BRADY - available positions
Postdoc positions at ICS, CAS now available.
(Starting 1.3.2024 and later)
Project description:
The last decades brought an unprecedented growth in mapping human brain function and its relation to inter-individual differences in cognition and behaviour. It moved from localizing functions to capturing interactions between brain areas - functional connectivity, along with a paradigm shift from investigation of task-evoked to intrinsic, spontaneous activity. The brain activity is increasingly seen as a train of switches among discrete brain states with distinct patterns of topological and topographical structure.
However, the quest for characterizing brain dynamics is facing serious challenges. The real-world data, let alone detailed computational models, suffer from extreme spatial dimensionality and complex temporal dynamics. Using linear dimension reduction is questionable given inherent neuronal nonlinearity. Indeed a plethora of methods for (and reports of) capturing complex brain patterns impede any reductionism. Understanding the brain through a low-dimensional dynamical model thus may appear illusory. At the same time, the unresolved problem of extreme spatial dimensionality fundamentally limits the full utilisation of brain dynamics findings in understanding the nature of neuropsychiatric disorders and introducing new therapies of different modalities (pharmacotherapy, neurostimulation, etc.).
Therefore, the research and clinical practice calls for - and shows evidence - of utility of simplistic models of brain dynamics. The evidence grows that the brain ‘lives’ on a low-dimensional manifold of possible states. This project aims to establish a family of generative models of graded complexity capturing brain dynamics that would:
- Approximate spatial patterns observed in resting state neuroimaging, including topology and topography of functional connectivity
- Approximate temporal brain dynamics, including activity fluctuations, connectivity dynamics, and information flow
- Provide an internally consistent unified account of electrophysiological and fMRI data, linking to biologically interpretable latent variables and structural connectivity
- Allow predicting the effect of external stimulation, such as complex sensory and cognitive tasks, electric of magnetic stimulation, or pharmacological intervention
- Allow capturing the key axes of inter-subject variability, linking it to psychometric data
- Ultimately allow characterising and predicting the trajectory of deviation from healthy brain dynamics in a range of psychiatric and neurological diseases
Topics of postdoctoral positions:
Topic 1: Detailed biophysical modelling of neurotransmitter action. (Supervised by Pavel Sanda)
Brief description: Neuromodulatory systems provide the basis for cognitive actions in the brain and strongly influence human behaviour. Each system is linked with a specific brain centre(s) and any imbalance in neurotransmitter release, delivery or interaction leads to various disease states. Capturing neurotransmitter influence on the microcircuit level leads to the proper description of the circuit dynamics and allows prediction of pharmaceutical interventions typically targeting particular neuromodulatory systems. The successful candidate will study the transitions between different functional modes in awake/sleep states and link their aberrations to potential neurochemical disease mechanisms at the cellular level.
Topic 2: Computationally efficient mean-field models of cortical microcircuits. (Supervised by Helmut Schmidt)
Brief description: The cortex is composed of various types of neurons, as well as astrocytes, forming cortical microcircuits that may be regarded as the fundamental unit of cortical networks. A special focus will be on astrocytes, which act as ion buffers and facilitating factor of synaptic plasticity, but also modulate blood flow. The successful candidate will develop mean-field models of such microcircuits, build and calibrate forward models for EEG and fMRI, and establish the correspondence with spiking network models (in close cooperation with topic 1).
Topic 3: Whole-brain dynamics with applications particularly to schizophrenia and Parkinson's disease. (Supervised by Gustavo Deco)
Brief description: Whole-brain models often assume homogeneity between cortical areas, in part to avoid overparameterization of the mathematical framework. However, the human cortex tends to be highly heterogeneous in terms of cortical thickness, (inter-)neuron densities, and receptor densities. Furthermore, subcortical areas modulate the activity of cortex in a complex fashion. Cortical and subcortical structure and function can be implicated across a multitude of disorders, such as schizophrenia (SZ) and Parkinson’s disease (PD). The successful candidate will develop whole-brain models that incorporate such heterogeneity and cortical-subcortical interaction, and calibrate the models using data from SZ and PD cohorts, as well as healthy controls.
Topic 4: Data-driven model inversion and personalized parameter identification. (Supervised by Nikola Jajcay)
Brief description: Inferring hidden brain states from neuroimaging measurements presents an intriguing possibility of detecting (patho-)physiological processes in individuals. There exists a wealth of neuroimaging data acquired in health and disease in rodents and humans from various sources: publicly available datasets such as the Human Connectome Project or UK Biobank, and in-house data from our partners at NIMH. The successful candidate will work on the model inversion pipeline, specifically, the inference of parameters from forward models developed by the other postdocs in this project.
Topic 5: Modelling interventions into arousal dynamics. (Supervised by Jaroslav Hlinka)
Brief description: Sleep-wake dysregulation is a common feature of many mental disorders, including depression and insomnia. The common denominator of their sleep and wake disorders is altered arousal, i.e. hyperarousal. The NIMH provides a unique, longitudinal dataset of circa 1000 resting state EEG recordings in depressed patients treated with different pharmacological and non-pharmacological methods. The successful candidate will test whether and how markers of arousal and arousal dynamics change after successful treatment, whether they can be used as predictors of treatment response, and whether descriptive and predictive potential of arousal characteristics varies between interventions, including sleep arousal and sleep dynamics.
Conditions:
- Contract is for 24 months duration initially (with possibility of extension).
- Positions are available from 01. 03. 2024.
- Applications will be reviewed on a rolling basis, until the positions are filled.
- This is a full-time fixed term contract appointment. Part time contract negotiable.
- Competitive monthly gross salary: 50 000 - 66 000 CZK based on qualifications and experience (net salary approx. 40 000 - 52 000 CZK). Cost Of Living Comparison
- Bonuses and travel funding for conferences and research stays depending on performance.
- No teaching duties.
- Applicants should:
- We seek curious, self-motivated, hard-working, team-spirited researchers.
- Prior experience with time series and network analysis is advantageous, but not required.
- The applications are to be sent to bradyadmin@cs.cas.cz and should contain:
Interested applicants are encouraged to contact the project coordinator, Jaroslav Hlinka (bradyadmin@cs.cas.cz), and the respective PIs for informal discussions: