Fedaravičius, Augustinas Povilas

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.

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 neurodegenerative disorder marked by motor impairments, often accompanied by pathological oscillations in brain networks (notably excessive beta-band activity). Understanding these alterations is crucial for improving diagnosis and guiding therapies such as deep brain stimulation (DBS). In this study, we combined a computational model with source-reconstructed EEG to examine how dopaminergic modulation and DBS affect brain network activity in PD. We created a neural mass model that accounts for dopamine-driven modulation. The model suggests that increasing dopamine reduces pathological oscillations and promotes more stable brain activity. To test this prediction, we analyzed high-density EEG from a PD patient recorded before DBS (on/off medication) and six months after DBS implantation (on/off stimulator, on/off medication). EEG signals were source-reconstructed using the patient’s MRI, and functional connectivity metrics (amplitude envelope correlation, AEC; phase-locking value, PLV) were calculated across standard frequency bands. Our results showed that beta-band functional connectivity was markedly reduced after DBS – the largest change among all frequency bands. Beta-band coupling between cortical regions (especially frontal and temporal areas) significantly decreased postDBS, whereas changes in delta, theta, alpha, and gamma bands were minor. This reduction in pathological beta synchrony aligns with our model’s prediction that enhanced dopaminergic signaling suppresses excessive network oscillations and corresponds to improved motor function with DBS therapy. These findings highlight beta-band connectivity as a key biomarker of PD network changes and demonstrate that integrating computational modeling with EEG connectivity analysis yields mechanistic insight into DBS effects, informing personalized neuromodulation strategies. To further support clinical translation, these models will be embedded into The Virtual Brain platform to create individualized digital twins of Parkinson’s disease patients.

Parkinson’s disease (PD) is a progressive neurodegenerative disorder characterised by motor and non-motor symptoms, and is associated with altered brain network dynamics. Understanding large-scale functional changes is crucial for improving diagnosis, treatment planning, and monitoring of disease progression or intervention effects. Electroencephalography (EEG) combined with source reconstruction and magnetic resonance imaging (MRI) data facilitates the analysis of cortical dynamics with greater anatomical precision. The present study investigates differences in preoperative and postoperative brain activity in a PD patient using source-constructed EEG. The objective of this study is to extract and compare the connectivity and dynamical features of brain networks to identify functional alterations associated with the deep brain stimulation (DBS) treatment. The resting state EEG dataset of PD patients was collected before and after DBS electrode implantation. The patients were treated at the Department of Neurosurgery, Lithuanian University of Health Sciences Hospital in Kaunas, Lithuania, 2024–2025. Preoperatively, EEG recordings were obtained when the patients were off L-DOPA medication, and postoperatively with the DBS electrode activated and off L-DOPA medication. Post-surgery EEGs were recorded six months later after the surgical intervention. All EEG recordings were acquired using a 64-channel ANT Neuro device. The study protocol was approved by the Kaunas Regional Biomedical Research Ethics Committee (No. BE-2-115), informed consent was obtained from every study participant. EEG preprocessing was performed using Matlab EEGLab and Makoto’s Miyakoshi pipeline. Brain sources were reconstructed utilising processed EEGs in Brainstorm, using patient MRI data to create Boundary Element Method (BEM) surfaces and an individualized head model via OpenMEEG. Electrode positions were manually adjusted, EEG data was concatenated, and downsampled to the Desikan-Killiany atlas. Source scouts were then extracted and resegmented to the 3-second epochs. The EEG features Amplitude Envelope Correlation (AEC) and Phase Lock Value (PLV) were calculated. A Wilcoxon permutation test (1000 permutations) was used to assess the statistical significance at the edge level, focusing on connections between brain regions. The resulting p-values were MaxT corrected to control the family-wise error rate, and the effect size was calculated. All statistically significant edges exhibited an effect size greater than 0.5. The most significant regions were selected by ranking PLV and AEC metrics. The top 15 of both features were compared to find regions with most statistically significant edges. A normalised sum of all statistically significant edges per EEG band was also calculated for comparison. The most significant changes were found in the Beta band for both AEC and PLV features, as reported in previous studies [1]. Specifically, the normalised number of the significant AEC features in the following bands were as follows: Delta: 0.00, Theta: 1.18, Alpha: 2.71, Beta: 15.82, Gamma: 5.85; the number of the PLV features were: Delta: 0.00, Theta: 1.56, Alpha: 3.53, Beta: 13.62, Gamma: 9.68. The regions with the most significant edges were the Fusiform Gyrus on both left and right side, Pericalcarine Cortex, Parahippocampal Gyrus, Supramarginal Gyrus, Temporal Pole, Pars Opercularis, Precentral Gyrus, and Superior Frontal Gyrus on the left hemisphere, and Caudal Middle Frontal Gyrus and Superior Temporal Gyrus on the right hemisphere. The findings of this study indicate that the most significant alterations in brain connectivity occurred in the Beta frequency band for both AEC and PLV metrics [1]. In contrast, other frequency bands showed comparatively minor alterations. These results suggest that DBS-induced modulation predominantly affects the Beta band connectivity within these critical brain regions, which may have implications for understanding the neural mechanisms underlying DBS efficacy in Parkinson’s disease

Surgical training traditionally adheres to the apprenticeship paradigm, potentially exposing trainees to an increased risk of complications stemming from their limited experience. To mitigate this risk, augmented and virtual reality have been considered, though their effectiveness is difficult to assess.

Parkinson’s disease (PD), a prevalent neurodegenerative condition, is characterized by motor symptoms linked to dopamine deficits and pathological oscillations in the basal ganglia (BG) neurons within the β frequency range. This study leverages dynamical systems theory to develop a comprehensive model of the BG in PD, integrating key neural populations and their interactions. Building on advances in modeling heterogeneous neural networks with quadratic integrate-and-fire (QIF) neurons [1], we present a full BG network model. This includes the subthalamic nucleus (STN), globus pallidus externus/internus (GPe/GPi), striatum, and thalamus, each represented by a two-dimensional system of firing rate differential equations. The model captures the direct, indirect, and hyper-direct pathways, integrating dopaminergic inputs from the substantia nigra and cortical excitations. Our analysis focuses on the impact of high-frequency stimulation (HFS) on network dynamics. Applying HFS to STN and GPi, we identify parameter regimes in which network synchronization can be effectively suppressed. This intervention is modeled as an increase in the excitability parameter of the targeted neural populations, leading to a Hopf bifurcation that stabilizes the network’s resting state and terminates pathological oscillations [2]. Additionally, we investigate the effects of cortical inputs and reduced dopaminergic input from the substantia nigra on BG dynamics. Our findings reveal that cortical inputs significantly influence the model’s behavior, while a decrease in dopaminergic input correlates with the emergence of synchronized collective dynamics, mimicking PD pathology. This model’s strength lies in its ability to seamlessly integrate and modify various neural populations, offering flexibility in network topology and enabling detailed analysis of both internal dynamics and responses to external stimulation. The primary limitation is its simplified representation of BG dynamics, which may not fully reflect in vivo complexities. Future validation against in vivo data is crucial for its applicability to Parkinson’s disease. Moreover, the model’s handling of cortical inputs is overly simplistic, necessitating more detailed future exploration to accurately capture their complex interactions with the basal ganglia.

Background: Fibrocartilaginous embolism (FCE) is a rare cause of ischemic myelopathy that occurs when the material of the nucleus pulposus migrates into vessels supplying the spinal cord. The authors presented a case of pediatric FCE that was successfully managed by adapting evidence-based recommendations used for spinal cord neuroprotection in aortic surgery. Observations: A 7-year-old boy presented to the emergency department with acute quadriplegia and hemodynamic instability that quickly progressed to cardiac arrest. After stabilization, the patient regained consciousness but remained in a locked-in state with no spontaneous breathing. The patient presented a diagnostic challenge. Traumatic, inflammatory, infectious, and ischemic etiologies were considered. Eventually, the clinical and radiological findings led to the presumed diagnosis of FCE. Treatment with continuous cerebrospinal fluid drainage (CSFD), pulse steroids, and mean arterial pressure augmentation was applied, with subsequent considerable and consistent neurological improvement. Lessons: The authors proposed consideration of the adaptation of spinal cord neuroprotection principles used routinely in aortic surgery for the management of traumatic spinal cord ischemia (FCE-related in particular), namely, permissive arterial hypertension and CSFD. This is hypothesized to allow for the maintenance of sufficient spinal cord perfusion until adequate physiological blood perfusion is reestablished (remodeling of the collateral arterial network and/or clearing/absorption of the emboli).

Collective oscillations and their suppression by external stimulation are analyzed in a large-scale neural network consisting of two interacting populations of excitatory and inhibitory quadratic integrate-and-fire neurons. In the limit of an infinite number of neurons, the microscopic model of this network can be reduced to an exact low-dimensional system of mean-field equations. Bifurcation analysis of these equations reveals three different dynamic modes in a free network: a stable resting state, a stable limit cycle, and bistability with a coexisting resting state and a limit cycle. We show that in the limit cycle mode, high-frequency stimulation of an inhibitory population can stabilize an unstable resting state and effectively suppress collective oscillations. We also show that in the bistable mode, the dynamics of the network can be switched from a stable limit cycle to a stable resting state by applying an inhibitory pulse to the excitatory population. The results obtained from the mean-field equations are confirmed by numerical simulation of the microscopic model.

Introduction Schwannomatosis is defined as multiple schwannomas without presence of neurofibromatosis and is a rare pathology. In vast majority of cases the schwannomas grow from different nerve roots or peripheral nerves. Presentation of case A 52-year-old woman presented with multiple intradural schwannomas arranged in a chain along the spinal canal causing significant compression. The lesions were successfully removed using a left side en-bloc hemilaminectomy technique in order to preserve maximal stability of the posterior column. Back and leg pain resolved completely. Tendon reflexes returned to normal shortly. There was decreased pain sensation in the distribution of the left L3 spinal root. Discussion The traditional surgical strategy for posterior approach by laminectomy or laminotomy is sometimes complicated with instability or deformation of the vertebral column that requires surgical stabilization. We performed a one side en-bloc hemilaminectomy thus maintaining the integrity of the muscles and ligaments on the opposite side and preserving maximal stability of the vertebral column. Densely adherent tumors required careful sharp dissection and separation under neurosurgical monitoring and stimulation for recognition and preservation of spinal roots. An additional tumor was discovered by exploration of the spinal canal using an endoscope. Conclusion Multiple spinal cord schwannomas that are growing along the same part of the vertebral column can be safely removed by one-sided hemilaminectomy with preservation of the integrity of the muscles and ligaments on the opposite side and thus maintain spinal stability. The 30° endoscope can be a good tool for visual exploration of the spinal canal.