Faculty of Philosophy, Department of Clinical Psychology
Jagiellonian University, Krakow
The Czech Academy of Sciences, Institute of Scientific Instruments
Student / Ph.D. Student in EEG Research
We offer a position in the Computational Neuroscience research team
Multi-modal and Functional Neuroimaging Research Group is
Opening new PhD positions
in the field of Neurosciences
Successful AZV projects
The Ministry of Health has already announced the result of a one-stage public competition in research, experimental development and innovation for the years 2022-2025 for special-purpose support for medical research and development projects.
Our Latest Research
A brain atlas of axonal and synaptic delays based on modelling of cortico-cortical evoked potentials
Epilepsy presurgical investigation may include focal intracortical single-pulse electrical stimulations with depth electrodes, which induce cortico-cortical evoked potentials at distant sites because of white matter connectivity. Cortico-cortical evoked potentials provide a unique window on functional brain networks because they contain sufficient information to infer dynamical properties of large-scale brain connectivity, such as preferred directionality and propagation latencies. Here, we developed a biologically informed modelling approach to estimate the neural physiological parameters of brain functional networks from the cortico-cortical evoked potentials recorded in a large multicentric database.
Comparison of CNN-Learned vs. Handcrafted Features for Detection of Parkinson's Disease Dysgraphia in a Multilingual Dataset
Parkinson's disease dysgraphia (PDYS), one of the earliest signs of Parkinson's disease (PD), has been researched as a promising biomarker of PD and as the target of a noninvasive and inexpensive approach to monitoring the progress of the disease. However, although several approaches to supportive PDYS diagnosis have been proposed (mainly based on handcrafted features (HF) extracted from online handwriting or the utilization of deep neural networks), it remains unclear which approach provides the highest discrimination power and how these approaches can be transferred between different datasets and languages.
Ultrashort Door-to-Needle Time for Intravenous Thrombolysis Is Safer and Improves Outcome in the Czech Republic: Nationwide Study 2004 to 2019
Background The benefit of intravenous thrombolysis is time dependent. It remains unclear, however, whether dramatic shortening of door-to-needle time (DNT) among different types of hospitals nationwide does not compromise safety and still improves outcome. Methods and Results Multifaceted intervention to shorten DNT was introduced at a national level, and prospectively collected data from a registry between 2004 and 2019 were analyzed. Generalized estimating equation was used to identify the association between DNT and outcomes independently from prespecified baseline variables. The primary outcome was modified Rankin score 0 to 1 at 3 months, and secondary outcomes were parenchymal hemorrhage/intracerebral hemorrhage (ICH), any ICH, and death.
A high-density EEG investigation into the neurocognitive mechanisms underlying differences between personality profiles in social information processing
This study investigated whether differences between personality styles in the processing of social stimuli reflect variability in underlying general-purpose or social-specific neurocognitive mechanisms. Sixty-five individuals classified previously into two distinct personality profiles underwent high-density electroencephalography whilst performing tasks that tap into both aspects of cognitive processing - namely, two distinct facets of general-purpose response inhibition (interference resolution and action withholding) during social information processing. To determine the stage of processing at which personality differences manifest, we assessed event-related components associated with the early visual discrimination of social stimuli (N170, N190) and later more general conflict-related processes (N2, P3).
Sick leave duration as a potential marker of functionality and disease severity in depression
Objective: To discuss the impact of depression on work and how depression-related sick leave duration could be a potential indicator and outcome for measuring functionality in depression.Methods: Our review was based on a literature search and expert opinion that emerged during a virtual meeting of European psychiatrists that was convened to discuss this topic.
Results: Current evidence demonstrates that depression-related sick leave duration is influenced by multiple disease-, patient- and work-related factors, together with societal attitudes towards depression and socioeconomic conditions. A wide variety of pharmacological and non-pharmacological treatments and work-based interventions are effective in reducing depression-related sick leave duration and/or facilitating return to work.
Semi-automated detection of cervical spinal cord compression with the Spinal Cord Toolbox
Background: Degenerative cervical spinal cord compression is becoming increasingly prevalent, yet the MRI criteria that define compression are vague, and vary between studies. This contribution addresses the detection of compression by means of the Spinal Cord Toolbox (SCT) and assesses the variability of the morphometric parameters extracted with it.
Methods: Prospective cross-sectional study. Two types of MRI examination, 3 and 1.5 T, were performed on 66 healthy controls and 118 participants with cervical spinal cord compression. Morphometric parameters from 3T MRI obtained by Spinal Cord Toolbox (cross-sectional area, solidity, compressive ratio, torsion) were combined in multivariate logistic regression models with the outcome (binary dependent variable) being the presence of compression determined by two radiologists.
Neuroplasticity in Motor Learning Under Variable and Constant Practice Conditions-Protocol of Randomized Controlled Trial
Background: There is numerous literature on mechanisms underlying variability of practice advantages. Literature includes both behavioral and neuroimaging studies. Unfortunately, no studies are focusing on practice in constant conditions to the best of our knowledge. Hence it is essential to assess possible differences in mechanisms of neuroplasticity between constant vs. variable practice conditions. The primary objectives of the study described in this protocol will be: (1) to determine the brain's structural and functional changes following constant and variable practice conditions in motor learning (structural and functional magnetic resonance imaging, MRI); (2) to determine the EEG activation and connectivity between cognitive, sensory, and motor cerebral cortex areas (central, temporal, parietal, occipital) in constant and variable practice conditions and as a function of practice time.
Structural MRI-Based Schizophrenia Classification Using Autoencoders and 3D Convolutional Neural Networks in Combination with Various Pre-Processing Techniques
Schizophrenia is a severe neuropsychiatric disease whose diagnosis, unfortunately, lacks an objective diagnostic tool supporting a thorough psychiatric examination of the patient. We took advantage of today’s computational abilities, structural magnetic resonance imaging, and modern machine learning methods, such as stacked autoencoders (SAE) and 3D convolutional neural networks (3D CNN), to teach them to classify 52 patients with schizophrenia and 52 healthy controls. The main aim of this study was to explore whether complex feature extraction methods can help improve the accuracy of deep learning-based classifiers compared to minimally preprocessed data. Our experiments employed three commonly used preprocessing steps to extract three different feature types.