Radžiūnas, Andrius

Negatyvūs simptomai pasireiškia iki pirmųjų psichozės simptomų, palaipsniui sunkėja ir blogina pacientų gyvenimo kokybę bei funkcionavimą. Nepaisant centrinio šių simptomų vaidmens šizofrenijos klinikoje, epidemiologinių duomenų apie jų paplitimą vis dar trūksta, todėl šio tyrimo tikslas buvo nustatyti negatyvių simptomų pasireiškimą ir įvertinti jų sąsajas su gyvenimo kokybe. Negatyvių simptomų vertinimo gairėse rekomenduojama juos vertinti pusiau struktūruotu interviu kartu su savęs vertinimo skale, todėl tyrimo metu į lietuvių kalbą buvo išverstos ir validuotos dvi negatyvius simptomus vertinančios skalės – Savęs vertinimo negatyvių simptomų skalė ir pusiau struktūruotu interviu pagrįsta Trumpoji negatyvių simptomų vertinimo skalė. Tyrime dalyvavo šizofrenijos spektro sutrikimais sergantys asmenys, gydyti Lietuvos sveikatos mokslų universiteto ligoninės Kauno klinikų Psichiatrijos klinikos stacionare. Nustatyta, kad dauguma tiriamųjų turėjo išreikštus negatyvius simptomus, tačiau pirminiai negatyvūs simptomai nustatyti tik maždaug penktadaliui tiriamųjų. Labiau išreikšti negatyvūs simptomai, ypač vertinti Savęs vertinimo negatyvių simptomų skale, buvo susiję su blogesne gyvenimo kokybe, o jų sąsajos su gyvenimo kokybės įverčiais buvo stipresnės nei sąsajos su sociodemografiniais veiksniais. Tai pirmieji tokio tipo epidemiologiniai duomenys Lietuvoje, papildantys pasaulinę literatūrą apie negatyvių simptomų ir skirtingų jų grupių paplitimą bei sąsajas su gyvenimo kokybe.

Šiame tyrime nustatyta sveikų tiriamųjų periferinių nervų dydžių ir echo¬geniškumo referentinės reikšmės Lietuvos populiacijai. Atlikta PN parametrų analizė atsižvelgiant į skirtingus demografinius ir antropometrinius duome¬nis. Manome, kad tai yra pirmasis tokio pobūdžio tyrimas Baltijos šalyse. Ultragarsinis tyrimas atliktas itin plačiai – pagal kelis skirtingus ultragarsinio tyrimo protokolus, įvertintas šių protokolų diagnostinis tikslumas bei pateik¬tas naujas vietinis ultragarsinio tyrimo protokolas. Tyrime analizuoti galimi PN charakteristikų skirtumai skirtingų uždegiminių polineuropatijų metu. Duomenys apie PN skerspjūvio plotą, skersmens ir echogeniškumo pokyčius trumpuoju laiko periodu po taikyto aktyvaus imunomoduliuojamojo gydymo gali būti vertingi sonografiniai žymenys gydymo atsako vertinimui. Duome¬nys apie sonografijos charakteristikas ir jų sąsajas su biologiniais žymenimis, atspindinčiais autoimuninius procesus bei aksono pakenkimą leis geriau su¬prasti grįžtamąją ir negrįžtamąją nervų pažaidas.

Postoperative delirium is a common complication following sub-thalamic nucleus deep brain stimulation surgery in Parkinson's disease patients. Postoperative delirium has been shown to prolong hospital stays, harm cognitive function, and negatively impact outcomes. Utilizing radiomics as a predictive tool for identifying patients at risk of delirium is a novel and personalized approach. This pilot study analyzed preoperative T1-weighted and T2-weighted magnetic resonance images from 34 Parkinson's disease patients, which were used to segment the thalamus, amygdala, and hippocampus, resulting in 10,680 extracted radiomic features. Feature selection using the minimum redundancy maximal relevance method identified the 20 most informative features, which were input into eight different machine learning algorithms. A high predictive accuracy of postoperative delirium was achieved by applying regularized binary logistic regression and linear discriminant analysis and using 10 most informative radiomic features. Regularized logistic regression resulted in 96.97% (±6.20) balanced accuracy, 99.5% (±4.97) sensitivity, 94.43% (±10.70) specificity, and area under the receiver operating characteristic curve of 0.97 (±0.06). Linear discriminant analysis showed 98.42% (±6.57) balanced accuracy, 98.00% (±9.80) sensitivity, 98.83% (±4.63) specificity, and area under the receiver operating characteristic curve of 0.98 (±0.07). The feed-forward neural network also demonstrated strong predictive capacity, achieving 96.17% (±10.40) balanced accuracy, 94.5% (±19.87) sensitivity, 97.83% (±7.87) specificity, and an area under the receiver operating characteristic curve of 0.96 (±0.10). However, when the feature set was extended to 20 features, both logistic regression and linear discriminant analysis showed reduced performance, while the feed-forward neural network achieved the highest predictive accuracy of 99.28% (±2.71), with 100.0% (±0.00) sensitivity, 98.57% (±5.42) specificity, and an area under the receiver operating characteristic curve of 0.99 (±0.03). Selected radiomic features might indicate network dysfunction between thalamic laterodorsal, reuniens medial ventral, and amygdala basal nuclei with hippocampus cornu ammonis 4 in these patients. This finding expands previous research suggesting the importance of the thalamic-hippocampal-amygdala network for postoperative delirium due to alterations in neuronal activity.

Background: Trigeminal neuralgia (TN) is commonly caused by a vascular compression of the trigeminal nerve (classical TN). Due to the high frequency of this compression at the nerve's root entry zone, a misunderstanding has arisen in the literature that this location is the only site that can cause TN. The present case demonstrates that TN can be caused by compression anywhere along the nerve-even in the face.

Vagus Associated Neurogenic Cough Occurring due to Unilateral Vascular Encroachment of its Root (VANCOUVER) syndrome is a recently described neurovascular compression syndrome causing neurogenic cough due to a vascular compression of the vagus nerve at the brainstem. Some patients with VANCOUVER syndrome can lateralize the tickling sensation causing their irresistible cough; others cannot. For those who cannot lateralize their symptoms, a diagnostic vagus nerve block is required. This paper highlights the technique of percutaneous cervical vagus nerve block and its use in the diagnostic protocol for VANCOUVER syndrome.

Parkinson's disease (PD) is a progressive neurodegenerative disorder characterized by motor and non-motor symptoms, including cognitive decline and reduced quality of life. Identifying reliable biomarkers for disease progression and symptom severity remains a critical challenge. In this study, levels of oxidative stress-related microRNAs (miR-24-3p, miR-103a-3p, miR-320a-3p, miR-494-3p, miR-126-5p, and miR-543) within blood serum extracellular vesicles (EVs) were examined in a cohort of 93 PD patients to assess their associations with cognitive function, symptom severity, quality of life, and other clinical characteristics. The methods included microRNA extraction from blood serum EVs, followed by cDNA synthesis and RT-qPCR for expression analysis. Upregulation of miR-126-5p, as well as downregulation of miR-24-3p showed the strongest associations with symptom severity and cognitive decline, whereas downregulated miR-320a-3p levels correlated with patient-reported quality of life in PD patients. Downregulation of miR-103a-3p, and miR-543 expression showed slight associations with motor symptoms, cognitive function, and quality of life domains; however, some of these associations lacked statistical power. These findings indicate that specific microRNA expression profiles in extracellular vesicles are associated with PD symptom severity and progression, supporting their further evaluation as biomarkers in larger independent cohorts.

Introduction Parkinson’s disease (PD) is a neurodegenerative disorder affecting motor and cognitive function. While dopaminergic medications provide symptom relief, deep brain stimulation (DBS) is often used in advanced cases to manage motor dysfunction. However, the response to DBS varies between individuals, and optimal stimulation settings are typically determined through clinical trial and error. This highlights the need for biomarkers to monitor DBS-induced changes in brain function. Methods In this pilot study, we explored EEG-based connectivity features as potential biomarkers for DBS effects in two PD patients. EEG was recorded before and after DBS implantation. Preoperative EEGs were collected while patients were off medication; postoperative EEGs were obtained six months later with DBS turned on. Preprocessing followed Makoto’s pipeline in EEGLAB, and source reconstruction was conducted in Brainstorm using MRI-based Boundary Element Method head models generated via Open-MEEG. EEG signals were projected onto the Desikan-Killiany atlas and segmented into 3-second epochs. We extracted three connectivity metrics from source- space data: Amplitude Envelope Correlation (AEC), Phase Locking Value (PLV), and Avalanche Transition Matrix (ATM). A Wilcoxon permutation test (1000 iterations) with max-statistic correction was used to assess changes between conditions. Results ATM showed no significant differences. In contrast, AEC and PLV both revealed reductions in beta- band connectivity after DBS activation (Fig 1). Subject specific patterns were observed: subject 1 showed stronger AEC changes, while subject 2 exhibited more pronounced PLV effects. These results suggest that beta-band connectivity is sensitive to neuromodulation and that EEG-based metrics can capture individualized brain dynamics. Conclusions This pilot study supports the use of source-space EEG connectivity analysis for tracking DBS- elated changes and lays the groundwork for future integration into modeling frameworks such as The Virtual Brain (TVB).

Introduction Parkinson's disease, the second most common neurodegenerative disorder, is marked by progressive loss of dopaminergic neurons. Dopaminergic disruption causes severe motor symptoms (resting tremor, rigidity, bradykinesia, postural instability), cognitive deficits, and reduced quality of life in aging. While incurable, treatments such as deep brain stimulation can alleviate symptoms. We aim to develop personalized virtual brain models for Parkinson's patients that explicitly incorporate dopaminergic modulation. Building on our earlier work, we now refine these models by integrating individual structural connectivity, advanced neural mass formulations, and EEG-derived biomarkers, enabling more precise characterization of patient-specific dynamics. Methods We introduced a modular framework to capture dopaminergic regulation at the neural mass scale (Depannemaecker, 2024; Casagrande, 2025). Targeting D1-type receptor dynamics, it enables investigation of how fluctuations in dopamine availability shape population responses. The framework adopts a mean-field formulation (Chen & Campbell, 2022), providing a tractable yet biologically plausible description of macroscopic activity, well-suited to study dopamine-mediated modulation of basal ganglia circuits in health and disease.To identify biomarkers of brain dynamics in Parkinson’s disease (PD), we analyzed resting-state EEG acquired before and after DBS electrode implantation. Preoperatively, patients were tested ON and OFF L-DOPA; postoperatively, six months later, they were assessed with DBS ON and OFF, under both medication conditions. EEG features—Amplitude Envelope Correlation (AEC) and Phase Locking Value (PLV)—were extracted, and significant effects identified with permutation ANOVA. Results Our framework reveals that dopaminergic modulation critically reshapes the dynamical repertoire of neural mass models. By systematically varying dopamine input levels, we identified transitions between qualitatively distinct regimes, including fixed-point convergence, sustained oscillatory activity (limit cycles), and bursting dynamics. Progressive increases in dopaminergic tone induced a marked expansion of quiescent states, accompanied by reduced oscillatory frequencies across both fast spiking and inter-burst domains. Importantly, these model-derived regimes correspond to electrophysiological features observed in EEG and DBS recordings from Parkinsonian patients, thereby providing a mechanistic account of dopamine-dependent neural dynamics. Analysis of EEG data showed that the most significant changes were found in the reduction in the Beta band for both PLV and AEC features, as reported in previous studies. Discussion Our results show that dopaminergic tone critically shapes neural mass dynamics, inducing transitions between quiescent, oscillatory, and bursting regimes. The observed reduction in oscillatory frequency and expansion of quiescent states parallel electrophysiological signatures in Parkinsonian EEG and DBS data, particularly in the Beta band. Next, we will integrate model-derived features with empirical connectivity into a unified framework, extending earlier approaches (Angiolelli, 2025) by incorporating DBS effects. This strategy advances toward personalized dynamical models to guide understanding and optimization of neuromodulation therapies.

Parkinson’s disease (PD) is a progressive neurodegenerative disorder characterized by the gradual loss of dopaminergic neurons in the substantia nigra. PD usually leads to hallmark motor symptoms such as bradykinesia and resting tremor. Although current therapies can alleviate symptoms, the disease remains incurable. MiRNAs derived from extracellular vesicles (EV-derived) have shown great potential as biomarkers for various diseases, including PD. Therefore, finding miRNA biomarkers could enable early disease detection and support personalised treatment strategies. We aimed yo profile EV-derived miRNAs in Parkinson’s disease patients and healthy controls using next-generation sequencing (NGS) to identify potential biomarkers for the disease. EV-derived miRNAs were isolated from the blood serum of five PD patients and five healthy controls. The samples were then subjected to NGS. Sequencing data were analysed using principal component analysis (PCA), DESeq2 for differential expression and KEGG pathway analysis. PCA revealed two distinct clusters corresponding to healthy controls and PD patients. Differential expression analysis using DESeq2 identified ten miRNAs significantly deregulated in PD patients compared to controls. KEGG pathway analysis showed that these miRNAs are involved in pathways, like PD, Alzheimer’s disease, dopaminergic synapse, MAPK signaling, suggesting potential links to molecular mechanisms underlying PD. We identified ten miRNAs as potential biomarkers for Parkinson’s disease. These miRNAs were found to be involved in neurodegeneration-related pathways, suggesting their possible role in PD pathogenesis. Further studies with larger sample sizes are required to validate these findings and better characterize the expression patterns of these miRNAs.