Puleikytė, Ieva

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

Creutzfeldt- Jakob disease (CJD) is a rare rapidly prograssive fatal neurodegenerative disease. Neuroleptic malignant syndrome (NMS) is a complication of antipsychotic medications which may be used to treat neuropsychiatric symptoms of CJD. WE present a case of a 51-year- old woman with CJD who developed NMS after being prescribed quetiapine.