Spectral characteristics of subthalamic nucleus local field potentials in Parkinson's disease: Phenotype and movement matter.

Parkinson's disease (PD) is clinically heterogeneous across patients and may be classified in three motor phenotypes: tremor dominant (TD), postural instability and gait disorder (PIGD), and undetermined. Despite the significant clinical characterization of motor phenotypes, little is known about how electrophysiological data, particularly subthalamic nucleus local field potentials (STN-LFP), differ between TD and PIGD patients. This is relevant since increased STN-LFP bandpower at α-ß range (8-35 Hz) is considered a potential PD biomarker and, therefore, a critical setpoint to drive adaptive deep brain stimulation. Acknowledging STN-LFP differences between phenotypes, mainly in rest and movement states, would better fit DBS to clinical and motor demands. We studied this issue through spectral analyses on 35 STN-LFP in TD and PIGD patients during rest and movement. We demonstrated that higher ß2 activity (22-35 Hz) was observed in PIGD only during rest. Additionally, bandpower differences between rest and movement occurred at the α-ß range, but with different patterns as per phenotypes: movement-induced desynchronization concerned lower frequencies in TD (10-20 Hz) and higher frequencies in PIGD patients (21-28 Hz). Finally, when supervised learning algorithms were employed aiming to discriminate PD phenotypes based on STN-LFP bandpower features, movement information had improved the classification accuracy, achieving peak performances when TD and PIGD movement-induced desynchronization ranges were considered. These results suggest that STN-LFP ß-band encodes phenotype-movement dependent information in PD patients.

Subthalamic low beta bursts differ in Parkinson's disease phenotypes.

OBJECTIVE: Parkinson's disease (PD) patients may be categorized into tremor-dominant (TD) and postural-instability and gait disorder (PIGD) motor phenotypes, but the dynamical aspects of subthalamic nucleus local field potentials (STN-LFP) and the neural correlates of this phenotypical classification remain unclear. METHODS: 35 STN-LFP (20 PIGD and 15 TD) were investigated through continuous wavelet transform and machine-learning-based methods. The beta oscillation - the main band associated with motor impairment in PD - dynamics was characterized through beta burst parameters across phenotypes and burst intervals under specific proposed criteria for optimal burst threshold definition. RESULTS: Low-frequency (13-22 Hz) beta burst probability was the best predictor for PD phenotypes (75% accuracy). PIGD patients presented higher average burst duration (p = 0.018), while TD patients exhibited higher burst probability (p = 0.014). Categorization into shorter and longer than 400 ms bursts led to significant interaction between burst length categories and the phenotypes (p < 0.050) as revealed by mixed-effects models. Long burst durations and short bursts probability positively correlated, respectively, with rigidity-bradykinesia (p = 0.029) and tremor (p = 0.038) scores. CONCLUSIONS: Subthalamic low-frequency beta bursts differed between TD and PIGD phenotypes and correlated with motor symptoms. SIGNIFICANCE: These findings improve the PD phenotypes' electrophysiological characterization and may define new criteria for adaptive deep brain stimulation.

Space-time filter for SSVEP brain-computer interface based on the minimum variance distortionless response.

Brain-computer interfaces (BCI) based on steady-state visually evoked potentials (SSVEP) have been increasingly used in different applications, ranging from entertainment to rehabilitation. Filtering techniques are crucial to detect the SSVEP response since they can increase the accuracy of the system. Here, we present an analysis of a space-time filter based on the Minimum Variance Distortionless Response (MVDR). We have compared the performance of a BCI-SSVEP using the MVDR filter to other classical approaches: Common Average Reference (CAR) and Canonical Correlation Analysis (CCA). Moreover, we combined the CAR and MVDR techniques, totalling four filtering scenarios. Feature extraction was performed using Welch periodogram, Fast Fourier transform, and CCA (as extractor) with one and two harmonics. Feature selection was performed by forward wrappers, and a linear classifier was employed for discrimination. The main analyses were carried out over a database of ten volunteers, considering two cases: four and six visual stimuli. The results show that the BCI-SSVEP using the MVDR filter achieves the best performance among the analysed scenarios. Interestingly, the system's accuracy using the MVDR filter is practically constant even when the number of visual stimuli was increased, whereas degradation was observed for the other techniques.

Fields of Forel Brain Stimulation Improves Levodopa-Unresponsive Gait and Balance Disorders in Parkinson's Disease.

BACKGROUND: Gait and balance disturbance are challenging symptoms in advanced Parkinson's disease (PD). Anatomic and clinical data suggest that the fields of Forel may be a potential surgical target to treat these symptoms. OBJECTIVE: To test whether bilateral stimulation centered at the fields of Forel improves levodopa unresponsive freezing of gait (FOG), balance problems, postural instability, and falls in PD. METHODS: A total of 13 patients with levodopa-unresponsive gait disturbance (Hoehn and Yahr stage ≥3) were included. Patients were evaluated before (on-medication condition) and 1 yr after surgery (on-medication-on-stimulation condition). Motor symptoms and quality of life were assessed with the Unified Parkinson's Disease Rating scale (UPDRS III) and Quality of Life scale (PDQ-39). Clinical and instrumented analyses assessed gait, balance, postural instability, and falls. RESULTS: Surgery improved balance by 43% (95% confidence interval [CI]: 21.2-36.4 to 35.2-47.1; P = .0012), reduced FOG by 35% (95% CI: 15.1-20.3 to 8.1-15.3; P = .0021), and the monthly number of falls by 82.2% (95% CI: 2.2-6.9 to -0.2-1.7; P = .0039). Anticipatory postural adjustments, velocity to turn, and postural sway measurements also improved 1 yr after deep brain stimulation (DBS). UPDRS III motor scores were reduced by 27.2% postoperatively (95% CI: 42.6-54.3 to 30.2-40.5; P < .0001). Quality of life improved 27.5% (95% CI: 34.6-48.8 to 22.4-37.9; P = .0100). CONCLUSION: Our results suggest that DBS of the fields of Forel improved motor symptoms in PD, as well as the FOG, falls, balance, postural instability, and quality of life.

The temporal stability of recurrence quantification analysis attributes from chronic atrial fibrillation electrograms

Abstract Introduction The temporal behavior of atrial electrograms (AEGs) collected during persistent atrial fibrillation (persAF) directly affects ablative treatment outcomes. We investigated different durations of AEGs collected during persAF using recurrence quantification analysis (RQA). Methods 797 bipolar AEGs with different durations (from 0.5 s to 8 s) from 18 patients were investigated. Four RQA-based attributes were evaluated based on AEG durations: determinism (DET); recurrence rate (RR); laminarity (LAM); and diagonal lines' entropy (ENTR). The Spearman correlation (ρ) between each duration versus 8 s was calculated. AEG classification was performed following the CARTO criteria (Biosense Webster) and receiving operating characteristic (ROC) curves were created for the RQA variables. Results The RQA variables successfully discriminated the AEGs: the area under the ROC curves were as high as 0.70 for AEGs with 3.5 s or greater. Three types of AEGs were found using these variables: normal, fractionated and temporally unstable. The number of unstable AEGs decreased with longer AEG segments. Different AEG durations significantly affected the RQA variables (P<0.0001), with no statistical difference between the durations 6 s, 7 s and 8 s for DET, LAM and ENTR, and no difference between 7 s and 8 s for RR (P<0.0001). AEGs with 3 s or longer have shown ρ ≥ 80% for all variables. Conclusion The RQA variables have been shown effective in the characterization of AEGs collected during persAF with a shorter duration than current recommendations, which motivates their use for the characterization of atrial substrate during persAF ablation.