Citations

The accurate prediction of motor progression in Parkinson’s disease (PD) remains a major clinical challenge that limits personalized treatment planning and efficient clinical trial design. In this study, we developed and validated a machine-learning framework integrating a targeted panel of plasma proteins measured by Olink proximity extension assays with clinical variables to stratify patients according to their progression risk. We analyzed baseline plasma samples from 211 early-stage PD patients enrolled in the Parkinson’s Progression Markers Initiative (PPMI) cohort using four targeted Olink panels, from which 28 circulating proteins were retained after quality-control filtering. Patients were classified as rapid or slow progressors based on their annualized change in MDS-UPDRS Part III scores. Among the algorithms tested, Random Forest achieved the highest discriminative performance with an area under the receiver operating characteristic curve (AUC) of 0.751 (95% CI: 0.684–0.811), which exceeded that of clinical predictors alone (AUC 0.666). The integration of targeted proteomic and clinical features further improved model performance (AUC 0.773; p = 0.009). Nested cross-validation confirmed minimal optimistic bias (AUC 0.743). To enhance clinical interpretability, we applied SHapley Additive exPlanations (SHAP) analysis, which identified interleukin-6 (IL-6), brain-derived neurotrophic factor (BDNF), and vascular endothelial growth factor A (VEGF-A) as the most influential predictors. SHAP feature rankings were highly stable across cross-validation folds (mean Spearman ρ = 0.91). The robustness of these findings was confirmed through sensitivity analyses using extreme quartile comparisons (AUC 0.823), treatment-naïve subgroup analysis (AUC 0.738), and a clinically anchored outcome definition based on the minimal clinically important difference (AUC 0.739). A decision curve analysis demonstrated a net clinical benefit across threshold probabilities of 0.25–0.70. Our results establish targeted plasma protein profiling combined with interpretable machine learning as a promising tool for PD motor progression risk stratification, with potential applications in individualized patient counseling regarding motor prognosis and the selection of candidates for disease-modifying trials.

Purpose: Subcortical functional brain mapping is a valuable technique, especially involving lesions within or near white matter tracts. Motor and language deficits caused by intraoperative damage to these subcortical fibers are associated with worse outcomes. However, there are variable approaches to performing subcortical functional brain mapping. This systematic review was performed to better understand indications and optimal stimulation parameters. Methods: A systematic review was performed by PRISMA guidelines, using PubMed, Scopus, and Medline databases. Search dates ranged from inception to November 30th, 2024. Studies included adults who underwent subcortical electrical stimulation for functional brain mapping. Results: A total of 55 articles (249 patients, mean age 44.8 years, 114 female) were included. The most common pathology was brain tumor (96.3%), primarily gliomas and metastases, and 24.4% of all cases had a history of epilepsy. The most frequently mapped white matter tracts were the arcuate fasciculus, corticospinal/pyramidal tract, and inferior fronto-occipital fasciculus, involving motor, language, and cognitive functions. Bipolar stimulation (50–60 Hz, 0.5–1 millisecond, 1–10 mA) was preferred over monopolar stimulation. Among 147 patients with outcome data, 89.1% did not suffer permanent adverse outcomes including neurologic deficits, although long-term outcomes were inconsistently reported. Conclusions: The methodologic variability of subcortical functional brain mapping and lack of long-term follow-up reported limits determination of optimal parameter selection for electrical stimulation although common trends are reported. Functional brain mapping approaches may be guided by the tract and network involved. Further studies are needed to develop a standardized approach and more definitive guideline to its performance.

Freezing of gait (FoG) affects 38–65% of advanced Parkinson’s disease patients, yet automated detection algorithms are often validated solely on laboratory datasets. This study quantifies the critical performance gap between laboratory and real-world performance—a prerequisite for clinical deployment. Using temporal convolutional networks (TCNs), we trained models on two public datasets representing ecological extremes: a daily living dataset (Figshare; n = 35, single-sensor) and a laboratory dataset (DAPHNET; n = 10, multi-sensor). We compared five training configurations to address class imbalance. Results showed that F1-based early stopping outperformed Area Under the Curve (AUC)-based stopping by 47% (F1: 0.55 vs. 0.37, p = 0.0008). Combining multiple imbalance corrections (focal loss, weighting, sampling) paradoxically degraded precision to 33% due to a ~60-fold over-weighting of the minority class. Most importantly, cross-dataset validation revealed an 83% performance gap: laboratory F1 reached 0.9999 ± 0.0002, whereas daily living F1 dropped to 0.55 ± 0.26 (p < 0.0001), with a 1299-fold increase in variance. These findings demonstrate that laboratory success does not guarantee real-world utility. We propose that the observed gap represents a “deployment gap” reflecting the combined influence of environmental complexity, sensor constraints, and physiological variability. These results provide an empirical framework for evaluating deployment readiness of wearable FoG detection systems and offer concrete training strategy recommendations for clinical translation.

OBJECTIVE The aim of this study was to compare 3 electrode designs—a novel 22-contact circular grid, standard 6-contact strip, and high-density (HD) grid between 32 and 64 contacts—for the detection of epileptiform activity during awake craniotomy. METHODS This study included patients who underwent functional brain mapping with and without direct electrical stimulation during awake craniotomy. Demographic, clinical, and electrocorticography (ECoG) data were collected. RESULTS A total of 194 patients were included, with 264 instances of electrode use: 113 circular grid, 96 strip electrode, and 55 HD grid. The HD grid (15%) was used less to record ECoG during direct electrical stimulation than the circular grid (80%, p < 0.001) and strip electrodes (69%, p < 0.001). Sporadic interictal epileptiform activity was better detected with circular (45%, p < 0.001) and HD (40%, p = 0.006) grids compared with strip electrodes (16%). Spontaneous seizures were recorded more frequently with the circular grid (16%, p = 0.018) compared with the strip electrode (9.4%) and HD grid (1.8%). Afterdischarges were more frequently detected by the circular grid (69%, p = 0.031) and with circular and HD grids (combined) compared with strip electrodes (67% vs 48%, p = 0.024), at lower median intensity (3 mA vs 5 mA, p < 0.001). Multivariable analyses demonstrated circular grids (p < 0.001) to be more sensitive in detecting spontaneous epileptiform activity than strip and HD grid electrodes, and grids (circular and HD) to be better in recording stimulus-induced epileptiform activity (p = 0.01). CONCLUSIONS This study demonstrates a higher rate of detection of epileptiform activity with circular and standard grids when compared with strip electrodes. The standard HD grid was used less during electrical stimulation, demonstrating the importance of both the number of contacts and design array when considering optimal conditions for ECoG and functional brain mapping.

Deep brain stimulation (DBS) is an established therapeutic intervention for a range of neurological disorders, including Parkinson’s disease, essential tremor, and epilepsy. The efficacy of DBS is predicated on the modulation of dysfunctional neural circuits through the application of electrical pulses. Recent advancements in MRI technology have facilitated the visualization of brain nuclei and their associated networks, which is critical for proper patient-specific targeting of electrodes in surgical procedures. This advancement aims to personalize surgical planning and postoperative programming by integrating patient-specific anatomical and connectivity models. The advent of clinical ultrahigh-field MRI, particularly 7T MRI, significantly enhances the targeting of DBS by improving spatial resolution, signal-to-noise ratio, and tissue contrast. Compared with lower field strengths, 7T MRI provides superior visualization of deeply situated brain nuclei and their extensive cortical projections, including the subthalamic nucleus, globus pallidus internus, and thalamus. This review synthesizes a multi-institutional consensus regarding the technical and clinical applications of 7T MRI, drawing on experience from more than 1000 procedures using 7T DBS. It encompasses both imaging acquisition and postprocessing techniques aimed at optimizing image quality. Our extensive clinical experience informs best practices for correcting distortions, mitigating image artifacts, and employing specific imaging sequences that enhance the visualization of common DBS targets. The insights and recommendations presented are intended to promote the safe and effective utilization of 7T MRI in DBS.

This study evaluated the effectiveness of using a traditional computed tomography (CT) simulation scan in the Leksell Gamma Knife® Icon™ workflow for stereotactic radiosurgery. We compared two workflows, one involving a traditional CT simulation co-registered with the magnetic resonance imaging (MRI) and cone-beam CT (CBCT) (MRI-CT-CBCT) and another without the CT scan, co-registering the MRI directly with CBCT (MRI-CBCT). The cohort included 50 frame-based (54 targets) and 55 frameless (124 targets) patients treated between August 2020 and December 2022. Target coverage was assessed by the percentage of each target covered by the prescription isodose line. The mean coverage differences for the frame-based and frameless cohorts were −1.20% ± 1.43% and −1.06% ± 1.51%, respectively, with 95% confidence intervals of −1.59, −0.81 and −1.32, −0.79. The MRI-CBCT workflow was deemed noninferior to the MRI-CT-CBCT workflow. Our data indicate that both workflows yield similar results, suggesting that a traditional CT simulation scan is unnecessary.

BACKGROUND AND OBJECTIVES: Upper limb motor recovery is limited and affects functional outcomes in stroke survivors. Recent randomized controlled trials have assessed the role of combining vagus nerve stimulation with standard neurorehabilitation. The aim of this review was to evaluate the effectiveness of pairing implanted vagus nerve stimulation (I-VNS) with rehabilitation therapy in ischemic stroke patients and to provide a framework for its integration into standard clinical practice. METHODS: We searched PubMed, Scopus, CINAHL Plus, Cochrane Central Register of Controlled Trials, and Cochrane Database of Systematic Reviews for articles published from inception to July 2024. Randomized controlled trials and follow-up studies assessing the efficacy and/or safety of I-VNS in stroke rehabilitation were included. The outcomes included upper limb motor function and adverse events related to intervention. The risk of bias was assessed using the Cochrane risk-of-bias tool, and a random-effects model was generated for the meta-analysis using the Meta package in R. RESULTS: The final analysis included 8 studies, with a cumulative sample size of n = 498 patients who underwent I-VNS–paired rehabilitation for upper extremity weakness in unilateral supratentorial stroke. The intervention group (I-VNS) included 245 patients (49.1%) whereas the control group had 252 patients (50.6%). The mean age was 59.0 ± 10.01 years in the intervention group and 61.0 ± 9.7 years in the control group. The intervention group included 155 male patients (63.2%), and the control group had 165 male patients (64.4%). The meta-analyses showed statistically significant improvement in upper limb motor function in the I-VNS–paired rehabilitation group with a mean difference of 2.73 (95% CI: 1.32-4.13; P = .04) in the Fugl-Meyer Assessment for Upper Extremity score. Only one study reported a serious adverse event. CONCLUSION: A systematic review and meta-analysis of the current literature provides high-level evidence that I-VNS–paired rehabilitation improves functional outcomes and has a favorable safety profile in patients with poststroke upper extremity weakness.