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Regular version of the site

MEG group

The MEG Lab focuses on: 

1) Investigation of cognitive, perceptual and motor brain functions in healthy humans

2) The identification of neurophysiological biomarkers in clinical populations. 

The human brain constantly receives and integrates multimodal sensory information, which is  assimilated at different levels of abstraction, determining our interpretation of reality and contributing to our decisions and to our actions. Bottom-up integration of sensory information and top-down modulation due to attentional shift and learning concur and cooperate to shape our performance. In the study of cognitive functions, we target neural processes conveying the integration of sensory information  to model our expectation. In particular, we target the dynamical interaction among different brain areas in the domains of working memory, sensorimotor learning and decision making. 

Epilepsy is a widespread brain disease with marked neurophysiological signatures, either during the occurrence of ictal events (seizures) as well as during interictal activity. In collaboration with local clinical facilities, we invite epilepsy patients to undergo MEG recordings. Based on these data, we develop new methodological approaches to unveil the structure of the epileptic network. Our investigation is based on MEG recordings and on the combination of MEG with scalp EEG, intracranial EEG, EMG, current stimulation, off-line TMS. While we target fundamental brain mechanisms, we also aim to establish specific protocols to support future therapeutic strategies.

We actively collaborate with all groups at the Institute of Cognitive Neuroscience.

 

 

Our projects:

 

Innovative Neurophysiological Biomarkers for Epilepsy Surgery 

PI: Tommaso Fedele, Alex Ossadtchi

In this project, we plan to conduct a clinical study of patients with pharmacoresistant epilepsy who undergo preoperative implantation of intracranial EEG (iEEG) for optimal planning of a surgical operation. Data on the neuronal activity of patients participating in the study will first be obtained using magnetoencephalography (MEG), then similar data will be obtained using the iEEG in the intensive monitoring unit. The localization of the epileptogenic zone will be based on the use of two innovative neurophysiological biomarkers for MEG / iEEG data: high-frequency oscillations (Fedele et al., 2017, Jacobs et al., 2018; Zijlmans et al., 2017) and an analysis of the functional connectivity of brain regions (Bartolomei et al., 2017; Lagarde et al., 2018; Sinha et al., 2017). The results of our analysis will be informative for clinical evaluation in each individual patient (Vakharia et al., 2018). Thus, based on the collection of a unique set of MEG / EEG data, this clinically oriented project, in turn, will contribute to the improvement of surgical intervention in epilepsy.

Bartolomei F, Lagarde S, Wendling F, McGonigal A, Jirsa V, Guye M, et al. Defining epileptogenic  networks: contribution of SEEG and signal analysis. Epilepsia 2017; 58: 1131–47

Fedele T, Burnos S, Boran E, Krayenbühl N, Hilfiker P, Grunwald T, Sarnthein J (2017) Resection of high frequency oscillations predicts seizure outcome in the individual patient. Sci Rep 7.

Lagarde S, Roehri N, Lambert I, Trebuchon A, McGonigal A, Carron R, Scavarda D, Milh M, Pizzo F, Colombet B, Giusiano B, Medina Villalon S, Guye M, Bénar C-G, Bartolomei F (2018) Interictal stereotactic-EEG functional connectivity in refractory focal epilepsies. Brain:1–15.

N. Van Klink, A. Hillebrand, M. Zijlmans, Identification of epileptic high frequency oscillations in the time domain by using MEG beamformer-based virtual sensors Clin. Neurophysiol., 127 (2016), pp. 197-208

 

What are experts made of? Uncovering expertise in motor sequence learning

PI: Russell Chan, Tommaso Fedele

Despite over 70 years of motor learning research, the field has yet to fully understand how learning expertise is developed in the brain (Richard, Clegg, & Seger, 2009; Willingham, 1999; Willingham, Wells, Farrell, & Stemwedel, 2000). This project will use MEG to investigate the changes in time-frequency wavelets as a function of expertise (Heideman, van Ede, & Nobre, 2018). The project will use a multimodal approach to combine both neurological signals and behavioural data and use multivariate approaches to differentiate the representation between experts and poorer motor sequence learners (Diedrichsen & Kriegeskorte, 2017). This  project seeks to uncover one of the fundamental cognitive mechanisms of motor sequence learning.

Diedrichsen, J., & Kriegeskorte, N. (2017). Representational models: A common framework for understanding encoding, pattern-component, and representational-similarity analysis. PLoS Comput Biol, 13(4), e1005508. doi:10.1371/journal.pcbi.1005508

Heideman, S. G., van Ede, F., & Nobre, A. C. (2018). Temporal alignment of anticipatory motor cortical beta lateralisation in hidden visual-motor sequences. European Journal of Neuroscience, 48(8), 2684-2695. doi:10.1111/ejn.13700

Richard, M. V., Clegg, B. A., & Seger, C. A. (2009). Implicit motor sequence learning is not represented purely in response locations. Q J Exp Psychol (Hove), 62(8), 1516-1522. doi:10.1080/17470210902732130

Willingham, D. B. (1999). Implicit motor sequence learning is not purely perceptual. Mem Cognit, 27(3), 561-572. Retrieved from https://www.ncbi.nlm.nih.gov/pubmed/10355244

Willingham, D. B., Wells, L. A., Farrell, J. M., & Stemwedel, M. E. (2000). Implicit motor sequence learning is represented in response locations. Mem Cognit, 28(3), 366-375. Retrieved from https://www.ncbi.nlm.nih.gov/pubmed/10881554

 

Spatial and temporal working memory identified by distinct oscillatory activity 

PI: Tommaso Fedele

Working Memory (WM, represents our ability to process information and guide future behavior (Baddeley, 1992). Several studies have investigated brain activity associated with spatial WM in humans and primates, while few have focused on the neural mechanisms of WM for temporal order information, and on the possible distinct neural resources employed in the processing of temporal and spatial information. Available evidence indicates that similar fronto-parietal regions are recruited during temporal and spatial WM, although there are data suggesting that they are distinct processes. The mechanisms that allow for differential maintenance of these two types of information are unclear.

One possibility is that neural oscillations may differentially contribute to temporal and spatial WM (Gmeindl et al., 2011; Delogu et al., 2012).  In a pioneer study on this topic, Roberts and colleagues (Roberts and al., 2013) used scalp electroencephalography (EEG) to compare patterns of oscillatory activity during maintenance of spatial and temporal information in WM using the very same items for different WM modalities. This study represents the first step in distinct mechanisms for the maintenance of temporal and spatial information in WM. We plan to implement the same stimulus material in MEG settings, which facilitates the visibility of gamma band components, and provides a better source reconstruction in order to allow seed-based connectivity analysis. The characterization of the circuitry orchestrating the working memory processing of spatial and temporal information opens the venue to stimulation studies targeting specific nodes of the network and modulating the single individual performance.

Baddeley A. Working memory. Science. 1992; 255(5044):556–559

Delogu F, Nijboer TC, Postma A. Binding "When" and "Where" Impairs Temporal, but not Spatial Recall in Auditory and Visual Working Memory. Frontiers in Psychology. 2012a; 3:62

Delogu F, Nijboer TC, Postma A. Encoding location and serial order in auditory working memory: evidence for separable processes. Cognitive Processing. 2012b; 13(3):267–276

Gmeindl L, Walsh M, Courtney SM. Binding serial order to representations in working memory: a spatial/verbal dissociation. Memory and Cognition. 2011; 39(1):37–46

Roberts BM, Hsieh LT, Ranganath C, Oscillatory activity during maintenance of spatial and temporal information in working memory. Neuropsychologia. 2013 Jan;51(2):349-57

 

How are sensory predictions modulated by behaviour? A MEG study

PI: Tommaso Fedele, Athina Tzovara

In our day to day lives we are constantly immersed in streams of sensory events like sounds or images, which very often follow repetitive patterns (Garrido et al., 2009). Because of these patterns, it is possible to use past experience to predict future events, before these occur, for example the sound of a siren might predict the arrival of an ambulance. 

Cortical and subcortical brain regions allow us to extract patterns from repeating events, and form predictions about the future (Barascud et al., 2016). Forming predictions can take place either in cases where they are relevant to our actions i.e. while paying attention to environmental stimuli, but also in an automatic way, i.e. while our levels of arousal are low (Tzovara et al., 2015) and attention is distracted (Chouiter et al., 2015). Although actions and behavioral relevance have a strong effect on sensory processing, it still remains unknown how they may alter the generation of sensory predictions, and therefore the learning of new patterns. 

In this study we will use magnetoencephalography (MEG), in combination with eye-tracking and behavioural metrics, in order to study how the formation of predictions is affected by participants’ behaviour.

The experiment is in collaboration with Athina Tzovara, at the University of Bern, Switzerland (https://ccneuro.github.io/). 
 

Barascud, Pearce, Griffiths, Firston, Chait, 2016. Brain responses in humans reveal ideal observer-like sensitivity to complex acoustic patterns. PNAS.

Chouiter, Tzovara, Dieguez, Annoni, Magezi, De Lucia, Spierer. 2015. Experience-based auditory predictions modulate brain activity to silence as do real sounds. J Cogn Neurosc

Garrido, Kliner, Stephan, Friston. 2009. The mismatch negativity: A review of underlying mechanisms. Clin. Neurophysiol.

Tzovara, Simonin, Oddo, Rossetti, De Lucia. 2015. Neural detection of complex sound sequences in the absence of consciousness. Brain.


 

Neurophysiological mechanisms driving cognitive biases and decision-making in trait anxiety

PI: Maria Herrojo Ruiz, Vadim Nikulin

Uncertainty is thought to be central to many psychiatric disorders, most notably anxiety. Only recently a quantitative understanding of the role played by uncertainty in these disorders has started to emerge, particularly in the context of decision-making. One of the key findings is that trait anxiety is associated with impairments in decision-making and learning in unstable environments, due to an abnormal estimation of the task statistical structure. This project will expand these findings by assessing the neural correlates underlying the anxiety-induced biases in processing uncertainty during decision-making. Combining MEG and individual T1-MRI, our goal is to assess whether neural activity in the anterior cingulate cortex and prefrontal areas – known to contribute to the symptoms in anxiety disorders – underlie the impairments in uncertainty processing in anxiety, and thus lead to poorer learning and decision-making.

Hein, T.P., Weber, L.A., de Fockert, J. and Ruiz, M.H., 2019. State anxiety biases estimates of uncertainty during reward learning in volatile environments. bioRxiv, p.809749.

Sporn, S., Hein, T. and Ruiz, M.H., 2020. Alterations in the amplitude and burst rate of beta oscillations impair reward-dependent motor learning in anxiety. Elife, 9, p.e50654.

Pulcu E, Browning M. The misestimation of uncertainty in affective disorders. Trends in Cognitive Sciences. 2019 Oct 1;23(10):865-75.

 

Differential Genetic Influences on Estimates of Uncertainty in Volatile Environments

PI: María Herrojo Ruiz, Ilya Zakharov, Vadim Nikulin, Tommaso Fedele

This project relates to the project “Neurophysiological mechanisms driving cognitive biases and decision-making in trait anxiety”. Comparing performance across a range of monozygotic and dizygotic twins and using a computational model of the task (Mathys et al, 2014), this project will assess how genetic factors influence different components of decision-making and learning under uncertainty.

Hein, T.P., Weber, L.A., de Fockert, J. and Ruiz, M.H., 2019. State anxiety biases estimates of uncertainty during reward learning in volatile environments. bioRxiv, p.809749.

Mathys, C.D., Lomakina, E.I., Daunizeau, J., Iglesias, S., Brodersen, K.H., Friston, K.J. and Stephan, K.E., 2014. Uncertainty in perception and the Hierarchical Gaussian Filter. Frontiers in human neuroscience, 8, p.825.

Linkenkaer-Hansen, K., Smit, D.J., Barkil, A., van Beijsterveldt, T.E., Brussaard, A.B., Boomsma, D.I., van Ooyen, A. and de Geus, E.J., 2007. Genetic contributions to long-range temporal correlations in ongoing oscillations. Journal of Neuroscience, 27(50), pp.13882-13889.
 

Characterization of the Somatosensory Mismatch Negativity in Proximal Muscles of the Upper Limb and Upper Body

PI: Maria Herrojo Ruiz, Anna Shestakova, Seymon Golosheykin

The Mismatch Negativity (MMN) is a neurophysiological signal reflecting the brain's automatic response to any sudden change. This signal can be evaluated with electroencephalography (EEG) or magnetoencephalography (MEG) by measuring the event-related responses or fields (ERP, ERF) locked to deviant stimuli embedded in a sequence of standard ones. The MMN has been extensively investigated in the auditory domain and used as a tool for diagnostics and predictions in clinical populations – known to have an altered MMN, such as schizophrenia. This project aims to better characterize the MMN in the somatosensory domain (sMMN) with a special focus on proximal muscles. This will allow us to establish normative values in the healthy population to use as reference for future comparisons with clinical populations affected by somatosensory and motor processing.

Using MEG recordings, we’re currently assessing the sMMN in healthy participants using ‘standard-omitted’ and ‘standard-deviant’ protocols. The target regions include distal muscles, such as abductor pollicis brevis, and proximal muscles (biceps brachii, pectoralis major). Ongoing results reveal a robust sMMN across all sets of locations, with variability in the direction of ERF change between deviant and standard stimuli across muscle groups. By describing the amplitude and topography patterns across all participants and muscle groups, this research will thus serve as reference for future work in clinical populations that may use the sMMN as a biomarker of abnormalities in somatosensory and motor processing.

Andersen, L.M. and Lundqvist, D., 2019. Somatosensory responses to nothing: an MEG study of expectations during omission of tactile stimulations. NeuroImage, 184, pp.78-89.

Restuccia, D., Marca, G.D., Valeriani, M., Leggio, M.G. and Molinari, M., 2007. Cerebellar damage impairs detection of somatosensory input changes. A somatosensory mismatch-negativity study. Brain, 130(1), pp.276-287.

Molinari, M. and Masciullo, M., 2019. The Implementation of Predictions During Sequencing. Frontiers in cellular neuroscience, 13, p.439.

Our staff

Tommaso Fedele

Assistant Professor

Maria Del Carmen Herrojo-Ruiz

Leading Research Fellow

Vadim Nikulin

Leading Research Fellow

Anna Shestakova

Chief Research Fellow

Semyon Golosheykin

Research Fellow


 

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