NEUROBIOLOGY 

 

Magdalena Sobień, Marcin Barański, Leszek Kaczmarek, Marzena Stefaniuk: Sociability and alcohol addiction-like traits in male and female mice

 

Alcohol consumption poses a significant social challenge, leading to negative consequences for both individuals and society. Even occasional drinking can escalate into serious alcohol-related issues. While some individuals consume alcohol without developing dependence, others are more vulnerable to addiction. Social influences play a crucial role in shaping drinking behaviors, affecting the likelihood of progression to alcohol use disorders. Understanding these factors is essential and animal models provide valuable insights into the mechanisms underlying alcohol addiction. The aim of this study was to examine if social factors – such as the interactions between pairs of animals within the group and their social status – influence excessive alcohol consumption and development of addiction-like traits in mice.

To investigate this we used the IntelliCage training system. Female (n=13) and male (n=14) C57BL6 mice were used. First, animals were allowed to freely explore the cage, next for three weeks they were given access to 20% ethanol for two hours each day. Motivation was assessed by providing access to 20% ethanol under progressive ratio schedule. Withdrawal lasted for one week where no access to ethanol was granted. Social interactions and addiction-like traits were analyzed.

Females and males exhibit slightly different activity with females being more active in terms of overall cage exploration. We observed individual differences in alcohol consumption within one sex. Similarly individual mice differed in the number of interactions between pairs of mice. While some mice actively followed others, some did not. We found no correlation between overall sociability (per whole experiment) and alcohol consumption.

Female and male mice display different interactions patterns across various experimental designs. Both groups show seeking behavior after all corners alcohol access. Sociability correlates with more social mice exhibiting more addiction-like behaviors.

 

Julia Kosowska: Proliferative activity of oligodendrocyte progenitor cells in the postnatal cerebral cortex in vivo

The proneural transcription factor Achaete-scute complex-like 1 (Ascl1) plays an important role in the development of the cerebral cortex and is implicated both in gliogenesis and neurogenesis. Therefore, Ascl1 has been widely used to reprogram non-neuronal cells into neurons in vitro. Previous work showed that Ascl1-mediated glia-to-neuron reprogrammingis less efficient in vivo; however, Ascl1 overexpression enhances the proliferative activity of

oligodendrocyte precursor cells (OPCs) at 12 days post injection (dpi). Based on this I decided to examine the effects of Ascl1 overexpression on cell cycle activity in transduced OPCs and astroglia in mouse postnatal cortex at 4 dpi. To examine the effects of Ascl1 overexpression in a wider context I decided to examine the effects of its phospho-site deficient variant (Ascl1SA6) known for efficient glia-to-neuron conversion, alongside Ascl1.

The experiments were conducted in vivo, in a mouse postnatal cerebral cortex; After transduction with Ascl1 the pups were killed, after receiving an injection of 5-ethynyl-2’-deoxyuridine (EdU) to identify proliferating cells 3h prior to killing. Immunohistochemistry was performed on the cortical tissue, followed by imagining with the confocal microscopy, and analysis with Image J and GraphPad Prism.

In line with the previous work, I show that Ascl1 enhances the cell cycle activity of OPCs but not astroglial cells. In contrast to Asc1, Ascl1SA6 does not significantly affect the proliferative activity in either OPCs or astroglial cells. Interestingly, Ascl1SA6-transduced cells display a significant change in morphology as early as 4 dpi, compared to both control and Ascl1-transduced cells.

Overall, the results indicate the role of Ascl1 as a selective enhancer of oligodendrogliogenesis and provide preliminary evidence that supports the role of Ascl1SA6 as an efficient glia-toneuron reprogramming factor. These results contribute to the general knowledge about the role of Ascl1 in the developing brain which in the future may contribute to enabling the development of therapies for neurodegenerative diseases.

  

Hanna Trebesova, Simona Laugner, Bogna Badyra, Ermis Ryakiotakis, Marcin Lipiec, Konrad Danielewski, Ksenia Meyza, Ewelina Knapska: Central amygdala and ventral tegmental area interplay in social behavior maintenance in mice

 

Understanding the neural basis of social motivation is crucial for uncovering how animals seek and maintain social contact. Social interaction is not only essential for survival and reproduction but also inherently rewarding, suggesting the involvement of specific motivational circuits in the brain. While multiple brain regions contribute to social behaviors, it remains unclear whether the neural mechanisms underlying the initiation of social contact are the same as those sustaining continued social engagement. This study investigates whether these processes rely on distinct or overlapping pathways, with a particular focus on the central amygdala (CeA) and its projections to the ventral tegmental area (VTA) – a key region involved in reward and motivation.

To explore the role of the CeA–VTA circuit in the maintenance of social interaction, adult male C57BL/6J mice were unilaterally injected with pAAV.hSyn.SiO.eOPN3-mScarlet into the CeA, and an optical fiber was implanted ipsilaterally in the VTA. After a four-week recovery period, the mice underwent habituation followed by behavioral testing during dyadic social interaction. During the experimental sessions, yellow light (589 nm) was delivered via the optic fiber to modulate CeA–VTA activity using optogenetic inhibition. Wireless fiber photometry recordings from the VTA, alongside optogenetic inhibition, enabled real-time monitoring of neural activity in response to various social cues. Immunohistochemistry was used to confirm CeA–VTA connectivity and verify the targeted circuit’s involvement.

By combining optogenetics with behaviorally relevant social stimuli, the study assessed how CeA–VTA modulation affects the initiation and maintenance of social interaction. Wireless fiber photometry recordings from the VTA, alongside optogenetic inhibition, enabled real-time monitoring of neural activity in response to various social cues. Immunohistochemistry was used to confirm CeA–VTA connectivity and verify the targeted circuit’s involvement. These results highlight the crucial role of the CeA–VTA pathway in maintaining ongoing social interaction.

 

CLINICAL

 

Antonina Smulska: A Correlation Between Migraine and Endometriosis and Its Clinical Implications

 

Migraine and endometriosis are two diseases that are associated with women. Endometriosis is a condition exclusively affecting the female population as it affects the female genital tract, while migraine is a primary headache disorder having the highest prevalence in women of reproductive age. Although, undoubtedly, they are two distinct disorders, some correlations have been suggested considering the epidemiological similarities.

This systematic review aimed to analyze the putative links between those two diseases. Two databases were searched in accordance with the PRISMA guidelines, which led to the inclusion of 28 of the most appropriate studies. The review was registered in PROSPERO.

A comprehensive analysis of the existing literature allowed us to distinguish six different aspects: (i) the prevalence of migraine in the course of endometriosis, in general, (ii) when comparing endometriosis patients to healthy individuals, (iii) the relation between different migraine types and endometriosis, (iv) pain symptoms in patients with endometriosis and migraine, and finally, (v) molecular and (vi) genetic bases of the suspected correlation

Although not all results are definitely apparent, the results showed a higher prevalence of endometriosis and migraine together than both diseases separately. More precisely, chronic migraine was demonstrated to be the most possibly linked to endometriosis. Moreover, pain symptoms were usually more evident in patients suffering from both diseases at the time. Finally, some suggestions were presented due to this comorbidity’s molecular and genetic bases; however, the literature, especially on this topic, is lacking.

 

 

Alessandro Crimi, W. Ciezobka: Causality, from Granger to LLMs passing by other things

 

We present an end-to-end AI framework for directed graphs, incorporating explainable AI techniques, aimed at modeling brain connectivity in stroke patients. Additionally, we explore the integration of time series analysis using foundation models inspired by large language models (LLMs) to enhance temporal dynamics understanding.

Our machine learning pipeline combines reservoir computing with directed graph analysis to derive effective connectivity from MRI data of stroke patients. Directed graphs are constructed from these connectivity measures and classified using a directed graph convolutional network. Explainable AI tools are employed to interpret the disrupted brain networks and identify relevant biomarkers.

The framework effectively classifies stroke-related brain network disruptions and provides clinically interpretable insights into connectivity alterations. The inclusion of foundation model-based time series analysis enhances the temporal resolution and robustness of the connectivity features.

This approach demonstrates the potential of combining reservoir computing, directed graph analysis, foundation model-driven time series analysis, and explainable AI to improve patient stratification in stroke and other brain diseases. Our technical innovations advance the understanding of effective brain connectivity and pave the way for more interpretable AI-driven clinical tools.

 

 

Klaudia Nowacka-Pieszak, Saeed Samaei, Dawid Borycki: Cerebral blood flow monitoring through speckle contrast analysis in interferometric speckle contrast optical spectroscopy

We present interferometric speckle contrast optical spectroscopy (iSCOS) — a novel approach for real-time, non-invasive monitoring of cerebral blood flow (CBF) during prefrontal activation induced by cognitive stimuli. Unlike conventional diffuse correlation spectroscopy (DCS) that requires ultra-fast detectors, iSCOS leverages spatial speckle contrast analysis, enabling high parallelization with standard 2D imaging sensors, while operating in continuous-wave (CW) mode.

The system employs a Mach-Zehnder interferometric configuration using multi-mode optical fibers to deliver and collect light. The reference and sample beams are recombined, and the resulting interference patterns are captured by a high-speed CMOS camera. Speckle contrast is computed through digital multi-exposure synthesis, allowing robust estimation of blood flow dynamics without the need for expensive single-photon detectors. We validated the system performance in liquid phantoms with controlled scattering properties (μs’ = 7.5–12.5 cm⁻¹), systematically analyzing speckle contrast and autocorrelation functions across various source-detector separations and

integration times. The speckle contrast exhibited superior stability and noise robustness compared to autocorrelation, particularly at larger separations and lower photon counts.

For in vivo evaluation, we monitored prefrontal cortex activation in a healthy volunteer during a reading task. The relative blood flow index increased by ~14% based on autocorrelation analysis and by ~32% using speckle contrast analysis, consistent with prior SPAD-based DCS measurements. Notably, the integration time required for iSCOS was ~10× shorter than conventional methods, demonstrating its efficiency in dynamic hemodynamic monitoring.

Our findings demonstrate that iSCOS enables fast, cost-effective, and scalable CBF monitoring, offering a promising tool for functional neuroimaging in affective neuroscience, psychiatric research, and clinical neuroergonomics.

 

 

COGNITIVE

 

Maria Wrzosek 1, Władysław Średniawa 1, Tomasz Pasterski 2, Paweł Sokal 3, Jan Kamiński 1: Role of substantia nigra in working memory – single neuron study

1 – Laboratory of Neurophysiology of Mind, Nencki Institute of Experimental Biology, Warsaw, Poland

2 – 1st Military Clinical Hospital with Polyclinic in Lublin – Branch in Elk

3 – Jan Biziel University Hospital Collegium Medicum Nicolaus Copernicus University

 

Working memory (WM) allows us to hold and manipulate information over short periods of time, and its efficient functioning is essential for goal-directed behavior. Studies on animals and humans alike have shown that dopamine is involved in WM processes. However, the exact nature of this mechanism remains unknown. According to one hypothesis, dopamine release acts as a gating signal, allowing new representations to be encoded in WM while replacing the old ones.

In this study, we sought to directly test this hypothesis by recording single-neuron activity from the human Substantia Nigra (SN)—a major source of dopaminergic projections—during a task engaging WM.

Data were obtained from Parkinson’s disease patients undergoing deep brain stimulation (DBS) surgery. During the awake part of the procedure, patients viewed two arrows presented sequentially on a screen and were instructed to recall the position of either the first (“ignore” condition) or the second arrow (“update” condition), depending on the trial. To assess the causal role of the SN in WM, we applied brief electrical stimulation in 40 out of 80 trials. Neuronal activity was recorded throughout the task using intracranial electrodes.

The behavioral results shows that electrical stimulation disrupt updating process observed as increase in the reaction time. Moreover, the observed changes in single neuron activity indicate that electrical stimulation can reliably modulate neural firing in the SN. Taken together, these findings support a critical role of dopaminergic system in WM.

 

Adam Brosnan, Hanna Trebesova, Ewelina Knapska: MCage-Ultra: Social hierarchies, Context, and Psychedelics in the Automated Mouse Societies

Establish an automated home-cage monitoring system that assesses social hierachy via chasing, in both males and females. Moreover, I want to see if contextual changes/psychedlics can influence social organisation.

Using EcoHAB, a home-cage monitoring system, to automatically assess social dominance without handling the animals. This is achieved via RFID chips that enable the detection of chasing behaviour and territoriality.

Both sexes demonstrate hierarchical organisation. However, in groups of female mice, the social organisation is a lot more equally distributed than in males. Under certain contexts, males switch from a hierarchy enforced via chasing, to a territorial formation in which one animal occupies 75% of the apparatus, leaving the rest of the animals confined to one small corner. Females appear to be quite flexible and retain the hierarchy based on chasing behaviour.

Males and females form stable social hierarchies in EcoHAB that are flexibly formed across different social contexts. However, male hierarchies are a lot steeper, with greater differences between dominant and subordinate animals. Moreover, males express dominance differently under certain social contexts. This suggests different evolutionary strategies that govern social organisation between the sexes. The impact of psychedelics has not been fully established because the protocol is still being developed. The animals appear to have been influenced via the saline injection (i.e., habituation to the needle). Future research needs to determine ways of delivering the drug without disturbing the animals. This finding demonstrates the importance of home-cage monitoring systems and the influence of handling the animals.

 

 

Marta Agnieszczak, Maria Sygidus, Aneta Szymaszek, Bartosz Kossowski, Maciej Juryńczyk: Cognitive function and brain volumetric profile of asymptomatic subjects with suspected multiple sclerosis.

Asymptomatic patients with incidentally identified brain lesions on magnetic resonance imaging (MRI) suggestive of multiple sclerosis (MS) do not fulfil the current diagnostic criteria of MS and are not offered disease-modifying treatment. In this prospective study we studied the relationship between asymptomatic MS patients, clinically definite MS patients and healthy controls (HC) with regards to hidden brain damage as assessed by cognitive function across multiple domains and volume of cerebral structures.

Twenty-three asymptomatic MS patients, 17 confirmed MS patients and 16 HC completed a battery of cognitive tests, including Symbol Digit Modalities Test (SDMT), California Verbal Learning Test (CVLT-II), Brief Visuospatial Memory Test (BVMT-R), Digit Span, and a computerized Stroop task, and underwent a prospective 3-Tesla research brain MRI in the Laboratory of Brain Imaging, Nencki Institute of Experimental Biology in Warsaw. MRI-derived metrics included normalized brain volume, thalamic volume, mean cortical thickness and T2-weighted lesion volume. Ten variables were selected for principal component analysis (PCA) and reduced to principal components to visualize study participants on the graph in relation to their cognitive function and brain structure volume. Group differences on component scores were analyzed using ANOVA and linear discriminant analysis (LDA).

The first four components accounted for 69.8% of total variance. Visualization of PCA showed three clusters of participants corresponding to three study groups with HC and definite MS patients largely separate while asymptomatic MS patients in-between and overlapping with the two other groups. The variables the most important for separation included visual learning, processing speed, lesion load and normalized brain volume. Clinically definite MS, asymptomatic MS and HC differed significantly on both first and second principal component (p < .05). Linear discriminant analysis using the first four principal components achieved an overall classification accuracy of 61%, with highest separability between clinically definite MS and HC groups.

PCA informed by multiple cognitive and brain volumetric features and coupled with discriminant analysis is able to distinguish early MS from HC and can help profile asymptomatic subjects with suspected MS in relation to hidden brain damage. Adding features from other modalities, e.g. magnetization transfer or white matter tractometry, which can depict underlying brain pathology, holds promise for further improvements in patient classification and guidance for clinical decisions.

 

 

COMPUTATIONAL

 

Agata Gut, Agnieszka Uryga: Influence of the calculation window on the entropy measures of heart rate variability in patients with traumatic brain injury

A healthy heart is not a metronome—each heartbeat varies in timing, resulting in a phenomenon called heart rate variability (HRV). HRV analysis is widely used in wearable devices as a stress metric; however the impact of the calculation window on HRV metrics, especially nonlinear entropy measures, remains understudied. The aim of this study is to investigate how calculation windows affect entropy-based HRV measures in traumatic brain injury (TBI) patients and to analyse their association with brain-specific blood biomarkers.

The analysis was based on 24-hour recordings of invasive arterial blood pressure (ABP) signals obtained from the CENTER-TBI patient database. ABP signal was segmented into pre-defined time intervals (5, 15, 30, and 60 minutes), enabling the identification of R–R intervals used to calculate HRV entropy values. Two types of entropy metrics were analysed: multiscale entropy (MSEn) and sample entropy (SampEn). The resulting entropy values were then compared with the concentrations of six serum biomarkers (S100B, NSE, GFAP, UCH-L1, NFL, and t-tau) available in the CENTER-TBI database, all collected within the first 24 hours after injury. This work was supported by the National Science Centre, Poland (grant no UMO-2022/47/D/ST7/00229).

The study included 20 subjects (male/female: 25%/75%; median age [Q1–Q3]: 53 [29–60] years). The length of the calculation window significantly affected both MSEn (p<0.001) and SampEn (p<0.001). MSEn values were significantly lower than SampEn, with these differences becoming more pronounced as the calculation window increased: 0.92±0.33 vs. 1.19±0.36, p=0.018 for 15-min window and 0.65±0.25 vs. 1.01±0.34; p<<0.001 for 60-min window. Among all screened biomarkers, only GFAP showed a moderate, inverse correlation with the entropy metrics (Spearman’s Rs between -0.4 and -0.5), with stronger correlations observed at longer calculation windows.

The length of the calculation window influences HRV entropy metrics and affects the observed differences between types of entropy measures. It also impacts the strength of the correlation between HRV entropy and specific serum biomarker concentrations. Further studies are needed to confirm those findings.

Piotr Biegański, Karolina Winczewska: Towards understanding actigraphic sleep/wake scoring

Actigraphy is a noninvasive method of measuring subjects movement using a watch-like device. It’s researched as a cheaper and more convenient alternative to polysomnography (PSG), which is the medical standard in sleep assessment. Algorithms used to classify time spent in bed into sleep/wake stages based upon actigraphic data have been developed for a long time, enabling calculating standardized metrics (e.g. sleep efficiency), which in turn allow diagnosis of various sleep disorders. The vast majority of used algorithms are constructed purely empirically, without understanding underlying data, therefore leaving a lot of room for improvement. Main objectives of presented research are: a) to construct a new actigraphic algorithm, yielding better concordance with PSG than existing ones, and b) to present in-depth analysis of how these algorithms work, and how they relate to underlying physiology.

We used two datasets of parallel PSG and actigraphic recordings, gathered over night from healthy humans – one collected at the Faculty of Physics, University of Warsaw, and open dataset Ear-EEG. The total number of recordings was N=123. PSG was staged by a trained expert in both cases. A previously developed framework (Biegański et al., 2021), which treats key step of sleep/wake scoring as filtering allowed in-depth analysis. Utilizing it, we were able to optimize filter parameters, and to explore filter properties – which has not been done before in the context of actigraphic algorithms. We also analyze classification quality in dependence of sleep stage, as described by PSG scoring. Finally, we present the dependency of classification quality on sampling frequency.

The newly developed algorithm is yielding significantly higher correlation with PSG scoring, than the classic algorithms. In the process two trends emerged, which may be understood as two distinct versions of the algorithm – one is similar to classic algorithms, and second differs in some aspects. The new algorithm also is much more robust to sampling frequency changes, as it stems from its a priori properties, and is confirmed by experimental data. Unified framework allows for identification of key features, which in turn enables informed algorithm construction, yielding lesser risk of overfitting.

Presented research is a step towards construction of a much better, than already existing, tool for actigraphy-based sleep quality assessment. The preliminary version of the new algorithm already presents much better concordancy with PSG than classic ones, while being more universal and device-invariant. Further research is needed, however, as some limitations of classic algorithms are still present in the proposed one.

Study financed from the state budget within the program of the Polish Minister of Education and Science under the name ”Perły Nauki”, project number PN/01/0111/2022, funding value 239 998.00 zł, total value 239 998.00 zł 

 

Sylwia Adamus, Małgorzata Draps, Piotr Suffczyński: Finding amygdala – a comparative analysis of different amygdala masks using functional magnetic resonance imaging data

 

Diffuse Optical Tomography (DOT), as a high-density variant of functional near-infrared spectroscopy (fNIRS), shows potential due to its increased spatial resolution, while retaining the ease of use that characterizes optical brain imaging techniques. We used the mobility of this solution to investigate the phenomenon of Cognitive Motor Interference (CMI). It occurs when the performance of one or both tasks deteriorates due to the simultaneous execution of a motor and cognitive dual task. Current methods and available literature do not allow for precise localization of the specific locus of dual-task interference in the brain (Leone et al., 2017). For this purpose the aim of the study was to identify neural differences between single-task (ST) and dual-task (DT) using DOT.

60 participants (31 F) took part in our study, aged 18-76 (M = 26,71; SD = 13,04). Individuals performed the task of multiplying two-digit numbers (MC) and resting (REST) while sitting (single-task) and walking on a treadmill (dual-task). The hemodynamic signal using fNIRS Spectrum C23 (Cortivision sp z o.o.) was recorded from 314 channels using 47 emitters and 32 detectors with distances 10, ~16 and ~36 mm. Photon propagation and image reconstruction was done on the ICBM152 model. Calculation of oxyhemoglobine (HbO) and deoxyhemoglobine (HbR) was done for specific labels from the Schaefer Atlas (Schaefer et al., 2018).

We found a significant difference in response accuracy F(1, 37) = 4.11; p = 0.05 between ST and DT. Participants performed worse on the DT task (M = 70; SE = 2.39) than on the ST task (M = 74.50; SE = 2.83). We observed a decrease of HbO and increase of HbR F(1, 40) = 14.52; p < 0.001; partial η2 = 0.27, during MC in mPFC (Mdiff = -0.021; pbonf = 0.002). Reversed pattern of activation was observed for REST in mPFC which is a part of default mode network (DMN) (Mdiff = 0.024; pbonf = 0.001). Surprisingly, contrary to our predictions such a pattern were also found for left dPFC F(1,30) = 14.80; p < 0.001; partial η2 = 0.33 and right dPFC F(1,32) = 33.605; p < 0.001; partial η2 = 0.51 which is a part of DMN. Decrease of HbO and increase of HbR was found for MC and reversed pattern has been observed for REST for left dPFC (Mdiff = -0.016; pbonf < 0.001; Mdiff = 0.019; pbonf < 0.001) and right dPFC (Mdiff = -0.025; pbonf < 0.001; Mdiff = 0.028; pbonf < 0.001).

Our findings demonstrate that DT coordination is not dependent upon the exclusive prefrontal area but rather involves the interplay of various specialized information-processing systems. Further analysis using functional connectivity is needed to clarify this.