BackgroundAlthough recently introduced directional DBS leads provide control of the stimulation field, programing is time-consuming.ObjectivesHere, we validate local field potentials recorded from directional contacts as a predictor of the most efficient contacts for stimulation in patients with PD.MethodsIntraoperative local field potentials were recorded from directional contacts in the STN of 12 patients and beta activity compared with the results of the clinical contact review performed after 4 to 7 months.ResultsNormalized beta activity was positively correlated with the contact’s clinical efficacy. The two contacts with the highest beta activity included the most efficient stimulation contact in up to 92% and that with the widest therapeutic window in 74% of cases.ConclusionLocal field potentials predict the most efficient stimulation contacts and may provide a useful tool to expedite the selection of the optimal contact for directional DBS.

Misdiagnosis among tremor syndromes is common, and can impact on both clinical care and research. To date no validated neurophysiological technique is available that has proven to have good classification performance, and the diagnostic gold standard is the clinical evaluation made by a movement disorders expert. We present a robust new neurophysiological measure, the tremor stability index, which can discriminate Parkinson’s disease tremor and essential tremor with high diagnostic accuracy. The tremor stability index is derived from kinematic measurements of tremulous activity. It was assessed in a test cohort comprising 16 rest tremor recordings in tremor-dominant Parkinson’s disease and 20 postural tremor recordings in essential tremor, and validated on a second, independent cohort comprising a further 50 tremulous Parkinson’s disease and essential tremor recordings. Clinical diagnosis was used as gold standard. One hundred seconds of tremor recording were selected for analysis in each patient. The classification accuracy of the new index was assessed by binary logistic regression and by receiver operating characteristic analysis. The diagnostic performance was examined by calculating the sensitivity, specificity, accuracy, likelihood ratio positive, likelihood ratio negative, area under the receiver operating characteristic curve, and by cross-validation. Tremor stability index with a cut-off of 1.05 gave good classification performance for Parkinson’s disease tremor and essential tremor, in both test and validation datasets. Tremor stability index maximum sensitivity, specificity and accuracy were 95%, 95% and 92%, respectively. Receiver operating characteristic analysis showed an area under the curve of 0.916 (95% confidence interval 0.797–1.000) for the test dataset and a value of 0.855 (95% confidence interval 0.754–0.957) for the validation dataset. Classification accuracy proved independent of recording device and posture. The tremor stability index can aid in the differential diagnosis of the two most common tremor types. It has a high diagnostic accuracy, can be derived from short, cheap, widely available and non-invasive tremor recordings, and is independent of operator or postural context in its interpretation.

Transcranial focused ultrasound is an emerging technique for non-invasive neurostimulation. Compared to magnetic or electric non-invasive brain stimulation, this technique has a higher spatial resolution and can reach deep structures. In addition, both animal and human studies suggest that, potentially, different sites of the central and peripheral nervous system can be targeted by this technique. Depending on stimulation parameters, transcranial focused ultrasound is able to determine a wide spectrum of effects, ranging from suppression or facilitation of neural activity to tissue ablation. The aim is to review the state of the art of the human transcranial focused ultrasound neuromodulation literature, including the theoretical principles which underlie the explanation of the bioeffects on neural tissues, and showing the stimulation techniques and parameters used and their outcomes in terms of clinical, neurophysiological or neuroimaging results and safety.

BackgroundIn the last decades, several studies showed that wearable sensors, used for assessing Parkinson’s disease (PD) motor symptoms and recording their fluctuations, could provide a quantitative and reliable tool for patient’s motor performance monitoring.ObjectiveThe aim of this study is to make a step forward the capability of quantitatively describing PD motor symptoms. The specific aims are: identify the most sensible place where to locate sensors to monitor PD bradykinesia and rigidity, and identify objective indexes able to discriminate PD OFF/ON motor status, and PD patients from healthy subjects (HSs).MethodsFourteen PD patients (H&Y stage 1–2.5), and 13 age-matched HSs, were enrolled. Five magneto-inertial wearable sensors, placed on index finger, thumb, metacarpus, wrist, and arm, were used as motion tracking systems. Sensors were placed on the most affected arm of PD patients, and on dominant hand of HS. Three UPDRS part III tasks were evaluated: rigidity (task 22), finger tapping (task 23), and prono-supination movements of the hands (task 25). A movement disorders expert rated the three tasks according to the UPDRS part III scoring system. In order to describe each task, different kinematic indexes from sensors were extracted and analyzed.ResultsFour kinematic indexes were extracted: fatigability; total time; total power; smoothness. The last three well-described PD OFF/ON motor status, during finger-tapping task, with an index finger sensor. During prono-supination task, wrist sensor was able to differentiate PD OFF/ON motor condition. Smoothness index, used as a rigidity descriptor, provided a good discrimination of the PD OFF/ON motor status. Total power index, showed the best accuracy for PD vs healthy discrimination, with any sensor location among index finger, thumb, metacarpus, and wrist.ConclusionThe present study shows that, in order to better describe the kinematic features of Parkinsonian movements, wearable sensors should be placed on a distal location on upper limb, on index finger or wrist. The proposed indexes demonstrated a good correlation with clinical scores, thus providing a quantitative tool for research purposes in future studies in this field.

The aim of this review is to summarize that most relevant technologies used to evaluate gait features and the associated algorithms that have shown promise to aid diagnosis and symptom monitoring in Parkinson’s disease (PD) patients. We searched PubMed for studies published between 1 January 2005, and 30 August 2019 on gait analysis in PD. We selected studies that have either used technologies to distinguish PD patients from healthy subjects or stratified PD patients according to motor status or disease stages. Only those studies that reported at least 80% sensitivity and specificity were included. Gait analysis algorithms used for diagnosis showed a balanced accuracy range of 83.5–100%, sensitivity of 83.3–100% and specificity of 82–100%. For motor status discrimination the gait analysis algorithms showed a balanced accuracy range of 90.8–100%, sensitivity of 92.5–100% and specificity of 88–100%. Despite a large number of studies on the topic of objective gait analysis in PD, only a limited number of studies reported algorithms that were accurate enough deemed to be useful for diagnosis and symptoms monitoring. In addition, none of the reported algorithms and technologies has been validated in large scale, independent studies.

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Aim: To evaluate the effect of an acute L-dopa administration on eye-closed resting state electroencephalographic (EEG) activity of cognitively preserved Parkinsonian patients.Methods: We examined 24 right-handed patients diagnosed as uncomplicated probable Parkinson’s disease (PD). Each patient underwent Unified Parkinson’s Disease Rating Scale (UPDRS)-part-III evaluation before and 60 min after an oral load of L-dopa-methyl-ester/carbidopa 250/25 mg. Resting condition eyes-closed EEG data were recorded both pre- and post L-dopa load. Absolute EEG power values were calculated at each scalp derivation for Delta, Theta, Alpha and Beta frequency bands. UPDRS scores (both global and subscale scores) and EEG data (power values of different frequency bands for each scalp derivation) were submitted to a statistical analysis to compare Pre and Post L-Dopa conditions. Finally, a correlation analysis was carried out between EEG spectral content and UPDRS scores.Results: Considering EEG power spectral analysis, no statistically significant differences arose on Delta and Theta bands after L-dopa intake. Conversely, Alpha and Beta rhythms significantly increased on centro-parietal scalp derivations, as a function of L-dopa administration. Correlation analysis indicated a significant negative correlation between Beta power increase on centro-parietal areas and UPDRS subscores (Rigidity of arms and Bradykinesia). A minor significant negative correlation was also found between Alpha band increase and resting tremor.Conclusions: Assuming that a significant change in EEG power spectrum after L-dopa intake may be related to dopaminergic mechanisms, our findings are consistent with the hypothesis that dopaminergic defective networks are implicated in cortical oscillatory abnormalities at rest in non-demented PD patients.

Non-invasive brain stimulation (NIBS) has been under investigation as adjunct treatment of various neurological disorders with variable success. One challenge is the limited knowledge on what would be effective neuronal targets for an intervention, combined with limited knowledge on the neuronal mechanisms of NIBS. Motivated on the one hand by recent evidence that oscillatory activities in neural systems play a role in orchestrating brain functions and dysfunctions, in particular those of neurological disorders specific of elderly patients, and on the other hand that NIBS techniques may be used to interact with these brain oscillations in a controlled way, we here explore the potential of modulating brain oscillations as an effective strategy for clinical NIBS interventions. We first review the evidence for abnormal oscillatory profiles to be associated with a range of neurological disorders of elderly (e.g., Parkinson’s disease (PD), Alzheimer’s disease (AD), stroke, epilepsy), and for these signals of abnormal network activity to normalize with treatment, and/or to be predictive of disease progression or recovery. We then ask the question to what extent existing NIBS protocols have been tailored to interact with these oscillations and possibly associated dysfunctions. Our review shows that, despite evidence for both reliable neurophysiological markers of specific oscillatory dis-functionalities in neurological disorders and NIBS protocols potentially able to interact with them, there are few applications of NIBS aiming to explore clinical outcomes of this interaction. Our review article aims to point out oscillatory markers of neurological, which are also suitable targets for modification by NIBS, in order to facilitate in future studies the matching of technical application to clinical targets.