Exploring the network dynamics underlying brain activity during rest. by Cabral, J, Kringelbach, M, Deco, G

Exploring the network dynamics underlying brain activity during rest by Cabral, J, Kringelbach, M, Deco, G

Exploring the network dynamics underlying brain activity during rest by Cabral, J, Kringelbach, M, Deco, G

Whole-brain spatiotemporal characteristics of functional connectivity in transitions between wakefulness and sleep by Stevner, A

Subjects:

Connectome-harmonic decomposition of human brain activity reveals dynamical repertoire re-organization under LSD by Atasoy, S, Roseman, L, Kaelen, M, Kringelbach, M, Deco, G, Carhart-Harris, R

Using magnetoencephalography to investigate brain activity during high frequency deep brain stimulation in a cluster headache patient. by Ray, N, Kringelbach, M, Jenkinson, N, Owen, S, Davies, P, Wang, S, De Pennington, N, Hansen, P, Stein, J, Aziz, T

“…RESULTS: We were able to measure brain activity successfully both during low and high frequency stimulation. …”

MEG can map short and long-term changes in brain activity following deep brain stimulation for chronic pain. by Mohseni, H, Smith, P, Parsons, C, Young, K, Hyam, J, Stein, J, Stein, A, Green, A, Aziz, T, Kringelbach, M

“…We demonstrate that a novel method, null-beamforming, can be used to localise accurately brain activity despite the artefacts caused by the presence of DBS electrodes and stimulus pulses. …”

Single or multi-frequency generators in on-going brain activity: a mechanistic whole-brain model of empirical MEG data by Deco, G, Cabral, J, Woolrich, M, Stevner, A, Van Hartevelt, T, Kringelbach, M

“…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.</p>…”

Insights into brain architectures from the homological scaffolds of functional connectivity networks by Lord, L, Expert, P, Fernandes, H, Petri, G, Van Hartevelt, T, Vaccarino, F, Deco, G, Turkheimer, F, Kringelbach, M

“…By definition, any graph metric will be defined upon this dyadic representation of the brain activity. It is however unclear to what extent these dyadic relationships can capture the brain’s complex functional architecture and the encoding of information in distributed networks. …”

Rethinking segregation and integration: contributions of whole-brain modelling by Deco, G, Tononi, G, Boly, M, Kringelbach, M

“…Experiments in which the consequences of selective inputs on brain activity are controlled and traced with great precision could provide such information. …”

A non-Gaussian LCMV beamformer for MEG source reconstruction by Mohseni, H, Kringelbach, M, Woolrich, M, Aziz, T, Smith, P

“…We present a new general method for non-Gaussian source estimation of stationary signals for localising brain activity in the MEG data. By providing a Bayesian formulation for linearly constraint minimum variance (LCMV) beamformer, we extend this approach and show that how the source probability density function (pdf), which is not necessarily Gaussian, can be estimated. …”

A Non-Gaussian LCMV beamformer for MEG Source Reconstruction by Mohseni, H, Kringelbach, M, Woolrich, M, Aziz, T, Smith, P, IEEE

“…We present a new general method for non-Gaussian source estimation of stationary signals for localising brain activity in the MEG data. By providing a Bayesian formulation for linearly constraint minimum variance (LCMV) beamformer, we extend this approach and show that how the source probability density function (pdf), which is not necessarily Gaussian, can be estimated. …”

Activation of the human orbitofrontal cortex to a liquid food stimulus is correlated with its subjective pleasantness. by Kringelbach, M, O'Doherty, J, Rolls, E, Andrews, C

“…Single-neuron recording studies in non-human primates indicate that orbitofrontal cortex neurons represent the reward value of the sight, smell and taste of food, and even changes in the relative reward value, but provide no direct evidence on brain activity that is correlated with subjective reports of the pleasantness of food. …”

Non-gaussian probabilistic MEG source localisation based on kernel density estimation by Mohseni, H, Baker, A, Probert-Smith, P, Kringelbach, M, Woolrich, M, Aziz, T

“…In this paper, we present a new general method for non-Gaussian source estimation of stationary signals for localising brain activity from MEG data. By providing a Bayesian formulation for MEG source localisation, we show that the source probability density function (pdf), which is not necessarily Gaussian, can be estimated using multivariate kernel density estimators. …”

Modeling the outcome of structural disconnection on resting-state functional connectivity. by Cabral, J, Hugues, E, Kringelbach, M, Deco, G

“…Theoretical results indicate that most disconnection-related neuropathologies should induce the same qualitative changes in resting-state brain activity.…”

Non-Gaussian probabilistic MEG source localisation based on kernel density estimation. by Mohseni, H, Kringelbach, M, Woolrich, M, Baker, A, Aziz, T, Probert-Smith, P

“…In this paper, we present a new general method for non-Gaussian source estimation of stationary signals for localising brain activity from MEG data. By providing a Bayesian formulation for MEG source localisation, we show that the source probability density function (pdf), which is not necessarily Gaussian, can be estimated using multivariate kernel density estimators. …”

Increased stability and breakdown of brain effective connectivity during slow-wave sleep: mechanistic insights from whole-brain computational modelling by Jobst, B, Hindriks, R, Laufs, H, Tagliazucchi, E, Hahn, G, Ponce-Alvarez, A, Stevner, A, Kringelbach, M, Deco, G

“…Recent research has found that the human sleep cycle is characterised by changes in spatiotemporal patterns of brain activity. Yet, we are still missing a mechanistic explanation of the local neuronal dynamics underlying these changes. …”

How structure sculpts function: Unveiling the contribution of anatomical connectivity to the brain’s spontaneous correlation structure by Bettinardi, R, Deco, G, Karlaftis, V, van Hartevelt, T, Fernandes, H, Kourtzi, Z, Kringelbach, M, Zamora-López, G

“…Intrinsic brain activity is characterized by highly organized co-activations between different regions, forming clustered spatial patterns referred to as resting-state networks. …”

Altered paralimbic interaction in behavioral addiction. by Rømer Thomsen, K, Joensson, M, Lou, H, Møller, A, Gross, J, Kringelbach, M, Changeux, J

“…In addition, previous stimulant abuse had a marked effect on the amplitude of oscillatory brain activity in the ACC and PCC, suggesting long-term deleterious effects of repeated dopaminergic drug exposure. …”

Structural connectivity in schizophrenia and its impact on the dynamics of spontaneous functional networks. by Cabral, J, Fernandes, H, Van Hartevelt, T, James, A, Kringelbach, M, Deco, G

“…In this work, we used a computational model of spontaneous large-scale brain activity to explore the role of the structural connectivity in the large-scale dynamics of the brain in health and schizophrenia. …”