Ripple oscillations in the left temporal neocortex are associated with impaired verbal episodic memory encoding

Background: We sought to determine if ripple oscillations (80-120Hz), detected in intracranial EEG (iEEG) recordings of epilepsy patients, correlate with an enhancement or disruption of verbal episodic memory encoding. Methods: We defined ripple and spike events in depth iEEG recordings during list learning in 107 patients with focal epilepsy. We used logistic regression models (LRMs) to investigate the relationship between the occurrence of ripple and spike events during word presentation and the odds of successful word recall following a distractor epoch, and included the seizure onset zone (SOZ) as a covariate in the LRMs. Results: We detected events during 58,312 word presentation trials from 7,630 unique electrode sites. The probability of ripple on spike (RonS) events was increased in the seizure onset zone (SOZ, p<0.04). In the left temporal neocortex RonS events during word presentation corresponded with a decrease in the odds ratio (OR) of successful recall, however this effect only met significance in the SOZ (OR of word recall 0.71, 95% CI: 0.59-0.85, n=158 events, adaptive Hochberg p<0.01). Ripple on oscillation events (RonO) that occurred in the left temporal neocortex non-SOZ also correlated with decreased odds of successful recall (OR 0.52, 95% CI: 0.34-0.80, n=140, adaptive Hochberg , p<0.01). Spikes and RonS that occurred during word presentation in the left middle temporal gyrus during word presentation correlated with the most significant decrease in the odds of successful recall, irrespective of the location of the SOZ (adaptive Hochberg, p<0.01). Conclusion: Ripples and spikes generated in left temporal neocortex are associated with impaired verbal episodic memory encoding

Subthalamic Nucleus Neurons Are Synchronized to Primary Motor Cortex Local Field Potentials in Parkinson's Disease

In Parkinson's disease (PD), striatal dopamine denervation results in a cascade of abnormalities in the single-unit activity of downstream basal ganglia nuclei that include increased firing rate, altered firing patterns, and increased oscillatory activity. However, the effects of these abnormalities on cortical function are poorly understood. Here, in humans undergoing deep brain stimulator implantation surgery, we use the novel technique of subdural electrocorticography in combination with subthalamic nucleus (STN) single-unit recording to study basal ganglia-cortex interactions at the millisecond time scale. We show that in patients with PD, STN spiking is synchronized with primary motor cortex (M1) local field potentials in two distinct patterns: first, STN spikes are phase-synchronized with M1 rhythms in the theta, alpha, or beta (4-30 Hz) bands. Second, STN spikes are synchronized with M1 gamma activity over a broad spectral range (50-200 Hz). The amplitude of STN spike-synchronized gamma activity in M1 is itself rhythmically modulated by the phase of a lower-frequency rhythm (phase-amplitude coupling), such that "waves" of phase-synchronized gamma activity precede the occurrence of STN spikes. We show the disease specificity of these phenomena in PD, by comparison with STN-M1 paired recordings performed in a group of patients with a different disorder, primary craniocervical dystonia. Our findings support a model of the basal ganglia-thalamocortical loop in PD in which gamma activity in primary motor cortex, modulated by the phase of low-frequency rhythms, drives STN unit discharge

Subthalamic Nucleus Neurons Are Synchronized to Primary Motor Cortex Local Field Potentials in Parkinson's Disease

In Parkinson’s disease (PD), striatal dopamine denervation results in a cascade of abnormalities in the single unit activity of downstream basal ganglia nuclei that include increased firing rate, altered firing patterns, and increased oscillatory activity. However, the effects of these abnormalities on cortical function are poorly understood. Here, in humans undergoing deep brain stimulator implantation surgery, we utilize the novel technique of subdural electrocorticography in combination with subthalamic nucleus (STN) single unit recording to study basal ganglia-cortex interactions at the millisecond time scale. We show that in patients with PD, STN spiking is synchronized with primary motor cortex (M1) local field potentials in two distinct patterns: First, STN spikes are phase-synchronized with M1 rhythms in the theta, alpha, or beta (4-30 Hz) bands. Second, STN spikes are synchronized with M1 gamma activity over a broad spectral range (50-200 Hz). The amplitude of STN spike-synchronized gamma activity in M1 is itself rhythmically modulated by the phase of a lower frequency rhythm (phase-amplitude coupling), such that “waves” of phase-synchronized gamma activity precede the occurrence of STN spikes. We show the disease specificity of these phenomena in PD, by comparison with STN-M1 paired recordings performed in a group of patients with a different disorder, primary cranio-cervical dystonia. Our findings support a model of the basal ganglia-thalamocortical loop in PD in which gamma activity in primary motor cortex, modulated by the phase of low frequency rhythms, drives STN unit discharge

A method for the topographical identification and quantification of high frequency oscillations in intracranial electroencephalography recordings

ObjectiveTo develop a reliable software method using a topographic analysis of time-frequency plots to distinguish ripple (80-200 Hz) oscillations that are often associated with EEG sharp waves or spikes (RonS) from sinusoid-like waveforms that appear as ripples but correspond with digital filtering of sharp transients contained in the wide bandwidth EEG.MethodsA custom algorithm distinguished true from false ripples in one second intracranial EEG (iEEG) recordings using wavelet convolution, identifying contours of isopower, and categorizing these contours into sets of open or closed loop groups. The spectral and temporal features of candidate groups were used to classify the ripple, and determine its duration, frequency, and power. Verification of detector accuracy was performed on the basis of simulations, and visual inspection of the original and band-pass filtered signals.ResultsThe detector could distinguish simulated true from false ripple on spikes (RonS). Among 2934 visually verified trials of iEEG recordings and spectrograms exhibiting RonS the accuracy of the detector was 88.5% with a sensitivity of 81.8% and a specificity of 95.2%. The precision was 94.5% and the negative predictive value was 84.0% (N = 12). Among, 1,370 trials of iEEG recording exhibiting RonS that were reviewed blindly without spectrograms the accuracy of the detector was 68.0%, with kappa equal to 0.01 ± 0.03. The detector successfully distinguished ripple from high spectral frequency 'fast ripple' oscillations (200-600 Hz), and characterize ripple duration and spectral frequency and power. The detector was confounded by brief bursts of gamma (30-80 Hz) activity in 7.31 ± 6.09% of trials, and in 30.2 ± 14.4% of the true RonS detections ripple duration was underestimated.ConclusionsCharacterizing the topographic features of a time-frequency plot generated by wavelet convolution is useful for distinguishing true oscillations from false oscillations generated by filter ringing.SignificanceCategorizing ripple oscillations and characterizing their properties can improve the clinical utility of the biomarker