◽  
Information Content ◽  
Compression Algorithm ◽  
Connectivity Structure ◽  
Cycle Basis ◽  
Minimal Cycle ◽  
Time Irreversibility ◽  
Basis Structure

The properties of complex networks derived from applying a compression algorithm to time series subject to symbolic ordinal-based encoding is explored. The information content of compression codewords can be used to detect forbidden symbolic patterns indicative of nonlinear determinism. The connectivity structure of ordinal-based compression networks summarized by their minimal cycle basis structure can also be used in tests for nonlinear determinism, in particular, detection of time irreversibility in a signal.

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