Prof. Daniel Wójcik from the Nencki Institute of Experimental Biology explains the principles of brain modelling

This work discusses the heat transfer aspects of the neonate’s brain cooling process carried out by the the device to treat hypoxic-ischemic encephalopathy. This kind of hypothermic therapy is undertaken in case of improper blood circulation during delivery which causes insufficient transport of oxygen to the brain and insufficient cooling of the brain by circulating blood. The experimental setup discussed in this manuscript consists of a special water flow meter and two temperature sensors allowing to measure inlet and outlet water temperatures. Collected results of the measurements allowed to determine time histories of the heat transfer rate transferred from brain to the cooling water for three patients. These results are then analysed and compared among themselves.

Prof. Małgorzata Kossut of the Nencki Institute of Experimental Biology talks about brain plasticity, the mechanisms of learning, and the mysteries of forgetfulness.

The complexity of the phenomena associated with the course of the cognitive processes that determine an efficient learning, excludes the possibility of collecting knowledge in other ways than neuronal-information. It excludes also possibilities of interpreting it, in other ways than with use of respectively formalized cognitive models. The presented paper is a kind of summary of the latest achievements in this field.

In the last decade of the XX-th century, several academic centers have launched intensive research programs on the brain-computer interface (BCI). The current state of research allows to use certain properties of electromagnetic waves (brain activity) produced by brain neurons, measured using electroencephalographic techniques (EEG recording involves reading from electrodes attached to the scalp - the non-invasive method - or with electrodes implanted directly into the cerebral cortex - the invasive method). A BCI system reads the user's “intentions” by decoding certain features of the EEG signal. Those features are then classified and "translated" (on-line) into commands used to control a computer, prosthesis, wheelchair or other device. In this article, the authors try to show that the BCI is a typical example of a measurement and control unit.

Dynamic development in children’s research has led to surprising discoveries about the learning and thinking patterns of fetuses, infants and young children. These studies have revolutionized not only our knowledge of children, but also our understanding of the nature of the human mind and brain. Moreover, within this context, it is believed that many areas of adulthood are the result of the experiences and changes that occur during the fetal period and in childhood. These experiences, therefore, are crucial for human development and what people achieve in the following stages of their lives. The results of the research on brain development during the fetal period and during childhood presented here, reveal a new perspective for understanding the essence and nature of the learning process. These studies also strongly suggest that the first two thousand days of a child’s life are critical in developing many basic human skills. Therefore, we must take great care of the quality of environment for a child’s development.

Complaints and awareness about environmental low-frequency (LF) noise and infrasound (IS) have increased in recent years, but knowledge about perceptual mechanisms is limited. To evaluate the use of the brain’s frequency-following response (FFR) as an objective correlate of individual sensitivity to IS and LF, we recorded the FFR to monaurally presented IS (11 Hz) and LF (38 Hz) tones over a 30-phon range for 11 subjects. It was found that 11-Hz FFRs were often significant already at ~0 phon, steeply grew to 20 phon, and saturated above. In contrast, the 38-Hz FFR growth was relatively shallow and continued to 60 phon. Furthermore, at the same loudness level (30 phon), the 11-Hz FFR strength was significantly larger (4.5 dB) than for 38 Hz, possibly reflecting a higher phase synchronization across the auditory pathway. Overall, unexpected inter-individual variability as well as qualitative differences between the measured FFR growth functions and typical loudness growth make interpretation of the FFR as objective correlate of IS and LF sensitivity difficult.

The brain is subject to damage, due to ageing, physiological processes and/or disease. Some of the damage is acute in nature, such as strokes; some is more subtle, like white matter lesions. White matter lesions or hyperintensities (WMH) can be one of the first signs of micro brain damage. We implemented the Acoustocerebrography (ACG) as an easy to use method designed to capture differing states of human brain tissue and the respective changes.

Aim: The purpose of the study is to compare the efficacy of ACG and Magnetic Resonance Imaging (MRI) to detect WMH in patients with clinically silent atrial fibrillation (AF).

Methods and results: The study included 97 patients (age 66.26 ± 6.54 years) with AF. CHA2DS2-VASc score (2.5 ±1.3) and HAS BLED (1.65 ± 0.9). According to MRI data, the patients were assigned into four groups depending on the number of lesions: L0 – 0 to 4 lesions, L5 – 5 to 9 lesions, L10 – 10 to 29 lesions, and L30 – 30 or more lesions. Authors found that the ACG method clearly differentiates the groups L0 (with 0–4 lesions) and L30 (with more than 30 lesions) of WMH patients. Fisher’s Exact Test shows that this correlation is highly significant (p < 0:001).

Conclusion: ACG is a new, easy and cost-effective method for detecting WMH in patients with atrial fibrillation

In this paper the biofeedback therapy application is presented. The application is implemented in desired biofeedback system based on RaspberyPI. The EEG signal is taken using popular headset with forehead probe and ear reference one. A patient is trying to focus on desired task and should keep attention level above threshold, the threshold is given and monitor by therapist. The success factor during one therapy session should be more than about 80%, so therapist have to control the threshold. The application consists algorithm for automatic threshold correction based on interview with experienced therapist.

Human brain is “the perfect guessing machine” (James V. Stone (2012) Vision and Brain, Cambridge, Mass: The MIT Press, p. 155), trying to interpret sensory data in the light of previous biases or beliefs. Bayesian inference is carried out by three complex networks of the human brain: salience network, central executive network, and default mode network. Their function is analysed both in neurotypical person and Attention Deficit Disorder. Modern human being having predictive brain and overloaded mind must develop social identity, whose evolution went probably through three stages: social selection based on punishment, sexual selection based on reputation, and group selection based on identity.