Visualizing spikes in source-space: Rapid and efficient evaluation of magnetoencephalography

Absolute pitch: neurophysiological evidence for early brain activity in prefrontal cortex

Absolute pitch (AP) is the ability to rapidly label pitch without an external reference. The speed of AP labeling may be related to faster sensory processing. We compared time needed for auditory processing in AP musicians, non-AP musicians, and nonmusicians (NM) using high-density electroencephalographic recording. Participants responded to pure tones and sung voice. Stimuli evoked a negative deflection peaking at ~100 ms (N1) post-stimulus onset, followed by a positive deflection peaking at ~200 ms (P2). N1 latency was shortest in AP, intermediate in non-AP musicians, and longest in NM. Source analyses showed decreased auditory cortex and increased frontal cortex contributions to N1 for complex tones compared with pure tones. Compared with NM, AP musicians had weaker source currents in left auditory cortex but stronger currents in left inferior frontal gyrus (IFG) during N1, and stronger currents in left IFG during P2. Compared with non-AP musicians, AP musicians exhibited stronger source currents in right insula and left IFG during N1, and stronger currents in left IFG during P2. Non-AP musicians had stronger N1 currents in right auditory cortex than nonmusicians. Currents in left IFG and left auditory cortex were correlated to response times exclusively in AP. Findings suggest a left frontotemporal network supports rapid pitch labeling in AP.

EEG-fMRI: Ballistocardiogram Artifact Reduction by Surrogate Method for Improved Source Localization

For the analysis of simultaneous EEG-fMRI recordings, it is vital to use effective artifact removal tools. This applies in particular to the ballistocardiogram (BCG) artifact which is difficult to remove without distorting signals of interest related to brain activity. Here, we documented the use of surrogate source models to separate the artifact-related signals from brain signals with minimal distortion of the brain activity of interest. The artifact topographies used for surrogate separation were created automatically using principal components analysis (PCA-S) or by manual selection of artifact components utilizing independent components analysis (ICA-S). Using real resting-state data from 55 subjects superimposed with simulated auditory evoked potentials (AEP), both approaches were compared with three established BCG artifact removal methods: Blind Source Separation (BSS), Optimal Basis Set (OBS), and a mixture of both (OBS-ICA). Each method was evaluated for its applicability for ERP and source analysis using the following criteria: the number of events surviving artifact threshold scans, signal-to-noise ratio (SNR), error of source localization, and signal variance explained by the dipolar model. Using these criteria, PCA-S and ICA-S fared best overall, with highly significant differences to the established methods, especially in source localization. The PCA-S approach was also applied to a single subject Berger experiment performed in the MRI scanner. Overall, the removal of BCG artifacts by the surrogate methods provides a substantial improvement for the analysis of simultaneous EEG-fMRI data compared to the established methods.

Sensitivity of a 29-Channel MEG Source Montage

In this paper, we study the performance of a source montage corresponding to 29 brain regions reconstructed from whole-head magnetoencephalographic (MEG) recordings, with the aim of facilitating the review of MEG data containing epileptiform discharges. Test data were obtained by superposing simulated signals from 100-nAm dipolar sources to a resting state MEG recording from a healthy subject. Simulated sources were placed systematically to different cortical locations for defining the optimal regularization for the source montage reconstruction and for assessing the detectability of the source activity from the 29-channel MEG source montage. The signal-to-noise ratio (SNR), computed for each source from the sensor-level and source-montage signals, was used as the evaluation parameter. Without regularization, the SNR from the simulated sources was larger in the sensor-level signals than in the source montage reconstructions. Setting the regularization to 2% increased the source montage SNR to the same level as the sensor-level SNR, improving the detectability of the simulated events from the source montage reconstruction. Sources producing a SNR of at least 15 dB were visually detectable from the source-montage signals. Such sources are located closer than about 75 mm from the MEG sensors, in practice covering all areas in the grey matter. The 29-channel source montage creates more focal signals compared to the sensor space and can significantly shorten the detection time of epileptiform MEG discharges for focus localization.

Neural dynamics supporting auditory long-term memory effects on target detection

Auditory long-term memory has been shown to facilitate signal detection. However, the nature and timing of the cognitive processes supporting such benefits remain equivocal. We measured neuroelectric brain activity while young adults were presented with a contextual memory cue designed to assist with the detection of a faint pure tone target embedded in an audio clip of an everyday environmental scene (e.g., the soundtrack of a restaurant). During an initial familiarization task, participants heard such audio clips, half of which included a target sound (memory cue trials) at a specific time and location (left or right ear), as well as audio clips without a target (neutral trials). Following a 1-h or 24-h retention interval, the same audio clips were presented, but now all included a target. Participants were asked to press a button as soon as they heard the pure tone target. Overall, participants were faster and more accurate during memory than neutral cue trials. The auditory contextual memory effects on performance coincided with three temporally and spatially distinct neural modulations, which encompassed changes in the amplitude of event-related potential as well as changes in theta, alpha, beta and gamma power. Brain electrical source analyses revealed greater source activity in memory than neutral cue trials in the right superior temporal gyrus and left parietal cortex. Conversely, neutral trials were associated with greater source activity than memory cue trials in the left posterior medial temporal lobe. Target detection was associated with increased negativity (N2), and a late positive (P3b) wave at frontal and parietal sites, respectively. The effect of auditory contextual memory on brain activity preceding target onset showed little lateralization. Together, these results are consistent with contextual memory facilitating retrieval of target-context associations and deployment and management of auditory attentional resources to when the target occurred. The results also suggest that the auditory cortices, parietal cortex, and medial temporal lobe may be parts of a neural network enabling memory-guided attention during auditory scene analysis.

A novel method for extracting interictal epileptiform discharges in multi-channel MEG: Use of fractional type of blind source separation

OBJECTIVE

Visual inspection of interictal epileptiform discharges (IEDs) in multi-channel MEG requires a time-consuming evaluation process and often leads to inconsistent results due to variability of IED waveforms. Here, we propose a novel extraction method for IEDs using a T/k type of blind source separation (BSS_T/k).

METHODS

We applied BSS_T/k with seven patients with focal epilepsy to test the accuracy of identification of IEDs. We conducted comparisons of the results of BSS components with those obtained by visual inspection in sensor-space analysis.

RESULTS

BSS_T/k provided better signal estimation of IEDs compared with sensor-space analysis. Importantly, BSS_T/k was able to uncover IEDs that could not be detected by visual inspection. Furthermore, IED components were clearly extracted while preserving spike and wave morphology. Variable IED waveforms were decomposed into one dominant component.

CONCLUSIONS

BSS_T/k was able to visualize the spreading signals over multiple channels into a single component from a single epileptogenic zone. BSS_T/k can be applied to focal epilepsy with a simple parameter setting.

SIGNIFICANCE

Our novel method was able to highlight IEDs with increased accuracy for identification of IEDs from multi-channel MEG data.

Visual analysis of high density EEG: As good as electrical source imaging?

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Visual analysis of HD-EEG is an excellent tool to explore the epileptogenic focus.

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ESI remains the gold standard for presurgical evaluation of the cortical source.

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ESI at 50% slope/ESI at peak discordance could predict worse surgical outcome.

Objective

In this study, we sought to determine whether visual analysis of high density EEG (HD-EEG) would provide similar localizing information comparable to electrical source imaging (ESI).

Methods

HD-EEG (256 electrodes) recordings from 20 patients suffering from unifocal, drug-resistant epilepsy (13 women, mean age 29.1 ± 2.62 years, 11 with temporal lobe epilepsy) were examined. In the visual analysis condition, we identified the 5 contacts with maximal spike amplitude and determined their localization with respect to the underlying cortex. ESI was computed using the LAURA algorithm of the averaged spikes in the patient’s individual MRI. We considered the localization “correct” if all 5 contacts were concordant with the resection volume underneath or if ESI was located within the resection as determined by the postoperative MRI.

Results

Twelve patients were postoperatively seizure-free (Engel Class IA), while the remaining eight were in class IB to IV. Visual analysis and ESI showed sensitivity of 58% and 75%, specificity of 75% and 87%, and accuracy of 65% and 80%, respectively. In 70% of cases, visual analysis and ESI provided concordant results.

Conclusions

Localization of the electrodes with maximal spike amplitude provides very good estimation of the localization of the underlying source. However, ESI has a higher accuracy and adds 3D information; therefore, it should remain the tool of choice for presurgical evaluation.

Significance

The present study proposes the possibility to analyze HD-EEG visually, in tandem with ESI or alone, if ESI is not accessible.

Taking the EEG Back Into the Brain: The Power of Multiple Discrete Sources

Background: In contrast to many neuroimaging modalities, clinical interpretation of EEG does not take advantage of post-processing and digital signal analysis. In most centers, EEG is still interpreted at sensor level, exactly as half a century ago. A major task in clinical EEG interpretation is the identification of interictal epileptiform discharges (IEDs). However, due to the overlap of background activity, IEDs can be hard to detect in the scalp EEG. Since traditional montages, like bipolar and average reference, are linear transformations of the recorded channels, the question is whether we can provide linear transformations of the digital EEG to take it back into the brain, at least on a macroscopic level. The goal is to improve visibility of epileptiform activities and to separate out most of the overlap. Methods: Multiple discrete sources provide a stable linear inverse to transform the EEG into source space with little cross-talk between source regions. The model can be based on a few dipoles or regional sources, adapted to the individual EEG and MRI data, or on selected standard sources evenly distributed throughout the brain, e.g. below the 25 EEG standard electrodes. Results: Auditory and somatosensory evoked potentials serve as teaching examples to show how various source spaces can reveal the underlying source components including their loss or alteration due to lesions. Source spaces were able to reveal the propagation of source activities in frontal IEDs and the sequential activation of the major nodes of the underlying epileptic network in myoclonic epilepsy. The power of multiple discrete sources in separating the activities of different brain regions was also evident in the ongoing EEG of cases with frontal cortical dysplasia and bitemporal lobe epilepsy. The new source space 25 made IEDs more clearly visible over the EEG background signals. The more focal nature of source vs. scalp space was quantitatively confirmed using a new measurement of focality. Conclusion: Multiple discrete sources have the power to transform the EEG back into the brain by defining new EEG traces in source space. Using standard source space 25, these can provide for improved clinical interpretation of EEG.