Tommaso Fedele

Pre-stimulus Alpha Oscillations and Inter-subject Variability of Motor Evoked Potentials in Single- and Paired-Pulse TMS Paradigms

Inter- and intra-subject variability of the motor evoked potentials (MEPs) to TMS is a well-known phenomenon. Although a possible link between this variability and ongoing brain oscillations was demonstrated, the results of the studies are not consistent with each other. Exploring this topic further is important since the modulation of MEPs provides unique possibility to relate oscillatory cortical phenomena to the state of the motor cortex probed with TMS. Given that alpha oscillations were shown to reflect cortical excitability, we hypothesized that their power and variability might explain the modulation of subject-specific MEPs to single- and paired-pulse TMS (spTMS, ppTMS, respectively). Neuronal activity was recorded with multichannel electroencephalogram. We used spTMS and two ppTMS conditions: intracortical facilitation (ICF) and short-interval intracortical inhibition (SICI). Spearman correlations were calculated within and across subjects between MEPs and the pre-stimulus power of alpha oscillations in low (8–10 Hz) and high (10–12 Hz) frequency bands. Coefficient of quartile variation was used to measure variability. Across-subject analysis revealed no difference in the pre-stimulus alpha power among the TMS conditions. However, the variability of high-alpha power in spTMS condition was larger than in the SICI condition. In ICF condition pre-stimulus high-alpha power variability correlated positively with MEP amplitude variability. No correlation has been observed between the pre-stimulus alpha power and MEP responses in any of the conditions. Our results show that the variability of the alpha oscillations can be more predictive of TMS effects than the commonly used power of oscillations and we provide further support for the dissociation of high and low-alpha bands in predicting responses produced by the stimulation of the motor cortex.

Monochromatic Ultra-Slow (~ 0.1 Hz) Oscillations in the human electroencephalogram and their relation to hemodynamics

Previous studies demonstrated the presence of Monochromatic Ultra-Slow Oscillations (MUSO) in human EEG. In the present study we explored the biological origin of MUSO by simultaneous recordings of EEG, Near-Infrared Spectroscopy (NIRS), arterial blood pressure, respiration and Laser Doppler flowmetry. We used a head-up tilt test in order to check whether MUSO might relate to Mayer waves in arterial blood pressure, known to be enhanced by the tilting procedure. MUSO were detected in 8 out of 10 subjects during rest and showed a striking monochromatic spectrum (0.07-0.14 Hz). The spatial topography of MUSO was complex, showing multiple foci variable across subjects. While the head-up tilt test increased the relative power of Mayer waves, it had no effect on MUSO. On the other hand, the relative spectral power of 0.1 Hz oscillations in EEG, NIRS and blood pressure signals were positively correlated across subjects in the tilted condition. Eight subjects showed a coherence between MUSO and NIRS/arterial blood pressure. Moreover, MUSO at different electrode sites demonstrated coherence not reducible to volume conduction, thus indicating that MUSO are unlikely to be generated by one source. We related our experimental findings to known biological phenomena being generated at about 0.1 Hz, i.e.: arterial blood pressure, cerebral and skin vasomotion, respiration and neuronal activity. While no definite conclusion can yet be drawn as to an exact physiological mechanism of MUSO, we suggest that these oscillations might be of a rather extraneuronal origin reflecting cerebral vasomotion.

Automatic detection of high frequency oscillations during epilepsy surgery predicts seizure outcome

OBJECTIVE High frequency oscillations (HFOs) and in particular fast ripples (FRs) in the post-resection electrocorticogram (ECoG) have recently been shown to be highly specific predictors of outcome of epilepsy surgery. FR visual marking is time consuming and prone to observer bias. We validate here a fully automatic HFO detector against seizure outcome. METHODS Pre-resection ECoG dataset (N=14 patients) with visually marked HFOs were used to optimize the detectors parameters in the time-frequency domain. The optimized detector was then applied on a larger post-resection ECoG dataset (N=54) and the output was compared with visual markings and seizure outcome. The analysis was conducted separately for ripples (80-250Hz) and FRs (250-500Hz). RESULTS Channel-wise comparison showed a high association between automatic detection and visual marking (p<0.001 for both FRs and ripples). Automatically detected FRs were predictive of clinical outcome with positive predictive value PPV=100% and negative predictive value NPV=62%, while for ripples PPV=43% and NPV=100%. CONCLUSIONS Our automatic and fully unsupervised detection of HFO events matched the expert observers performance in both event selection and outcome prediction. SIGNIFICANCE The detector provides a standardized definition of clinically relevant HFOs, which may spread its use in clinical application.

Magnetic resonance imaging at frequencies below 1 kHz

Within the magnetic resonance imaging (MRI) community the trend is going to higher and higher magnetic fields, ranging from 1.5 T to 7 T, corresponding to Larmor frequencies of 63.8-298 MHz. Since for high-field MRI the magnetization increases with the applied magnetic field, the signal-to-noise-ratio increases as well, thus enabling higher image resolutions. On the other hand, MRI is possible also at ultra-low magnetic fields, as was shown by different groups. The goal of our development was to reach a Larmor frequency range of the low-field MRI system corresponding to the frequency range of human brain activities ranging from near zero-frequency (near-DC) to over 1 kHz. Here, first 2D MRI images of phantoms taken at Larmor frequencies of 100 Hz and 731 Hz will be shown and discussed. These frequencies are examples of brain activity triggered by electrostimulation of the median nerve. The method will allow the magnetic fields of the brain currents to influence the magnetic resonance image, and thus lead to a direct functional imaging modality of neuronal currents.

Extension of non-invasive EEG into the kHz range for evoked thalamocortical activity by means of very low noise amplifiers

Ultrafast electroencephalographic signals, having frequencies above 500 Hz, can be observed in somatosensory evoked potential measurements. Usually, these recordings have a poor signal-to-noise ratio (SNR) because weak signals are overlaid by intrinsic noise of much higher amplitude like that generated by biological sources and the amplifier. As an example, recordings at the scalp taken during electrical stimulation of the median nerve show a 600 Hz burst with submicro-volt amplitudes which can be extracted from noise by the use of massive averaging and digital signal processing only. We have investigated this signal by means of a very low noise amplifier made in-house (minimal voltage noise 2.7 nV Hz(-1/2), FET inputs). We examined how the SNR of the data is altered by the bandwidth and the use of amplifiers with different intrinsic amplifier noise levels of 12 and 4.8 nV Hz(-1/2), respectively. By analyzing different frequency contributions of the signal, we found an extremely weak 1 kHz component superimposed onto the well-known 600 Hz burst. Previously such high-frequency electroencephalogram responses around 1 kHz have only been observed by deep brain electrodes implanted for tremor therapy of Parkinson patients. For the non-invasive measurement of such signals, we recommend that amplifier noise should not exceed 4 nV Hz(-1/2).

Are high-frequency (600 Hz) oscillations in human somatosensory evoked potentials due to phase-resetting phenomena?

OBJECTIVE Median nerve somatosensory evoked potentials (SEP) contain a brief oscillatory wavelet burst at about 600 Hz (σ-burst) superimposed on the initial cortical component (N20). While invasive single-cell recordings suggested that this burst is generated by increased neuronal spiking activity in area 3b, recent non-invasive scalp recordings could not reveal concomitant single-trial added-activity, suggesting that the SEP burst might instead be generated by phase-reset of ongoing high-frequency EEG. Here, a statistical model and exemplary data are presented reconciling these seemingly contradictory results. METHODS A statistical model defined the conditions required to detect added-activity in a set of single-trial SEP. Its predictions were tested by analyzing human single-trial scalp SEP recorded with custom-made low-noise amplifiers. RESULTS The noise level in previous studies did not allow to detect single-trial added-activity in the period concomitant with the trial-averaged σ-burst. In contrast, optimized low-noise recordings do reveal added-activity in a set of single-trials. CONCLUSIONS The experimental noise level is the decisive factor determining the detectability of added-activity in single-trials. A low-noise experiment provided direct evidence that the SEP σ-burst is at least partly generated by added-activity matching earlier invasive single-cell recordings. SIGNIFICANCE Quantitative criteria are provided for the feasibility of single-trial detectability of band-limited added-activity.

Non-invasive single-trial EEG detection of evoked human neocortical population spikes

QUESTION Human high-frequency (>400 Hz) components of somatosensory evoked potentials (hf-SEPs), which can be recorded non-invasively at the scalp, are generated by cortical population spikes, as inferred from microelectrode recordings in non-human primates. It is a critical limitation to broader neurophysiological study of hf-SEPs in that hundreds of responses have to be averaged to detect hf-SEPs reliably. Here, we establish a framework for detecting human hf-SEPs non-invasively in single trials. METHODS Spatio-temporal features were extracted from band-pass filtered (400-900 Hz) hf-SEPs by bilinear Common Spatio-Temporal Patterns (bCSTP) and then classified by a weighted Extreme Learning Machine (w-ELM). The effect of varying signal-to-noise ratio (SNR), number of trials, and degree of w-ELM re-weighting was characterized using surrogate data. For practical demonstration of the algorithm, median nerve hf-SEPs were recorded inside a shielded room in four subjects, spanning the hf-SEP signal-to-noise ratio characteristic for a larger population, utilizing a custom-built 29-channel low-noise EEG amplifier. RESULTS Using surrogate data, the SNR proved to be pivotal to detect hf-SEPs in single trials efficiently, with the trade-off between sensitivity and specificity of the algorithm being obtained by the w-ELM re-weighting parameter. In practice, human hf-SEPs were detected non-invasively in single trials with a sensitivity of up to 99% and a specificity of up to 97% in two subjects, even without any recourse to knowledge of stimulus timing. Matching with the results of the surrogate data analysis, these rates dropped to 62-79% sensitivity and 18-31% specificity in two subjects with lower SNR. CONCLUSIONS Otherwise buried in background noise, human high-frequency EEG components can be extracted from low-noise recordings. Specifically, refined supervised filter optimization and classification enables the reliable detection of single-trial hf-SEPs, representing non-invasive correlates of cortical population spikes. SIGNIFICANCE While low-frequency EEG reflects summed postsynaptic potentials, and thereby neuronal input, we suggest that high-frequency EEG (>400 Hz) can provide non-invasive access to the unaveraged output of neuronal computation, i.e., single-trial population spike activity evoked in the responsive neuronal ensemble.

Long-Range Temporal Correlations in the amplitude of alpha oscillations predict and reflect strength of intracortical facilitation: Combined TMS and EEG study

While variability of the motor responses to transcranial magnetic stimulation (TMS) is widely acknowledged, little is known about its central origin. One plausible explanation for such variability may relate to different neuronal states defining the reactivity of the cortex to TMS. In this study intrinsic spatio-temporal neuronal dynamics were estimated with Long-Range Temporal Correlations (LRTC) in order to predict the inter-individual differences in the strength of intra-cortical facilitation (ICF) and short-interval intracortical inhibition (SICI) produced by paired-pulse TMS (ppTMS) of the left primary motor cortex. LRTC in the alpha frequency range were assessed from multichannel electroencephalography (EEG) obtained at rest before and after the application of and single-pulse TMS (spTMS) and ppTMS protocols. For the EEG session, preceding TMS application, we showed a positive correlation across subjects between the strength of ICF and LRTC in the fronto-central and parietal areas. This in turn attests to the existence of subject-specific neuronal phenotypes defining the reactivity of the brain to ppTMS. In addition, we also showed that ICF was associated with the changes in neuronal dynamics in the EEG session after the application of the stimulation. This result provides a complementary evidence for the recent findings demonstrating that the cortical stimulation with sparse non-regular stimuli might have considerable long-lasting effects on the cortical activity.

Resection of high frequency oscillations predicts seizure outcome in the individual patient

High frequency oscillations (HFOs) are recognized as biomarkers for epileptogenic brain tissue. A remaining challenge for epilepsy surgery is the prospective classification of tissue sampled by individual electrode contacts. We analysed long-term invasive recordings of 20 consecutive patients who subsequently underwent epilepsy surgery. HFOs were defined prospectively by a previously validated, automated algorithm in the ripple (80–250 Hz) and the fast ripple (FR, 250–500 Hz) frequency band. Contacts with the highest rate of ripples co-occurring with FR over several five-minute time intervals designated the HFO area. The HFO area was fully included in the resected area in all 13 patients who achieved seizure freedom (specificity 100%) and in 3 patients where seizures reoccurred (negative predictive value 81%). The HFO area was only partially resected in 4 patients suffering from recurrent seizures (positive predictive value 100%, sensitivity 57%). Thus, the resection of the prospectively defined HFO area proved to be highly specific and reproducible in 13/13 patients with seizure freedom, while it may have improved the outcome in 4/7 patients with recurrent seizures. We thus validated the clinical relevance of the HFO area in the individual patient with an automated procedure. This is a prerequisite before HFOs can guide surgical treatment in multicentre studies.

Prediction of seizure outcome improved by fast ripples detected in low-noise intraoperative corticogram

OBJECTIVE Fast ripples (FR, 250-500Hz) in the intraoperative corticogram have recently been proposed as specific predictors of surgical outcome in epilepsy patients. However, online FR detection is restricted by their low signal-to-noise ratio. Here we propose the integration of low-noise EEG with unsupervised FR detection. METHODS Pre- and post-resection ECoG (N=9 patients) was simultaneously recorded by a commercial device (CD) and by a custom-made low-noise amplifier (LNA). FR were analyzed by an automated detector previously validated on visual markings in a different dataset. RESULTS Across all recordings, in the FR band the background noise was lower in LNA than in CD (p<0.001). FR rates were higher in LNA than CD recordings (0.9±1.4 vs 0.4±0.9, p<0.001). Comparison between FR rates in post-resection ECoG and surgery outcome resulted in positive predictive value PPV=100% in CD and LNA, and negative predictive value NPV=38% in CD and NPV=50% for LNA. Prediction accuracy was 44% for CD and 67% for LNA. CONCLUSIONS Prediction of seizure outcome was improved by the optimal integration of low-noise EEG and unsupervised FR detection. SIGNIFICANCE Accurate, automated and fast FR rating is essential for consideration of FR in the intraoperative setting.