Biology subjects

Deco, G.

Publications and source records attributed to Deco, G..

27 records · Page 2Linked to original sources

bioRxiv2017

Non-parametric test for connectivity detection in multivariate autoregressive networks and application to multiunit activity data

Directed connectivity inference has become a cornerstone in neuroscience following the recent progress in neuroimaging and elctrophysiological techniques to characterize anatomical and functional networks. This paper focuses on the detection of existing connections from the observed activity in networks of 50 to 150 nodes with linear feedback in discrete time. Through the variation of multiple network parameters, our numerical results indicate that directed connections - in the time domain - are more accurately estimated based on the coefficients obtained from multivariate autoregressive (MVAR) than Granger causality analysis, which is based on the error residuals of the same MVAR linear regression. Based on these findings, we propose a non-parametric significance test for connectivity detection, which achieves a good control of false positives (type 1 error) and is robust to various network topologies. When generating surrogate distributions, we compare the effects of circular shifts, random permutations and phase randomization of the observed time series, each breaking down covariances in a specific manner: the MVAR estimates from those shuffled covariances build a null-hypothesis distribution for each connection, from which the original connectivity estimate can be compared. We apply our method to multiunit activity data recorded from Utah electrode arrays in monkey and examine the detected interactions between 25 channels for a proof of concept. The results unravel a non-trivial underlying connectivity structure, which differentiates the effect of incoming and outgoing connections.

neuroscience↗

bioRxiv2016

Robust extraction of spatio-temporal patterns from resting state fMRI

It is well-established that patterns of functional connectivity (FC) - measures of correlated activity between pairs of voxels or regions observed in the human brain using neuroimaging - are robustly expressed in spontaneous activity during rest. These patterns are not static, but exhibit complex spatio-temporal dynamics. Over the last years, a multitude of methods have been proposed to reveal these dynamics on the level of the whole brain. One finding is that the brain transitions through different FC configurations over time, and substantial effort has been put into characterizing these configurations. However, the dynamics governing these transitions are more elusive, specifically, the contribution of stationary vs. non-stationary dynamics is an active field of inquiry. In this study, we use a whole-brain approach, considering FC dynamics between 66 ROIs covering the entire cortex. We combine an innovative dimensionality reduction technique, tensor decomposition, with a mean field model which possesses stationary dynamics. It has been shown to explain resting state FC averaged over time and multiple subjects, however, this average FC summarizes the spatial distribution of correlations while hiding their temporal dynamics. First, we apply tensor decomposition to resting state scans from 24 healthy controls in order to characterize spatio-temporal dynamics present in the data. We simultaneously utilize temporal and spatial information by creating tensors that are subsequently decomposed into sets of brain regions (\"communities\") that share similar temporal dynamics, and their associated time courses. The tensors contain pairwise FC computed inside of overlapping sliding windows. Communities are discovered by clustering features pooled from all subjects, thereby ensuring that they generalize. We find that, on the group level, the data give rise to four distinct communities that resemble known resting state networks (RSNs): default mode network, visual network, control networks, and somatomotor network. Second, we simulate data with our stationary mean field model whose nodes are connected according to results from DTI and fiber tracking. In this model, all spatio-temporal structure is due to noisy fluctuations around the average FC. We analyze the simulated data in the same way as the empirical data in order to determine whether stationary dynamics can explain the emergence of distinct FC patterns (RSNs) which have their own time courses. We find that this is the case for all four networks using the spatio-temporal information revealed by tensor decomposition if nodes in the simulation are connected according to model-based effective connectivity. Furthermore, we find that these results require only a small part of the FC values, namely the highest values that occur across time and ROI pair. Our findings show that stationary dynamics can account for the emergence of RSNs. We provide an innovative method that does not make strong assumptions about the underlying data and is generally applicable to resting state or task data from different subject populations.

neuroscience↗

bioRxiv2016

The dynamics of human cognition: increasing global integration coupled with decreasing segregation found using intracortical EEG

Cognitive processing requires the ability to flexibly integrate and process information across large brain networks. More information is needed on how brain networks dynamically reorganize to allow such broad communication across many different brain regions in order to integrate the necessary information. Here, we use intracranial EEG to record neural activity from 12 epileptic patients while they perform three cognitive tasks in order to study how the functional connectivity changes to facilitate communication across the underlying network spanning many different brain regions. At the topological level, this facilitation is characterized by measures of integration and segregation. Across all patients, we found significant increases in integration and decreases in segregation during cognitive processing, especially in the gamma band (50-90 Hz). Accordingly, we also found significantly higher level of global synchronization and functional connectivity during the execution of the cognitive task, again particularly in the gamma band. More importantly, we demonstrate here for the first time that the modulations at the level of functional connectivity facilitating communication across the network were not caused by changes in the level of the underlying oscillations but caused by a rearrangement of the mutual synchronisation between the different nodes as proposed by the \"Communication Through Coherence\" Theory.

neuroscience↗

bioRxiv2016

Resting-state fMRI correlations: from link-wiseunreliability to whole brain stability

The functional architecture of spontaneous BOLD fluctuations has been characterized in detail by numerous studies, demonstrating its potential relevance as a biomarker. However, the systematic investigation of its consistency is still in its infancy. Here, we analyze both the within- and between-subject variability as well as the test-retest reliability of resting-state functional connectivity (FC) estimates in a unique data set comprising multiple fMRI scans (42) from 5 subjects, and 50 single scans from 50 subjects. To this aim we adopted a statistical framework enabling us to disentangle the contribution of different sources of variability and their dependence on scan duration, and showed that the low reliability of single links can be largely improved using multiple scans per subject. Moreover, we show that practically all observed inter-region variability (at the link-level) is not significant and due to the statistical uncertainty of the estimator itself rather than to genuine variability among areas. Finally, we use the proposed statistical framework to demonstrate that, despite the poor consistency of single links, the information carried by the whole-brain spontaneous correlation structure is indeed robust, and can in fact be used as a functional fingerprint.

neuroscience↗

bioRxiv2016

Bridging multiple scales in the human brain using computational modelling

Brain dynamics span multiple spatial and temporal scales, from fast spiking neurons to slow fluctuations over distributed areas. No single experimental method links data across scales. Here, we bridge this gap using The Virtual Brain connectome-based modelling platform to integrate multimodal data with biophysical models and support neurophysiological inference. Simulated cell populations were linked with subject-specific white-matter connectivity estimates and driven by electroencephalography-derived electric source activity. The models were fit to subject-specific resting-state functional magnetic resonance imaging data, and overfitting was excluded using 5-fold cross-validation. Further evaluation of the models show how balancing excitation with feedback inhibition generates an inverse relationship between -rhythms and population firing on a faster time scale and resting-state network oscillations on a slower time scale. Lastly, large-scale interactions in the model lead to the emergence of scale-free power-law spectra. Our novel findings underscore the integrative role for computational modelling to complement empirical studies.

neuroscience↗

bioRxiv2016

Exploring Anti-correlated Resting State BOLD Signals Through Dynamic Functional Connectivity and Whole-brain Computational Modeling

Resting-state functional magnetic resonance imaging and diffusion weight imaging became a conventional tool to study brain connectivity in healthy and diseased individuals. However, both techniques provide indirect measures of brain connectivity leading to controversies on their interpretation. Among these controversies, interpretation of anti-correlated functional connections and global average signal is a major challenge for the field. In this paper, we used dynamic functional connectivity to calculate the probability of anti-correlations between brain regions. The brain regions forming task-positive and task-negative networks showed high anti-correlation probabilities. The fluctuations in anti-correlation probabilities were significantly correlated with those in global average signal and functional connectivity. We investigated the mechanisms behind these fluctuations using whole-brain computational modeling approach. We found that the underlying effective connectivity and intrinsic noise reflect the static spatiotemporal patterns, whereas the hemodynamic response function is the key factor defining the fluctuations in functional connectivity and anti-correlations. Furthermore, we illustrated the clinical implications of these findings on a group of bipolar disorder patients suffering a depressive relapse (BPD).

neuroscience↗

bioRxiv2016

Single or Multi-Frequency Generators in On-going Brain Activity: a Mechanistic Whole-Brain Model following empirical MEG evidences

During rest, envelopes of band-limited on-going MEG signals co-vary across the brain in consistent patterns, which have been related to resting-state networks measured with fMRI. To investigate the genesis of such envelope correlations, we consider a whole-brain network model assuming two distinct fundamental scenarios: one where each brain area generates oscillations in a single frequency, and a novel one where each brain area can generate oscillations in multiple frequency bands. The models share, as a common generator of damped oscillations, the normal form of a supercritical Hopf bifurcation operating at the critical border between the steady state and the oscillatory regime. The envelopes of the simulated signals are compared with empirical MEG data using new methods to analyse the envelope dynamics in terms of their phase coherence and stability across the spectrum of carrier frequencies.\n\nConsidering the whole-brain model with a single frequency generator in each brain area, we obtain the best fit with the empirical MEG data when the fundamental frequency is tuned at 12Hz. However, when multiple frequency generators are placed at each local brain area, we obtain an improved fit of the spatio-temporal structure of on-going MEG data across all frequency bands. Our results indicate that the brain is likely to operate on multiple frequency channels during rest, introducing a novel dimension for future models of large-scale brain activity.

neuroscience↗

bioRxiv2016

Uncovering the underlying mechanisms and whole-brain dynamics of therapeutic deep brain stimulation for Parkinson’s disease

Deep brain stimulation (DBS) for Parkinsons disease is a highly effective treatment in controlling otherwise debilitating symptoms yet the underlying brain mechanisms are currently not well understood. We used whole-brain computational modeling to disclose the effects of DBS ON and OFF during collection of resting state fMRI in ten Parkinsons Disease patients. Specifically, we explored the local and global impact of DBS in creating asynchronous, stable or critical oscillatory conditions using a supercritical bifurcation model. We found that DBS shifts the global brain dynamics of patients nearer to that of healthy people by significantly changing the bifurcation parameters in brain regions implicated in Parkinsons Disease. We also found higher communicability and coherence brain measures during DBS ON compared to DBS OFF. Finally, by modeling stimulation we identified possible novel DBS targets. These results offer important insights into the underlying effects of DBS, which may in time offer a route to more efficacious treatments.

neuroscience↗

bioRxiv2016

Degenerate time-dependent network dynamics anticipate seizures in human epileptic brain

Epileptic seizures are known to follow specific changes in brain dynamics. While some algorithms can nowadays robustly detect these changes, a clear understanding of the mechanism by which these alterations occur and generate seizures is still lacking. Here, we provide cross-validated evidence that such changes are initiated by an alteration of physiological network state dynamics. Specifically, our analysis of long intracranial EEG recordings from a group of 10 patients identifies a critical phase of a few hours in which time-dependent network states become less variable (\"degenerate\") and is followed by a global functional connectivity reduction before seizure onset. This critical phase is characterized by an abnormal occurrence of highly correlated network instances and is shown to particularly affect the activity of resection regions in patients with validated postsurgical outcome. Our approach characterizes pre-seizure networks dynamics as a cascade of two sequential events providing new insights into seizure prediction and control.

neuroscience↗