MEG

Structured three-year PhD programme of the International Max Planck Research School on Cognitive NeuroImaging, run by the Max Planck Institute for Human Cognitive and Brain Sciences in Leipzig with Leipzig University, TUD Dresden University of Technology and University College London. Projects sit in three modules: Cognitive Neuroscience, Clinical and Translational Neuroscience, and Development of Neuroimaging and Modelling Methods, with more than 20 recruiting faculty. Facilities include a 9.4T small-bore, a 7T and four 3T MRI scanners (one a Connectom scanner with ultra-strong gradients), a

Permanent research specialist post at NatMEG, Department of Clinical Neuroscience, focused on OPM-MEG (optically pumped magnetometer MEG), whose sensors sit on or near the head and allow measurements during movement and across head sizes. The specialist develops MEG methods and applications in cognitive and clinical neuroscience, serves as Unit Manager at NatMEG with user support, teaches brain imaging (primarily MEG) and supervises doctoral students and postdocs. Full time in Solna, Stockholm, starting with a six-month trial period. Applications close on 11 November 2026 (reference STÖD 2-426

…a shared task measured with EEG, MEG and fMRI, evidence about location, duration and inter-area communication, and the practical difficulties of interpreting mixed results across competing theories.

…from population activity measured with MEG and intracranial EEG to individual neurons sampled across cortical depth with human Neuropixels probes. The studies ask which representations and operations connect speech sounds to meaning, and how information at different timescales is nested across time and cortical space. Comparing biological recordings with artificial models reveals candidate mechanisms for exchangin

…evidence that brain recordings (fMRI, MEG, ECoG) acquired while participants read or listened to unconstrained speech can be predicted by representations extracted from state-of-the-art text- and speech-based LLMs. In particular, text-based LLMs tend to align better with higher-level language regions, capturing more semantic aspects, while speech

Brain activity lies on a very complex network. This complexity is multiple but a prominent one is the non-linear and multi-factorial interaction between its nodes. In the specific case of Parkinson's disease (PD), the brain loses dopaminergic neurons in the substantia nigra compacta (SNc). Highly involved in the basal ganglia-cortex-thalamus-cerebellum (BG-Ctx-T-Cb) network, the main role of dopamine (DA) is in learning, decision-making and motor control, as manifested in the PD symptoms such as rigidity, slowness of movements, tremor and poor behavioural flexibility. Following DA loss, the un

…electro- and magnetoencephalography (E/MEG) [2] (Fig. 1h), amplitude envelopes of oscillations [1] (Fig. 1g), and functional Magnetic Resonance Imaging (fMRI) [3] (Fig. 1f) using several existing methods. Theoretical w

…Recent MEG studies have identified cortical contributions to the speech-FFR (Coffey et al. (2017), Kulasingham et al. (2020), Schüller et al. (2024)), but it remains unclear if these cortica

…signals like EEG and MEG, in particular by optimizing interactions between brain regions, following previous work on the dynamic causal model (DCM) . We compare several modern Bayesian inference schemes for model inversion on simulated data . We benchmark the identifiability of local parameters governing the interplay between several neuronal populations in each region, as well as global parameters t

Neural population signals recorded with magneto- (MEG) or electroencephalography (EEG) contain information about viewed natural images and their categories. However, it remains unclear which specific visual features contribute to this information. To address this, we applied spectral manipulations and different texturization algorithms to natural images of various categories and recorded MEG in human subjects presented with these stimuli. The employed stimulus space allowed us to attribute components of visual information to specific low-, mid- and high-level visual features. Furthermore, this

…contribution to the speech-FFR via MEG recordings in 42 participants with different levels of musi

…(EEG) or magnetoencephalography (MEG), termed speech-FFR. While EEG measurements have found a subcortically evoked early neural activity to this speech-FFR, MEG studies have recently emphasized involvement of cortical sources as well. Here, we focused on disentangling the sources of the neural response at the fundamental frequency of speech using EEG-based source localization. We theref

Social cognition refers to a range of processes that allow us to make sense of others' behaviors and engage in daily interactions. A key network supporting social cognition is the mirror neuron system (MNS), a set of fronto-parietal regions that allows inferring others' intentions through action observation. The MNS exhibits considerable plasticity based on an individual's motor repertoire, but little is known about its potential for reorganization in the presence of brain damage. To address this gap, we investigated the adaptive capacity of the MNS in patients with gliomas affecting key netwo

…then show that magnetoencephalography (MEG) measurements during tACS deviate from the predictions of this model. Specifically, we measured brain activity in human subjects either during 10 Hz vis

…as the acoustic envelope based on EEG/MEG. However, examining how the human brain processes words during continuous speech listening remains challenging.This study presents an assessment of encoding TRF methodologies for the measurement of lexical processing, quantifying and explaining their limitation

The ability to organise one's body for action without having to think about it is taken for granted, whether it is handwriting, typing on a smartphone or computer keyboard, tying a shoelace or playing the piano. When compromised, e.g. in stroke, neurodegenerative and developmental disorders, the individuals’ study, work and day-to-day living are impacted with high societal costs. Until recently, indirect methods such as invasive recordings in animal models, computer simulations, and behavioural markers during sequence execution have been used to study covert motor sequence planning in humans.

Navigating rodents show “forward sweeps” - sequential reactivation of non-local neural representations of future states starting from their current position. While direct evidence remains scant, it is widely thought that such forward sweeps support model-based (MB) evaluation of actions. However, a key challenge in testing this hypothesis is the absence of experimental paradigms that allow for precise measurement of MB action evaluation. Notably, typical assessments of MB evaluation, i.e. flexibility following changes to rewards (reward revaluation), conflate reliance on full MB evaluation wit

Selection is the hallmark of attention: processing improves for attended items but is relatively impaired for unattended items. It is well known that visual spatial attention changes sensory signals and perception in this selective fashion. In the work I will present, we asked whether and how attentional selection happens across time. First, our experiments revealed that voluntary temporal attention (attention to specific points in time) is selective, resulting in perceptual tradeoffs across time. Second, we measured small eye movements called microsaccades and found that directing voluntary t

Electro- and magneto-encephalography (EEG/MEG) are the leading methods to non-invasively record human neural dynamics with millisecond temporal resolution. However, it can be extremely difficult to infer the underlying cellular and circuit level origins of these macro-scale signals without simultaneous invasive recordings. This limits the translation of E/MEG into novel principles of information processing, or into new treatment modalities for neural pathologies. To address this need, we developed the Human Neocortical Neurosolver (HNN: https://hnn.brown/edu ), a new user-friendly neural model

Autobiographical memories are the ghosts of our past. Through them we visit places long departed, see faces once familiar, and hear voices now silent. These, often decades-old, personal experiences can be recalled on a whim or come unbidden into our everyday consciousness. Autobiographical memories are crucial to cognition because they facilitate almost everything we do, endow us with a sense of self and underwrite our capacity for autonomy. They are often compromised by common neurological and psychiatric pathologies with devastating effects. Despite autobiographical memories being central t

Every day when we fall asleep we lose consciousness, we are not there. And then, every morning, when we wake up, we regain it. What mechanisms give rise to consciousness, and how can we explain consciousness in the realm of the physical world of atoms and matter? For centuries, philosophers and scientists have aimed to crack this mystery. Much progress has been made in the past decades to understand how consciousness is instantiated in the brain, yet critical questions remain: can we develop a consciousness meter? Are computers conscious? What about other animals and babies? We have embarked i