It has been known for many years that the power of beta-band oscillatory activity in motor-related brain regions decreases during the preparation and execution of voluntary movements. However, it is not clear yet whether the amplitude of this desynchronization is modulated by any parameter of the motor task. Here, we examined whether the degree of uncertainty about the upcoming movement direction modulated beta-band desynchronization during motor preparation. To this end, we recorded whole-head neuromagnetic signals while human subjects performed an instructed-delay reaching task with one, two, or three possible target directions. We found that the reduction of power of beta-band activity (16 -28 Hz) during motor preparation was scaled relative to directional uncertainty. Furthermore, we show that the change of beta-band power correlates with the change of latency of response associated with response uncertainty. Finally, we show that the main source of beta-band desynchronization was located in the peri-Rolandic region. The results establish directional uncertainty as an important determinant of beta-band power during motor preparation and indicate that neural activity in the sensorimotor cortex during motor preparation covaries with directional uncertainty.

High-frequency oscillations in local field potentials recorded with intracranial EEG are putative biomarkers of seizure onset zones in epileptic brain. However, localized 80-500 Hz oscillations can also be recorded from normal and non-epileptic cerebral structures. When defined only by rate or frequency, physiological high-frequency oscillations are indistinguishable from pathological ones, which limit their application in epilepsy presurgical planning. We hypothesized that pathological high-frequency oscillations occur in a repetitive fashion with a similar waveform morphology that specifically indicates seizure onset zones. We investigated the waveform patterns of automatically detected high-frequency oscillations in 13 epilepsy patients and five control subjects, with an average of 73 subdural and intracerebral electrodes recorded per patient. The repetitive oscillatory waveforms were identified by using a pipeline of unsupervised machine learning techniques and were then correlated with independently clinician-defined seizure onset zones. Consistently in all patients, the stereotypical high-frequency oscillations with the highest degree of waveform similarity were localized within the seizure onset zones only, whereas the channels generating high-frequency oscillations embedded in random waveforms were found in the functional regions independent from the epileptogenic locations. The repetitive waveform pattern was more evident in fast ripples compared to ripples, suggesting a potential association between waveform repetition and the underlying pathological network. Our findings provided a new tool for the interpretation of pathological high-frequency oscillations that can be efficiently applied to distinguish seizure onset zones from functionally important sites, which is a critical step towards the translation of these signature events into valid clinical biomarkers.awx374media15721572971001.

Postoperative LFP activity can be recorded years after DBS implantation and demonstrates a similar profile in response to movement as during acute recordings, although amplitude may decrease. These results support the feasibility of constructing a closed-loop, patient-responsive DBS device based on LFP activity.

Background: Deep brain stimulation (DBS) for the treatment of movement disorders has provided researchers with an opportunity to record electrical oscillatory activity from electrodes implanted in deep brain structures. Extracellular activity recorded from a population of neurons, termed local field potentials (LFPs), has shed light on the pathophysiology of movement disorders and holds the potential to lead to refinement in existing treatments. Objective: This paper reviews the clinical significance of LFPs recorded from macroelectrodes implanted in basal ganglia and thalamic targets for the treatment of Parkinson's disease, essential tremor and dystonia. Methods: Neural population dynamics and subthreshold events, which are undetectable by single-unit recordings, can be examined with frequency band analysis of LFPs (frequency range: 1-250 Hz). Results: Of clinical relevance, reliable correlations between motor symptoms and components of the LFP power spectrum suggest that LFPs may serve as a biomarker for movement disorders. In particular, Parkinson's rigidity has been shown to correlate with the power of beta oscillations (13-30 Hz), and essential tremor coheres with oscillations of 8-27 Hz. Furthermore, evidence indicates that the optimal contacts for DBS programming can be predicted from the anatomic location of beta and gamma bands (48-200 Hz). Conclusion: LFP analysis has implications for improved electrode targeting and the development of a real-time, individualized, ‘closed-loop' stimulation system.