Attentional Modulation of the Cortical Contribution to the Frequency-Following Response Evoked by Continuous Speech.

Selective attention to one of several competing speakers is required for comprehending a target speaker among other voices and for successful communication with them. It moreover has been found to involve the neural tracking of low-frequency speech rhythms in the auditory cortex. Effects of selective attention have also been found in subcortical neural activities, in particular regarding the frequency-following response related to the fundamental frequency of speech (speech-FFR). Recent investigations have, however, shown that the speech-FFR contains cortical contributions as well. It remains unclear whether these are also modulated by selective attention. Here we used magnetoencephalography to assess the attentional modulation of the cortical contributions to the speech-FFR. We presented both male and female participants with two competing speech signals and analyzed the cortical responses during attentional switching between the two speakers. Our findings revealed robust attentional modulation of the cortical contribution to the speech-FFR: the neural responses were higher when the speaker was attended than when they were ignored. We also found that, regardless of attention, a voice with a lower fundamental frequency elicited a larger cortical contribution to the speech-FFR than a voice with a higher fundamental frequency. Our results show that the attentional modulation of the speech-FFR does not only occur subcortically but extends to the auditory cortex as well.SIGNIFICANCE STATEMENT Understanding speech in noise requires attention to a target speaker. One of the speech features that a listener can use to identify a target voice among others and attend it is the fundamental frequency, together with its higher harmonics. The fundamental frequency arises from the opening and closing of the vocal folds and is tracked by high-frequency neural activity in the auditory brainstem and in the cortex. Previous investigations showed that the subcortical neural tracking is modulated by selective attention. Here we show that attention affects the cortical tracking of the fundamental frequency as well: it is stronger when a particular voice is attended than when it is ignored.

Comprehensive 7 T CEST: A clinical MRI protocol covering multiple exchange rate regimes.

Chemical exchange saturation transfer (CEST) is a magnetic resonance (MR) imaging method providing molecular image contrasts based on indirect detection of low concentrated solutes. Previous CEST studies focused predominantly on the imaging of single CEST exchange regimes (e.g., slow, intermediate or fast exchanging groups). In this work, we aim to establish a so-called comprehensive CEST protocol for 7 T, covering the different exchange regimes by three saturation B1 amplitude regimes: low, intermediate and high. We used the results of previous publications and our own simulations in pulseq-CEST to produce a 7 T CEST protocol that has sensitivity to these three B1 regimes. With postprocessing optimization (simultaneous mapping of water shift and B1, B0-fitting, multiple interleaved mode saturation B1 correction, neural network employment (deepCEST) and analytical input feature reduction), we are able to shorten our initially 40 min protocol to 15 min and generate six CEST contrast maps simultaneously. With this protocol, we measured four healthy subjects and one patient with a brain tumor. We established a comprehensive CEST protocol for clinical 7 T MRI, covering three different B1 amplitude regimes. We were able to reduce the acquisition time significantly by more than 50%, while still maintaining decent image quality and contrast in healthy subjects and one patient with a tumor. Our protocol paves the way to perform comprehensive CEST studies in clinical scan times for hypothesis generation regarding molecular properties of certain pathologies, for example, ischemic stroke or high-grade brain tumours.

Surgical hematoma evacuation of cortical intracerebral hemorrhage ≥10 ml reduces risk of subsequent epilepsy by more than 70%: A retrospective monocenter study.

AIM: The aim of this study was to re-evaluate risk factors for post-ICH epilepsy (PICHE) and examine the impact of surgical hematoma evacuation on epilepsy development after ICH. BACKGROUND AND PURPOSE: Epilepsy is a common complication after intracerebral hemorrhage (ICH). Information on risk factors is still scarce and the role of ICH evacuation remains uncertain. METHODS: We retrospectively included patients with spontaneous ICH treated in our hospital in 2006-2019. Patients' medical records were analyzed. In addition, mailed questionnaires and telephone interviews were used to complete the dataset. Uni- and multivariable hazard ratios (HRs) were applied to investigate risk factors for PICHE and the impact of surgical ICH evacuation. RESULTS: Among 587 ICH patients available for analyses, 139 (23.7%) developed PICHE (mean follow-up 1795 ± 1378 days). The median time of epilepsy onset was 7 months after ICH (range 1-132 months). Risk factors associated with PICHE were cortical hemorrhage (multivariable HR 1.65 [95% CI 1.14-2.37]; p = 0.008), ICH volume > 10 ml (multivariable HR 1.91 [95% CI 1.33-2.73]; p < 0.001) and acute symptomatic seizures (multivariable HR 1.81 [95% CI 1.20-2.75]; p = 0.005). Patients with cortical ICH > 10 ml who underwent surgical hematoma evacuation were less likely to develop epilepsy than those with conservative treatment alone (multivariable HR 0.26 [95% CI 0.08-0.84]; p = 0.025). CONCLUSIONS: Post-ICH epilepsy is frequent and predicted by large cortical ICH and acute symptomatic seizures. Hematoma evacuation reduced the risk of PICHE by more than 70% in patients with large cortical ICH. This finding could be considered in the clinical decision making on the acute treatment of ICH.

Neural networks for estimation of facial palsy after vestibular schwannoma surgery.

PURPOSE: Facial nerve damage in vestibular schwannoma surgery is associated with A-train patterns in free-running EMG, correlating with the degree of postoperative facial palsy. However, anatomy, preoperative functional status, tumor size and occurrence of A-trains clusters, i.e., sudden A-trains in most channels may further contribute. In the presented study, we examine neural networks to estimate postoperative facial function based on such features. METHODS: Data from 200 consecutive patients were used to train neural feed-forward networks (NN). Estimated and clinical postoperative House and Brackmann (HB) grades were compared. Different input sets were evaluated. RESULTS: Networks based on traintime, preoperative HB grade and tumor size achieved good estimation of postoperative HB grades (chi2 = 54.8), compared to using tumor size or mean traintime alone (chi2 = 30.6 and 31.9). Separate intermediate nerve or detection of A-train clusters did not improve performance. Removal of A-train cluster traintime improved results (chi2 = 54.8 vs. 51.3) in patients without separate intermediate nerve. CONCLUSION: NN based on preoperative HB, traintime and tumor size provide good estimations of postoperative HB. The method is amenable to real-time implementation and supports integration of information from different sources. NN could enable multimodal facial nerve monitoring and improve postoperative outcomes.

Spectral fingerprints or spectral tilt? Evidence for distinct oscillatory signatures of memory formation.

Decreases in low-frequency power (2-30 Hz) alongside high-frequency power increases (>40 Hz) have been demonstrated to predict successful memory formation. Parsimoniously, this change in the frequency spectrum can be explained by one factor, a change in the tilt of the power spectrum (from steep to flat) indicating engaged brain regions. A competing view is that the change in the power spectrum contains several distinct brain oscillatory fingerprints, each serving different computations. Here, we contrast these two theories in a parallel magnetoencephalography (MEG)-intracranial electroencephalography (iEEG) study in which healthy participants and epilepsy patients, respectively, studied either familiar verbal material or unfamiliar faces. We investigated whether modulations in specific frequency bands can be dissociated in time and space and by experimental manipulation. Both MEG and iEEG data show that decreases in alpha/beta power specifically predicted the encoding of words but not faces, whereas increases in gamma power and decreases in theta power predicted memory formation irrespective of material. Critically, these different oscillatory signatures of memory encoding were evident in different brain regions. Moreover, high-frequency gamma power increases occurred significantly earlier compared to low-frequency theta power decreases. These results show that simple "spectral tilt" cannot explain common oscillatory changes and demonstrate that brain oscillations in different frequency bands serve different functions for memory encoding.

Web-based decision support system for patient-tailored selection of antiseizure medication in adolescents and adults: An external validation study.

BACKGROUND AND PURPOSE: Antiseizure medications (ASMs) should be tailored to individual characteristics, including seizure type, age, sex, comorbidities, comedications, drug allergies, and childbearing potential. We previously developed a web-based algorithm for patient-tailored ASM selection to assist health care professionals in prescribing medication using a decision support application (https://epipick.org). In this validation study, we used an independent dataset to assess whether ASMs recommended by the algorithm are associated with better outcomes than ASMs considered less desirable by the algorithm. METHODS: Four hundred twenty-five consecutive patients with newly diagnosed epilepsy were followed for at least 1 year after starting an ASM chosen by their physician. Patient characteristics were fed into the algorithm, blinded to the physician's ASM choices and outcome. The algorithm recommended ASMs, ranked in hierarchical groups, with Group 1 ASMs labeled as the best option for that patient. We evaluated retention rates, seizure freedom rates, and adverse effects leading to treatment discontinuation. Survival analysis contrasted outcomes between patients who received favored drugs and those who received lower ranked drugs. Propensity score matching corrected for possible imbalances between the groups. RESULTS: Antiseizure medications classified by the algorithm as best options had a higher retention rate (79.4% vs. 67.2%, p = 0.005), higher seizure freedom rate (76.0% vs. 61.6%, p = 0.002), and lower rate of discontinuation due to adverse effects (12.0% vs. 29.2%, p < 0.001) than ASMs ranked as less desirable by the algorithm. CONCLUSIONS: Use of the freely available decision support system is associated with improved outcomes. This drug selection application can provide valuable assistance to health care professionals prescribing medication for individuals with epilepsy.

Learning from EMG: semi-automated grading of facial nerve function.

The current grading of facial nerve function is based on subjective impression with the established assessment scale of House and Brackmann (HB). Especially for research a more objective method is needed to lower the interobserver variability to a minimum. We developed a semi-automated grading system based on (facial) surface EMG-data measuring the facial nerve function of 28 patients with vestibular schwannoma surgery. The sEMG was recorded preoperatively, postoperatively and after 3-12 months. In addition, the HB grade was determined. After manual selection and preprocessing, the data were subjected to machine learning classificators (Logistic regression, SVM and KNN). Lateralization indices were calculated and multivariant machine learning analysis was performed according to three scenarios [differentiation of normal (1) and slight (2) vs. impaired facial nerve function and classification of HB 1-3 (3)]. The calculated AUC for each scenario showed overall good differentiation capability with a median AUC of 0.72 for scenario 1, 0.91 for scenario 2 and multiclass AUC of 0.74 for scenario 3. This study approach using sEMG and machine learning shows feasibility regarding facial nerve grading in perioperative VS-surgery setting. sEMG may be a viable alternative to House Brackmann regarding objective evaluation of facial function especially for research purposes.

Optimal choice of antiseizure medication: Agreement among experts and validation of a web-based decision support application.

OBJECTIVE: Optimal choice of antiseizure medication (ASM) depends on seizure type, syndrome, age, gender, comorbidities and co-medications. There are no fixed rules on how to weigh these factors; choices are subjective and experience-driven. We investigated agreement among experts in selecting ASM as monotherapy and used their prevailing choices to validate a web-based decision-support application. METHODS: Twenty-four international experts, blinded to the app, selected the optimal ASM for 25 individual patient-cases covering a wide variation of seizure types and other factors influencing ASM selection. The app ranked ASMs in order of likely appropriateness for each case. In a second step, experts rated anonymously the choices of the app. RESULTS: Of the 25 patient-cases (age 13-74 years), 13 were female, 18 (72%) had comorbidities, six (24%) were on contraceptives, and 13 (52%) had other co-medications. The median number of experts who selected the same ASM for a given case was 15 (62.5%) and interquartile range (IQR) 13-18 (54%-75%). Gwet's agreement coefficient among experts was 0.38 (95% confidence interval [CI] 0.32-0.44), corresponding to a "fair" agreement. Agreement between the app and the prevailing expert choice for each case was 0.48 (95% CI 0.29-0.67), corresponding to a "moderate" beyond chance agreement. The percent agreement between the highest ranked selections of the app and the expert selections was 73% (95% CI 64%-82%). Ninety-five percent of the experts considered that no incorrect or potentially harmful ASMs were ranked highest by the app, and most experts strongly agreed with the app's selections. SIGNIFICANCE: This app, now validated by experts, provides an objective, reproducible method for selecting ASM that accounts for relevant clinical features. It is freely available at: https://epipick.org.

A web-based algorithm to rapidly classify seizures for the purpose of drug selection.

OBJECTIVE: To develop and validate a pragmatic algorithm that classifies seizure types, to facilitate therapeutic decision-making. METHODS: Using a modified Delphi method, five experts developed a pragmatic classification of nine types of epileptic seizures or combinations of seizures that influence choice of medication, and constructed a simple algorithm, freely available on the internet. The algorithm consists of seven questions applicable to patients with seizure onset at the age of 10 years or older. Questions to screen for nonepileptic attacks were added. Junior physicians, nurses, and physician assistants applied the algorithm to consecutive patients in a multicenter prospective validation study (ClinicalTrials.gov identifier: NCT03796520). The reference standard was the seizure classification by expert epileptologists, based on all available data, including electroencephalogram (EEG), video-EEG monitoring, and neuroimaging. In addition, physicians working in underserved areas assessed the feasibility of using the web-based algorithm in their clinical setting. RESULTS: A total of 262 patients were assessed, of whom 157 had focal, 51 had generalized, and 10 had unknown onset epileptic seizures, and 44 had nonepileptic paroxysmal events. Agreement between the algorithm and the expert classification was 83.2% (95% confidence interval = 78.6%-87.8%), with an agreement coefficient (AC1) of .82 (95% confidence interval = .77-.87), indicating almost perfect agreement. Thirty-two health care professionals from 14 countries evaluated the feasibility of the web-based algorithm in their clinical setting, and found it applicable and useful for their practice (median = 6.5 on 7-point Likert scale). SIGNIFICANCE: The web-based algorithm provides an accurate classification of seizure types, which can be used for selecting antiseizure medications in adolescents and adults.

Prophylactic nimodipine treatment improves hearing outcome after vestibular schwannoma surgery in men: a subgroup analysis of a randomized multicenter phase III trial.

A 2016 published randomized multicenter phase III trial of prophylactic nimodipine treatment in vestibular schwannoma surgery showed only a tendency for higher hearing preservation rates in the treatment group. Gender was not included in statistical analysis at that time. A retrospective analysis of the trial considering gender, preoperative hearing, and nimodipine treatment was performed. The treatment group received parenteral nimodipine from the day before surgery until the seventh postoperative day. The control group was not treated prophylactically. Cochlear nerve function was determined by pure-tone audiometry with speech discrimination preoperatively, during in-patient care, and 1 year after surgery and classified according to the Gardner-Robertson grading scale (GR). Logistic regression analysis showed a statistically significant effect for higher hearing preservation rates (pre- and postoperative GR 1-4) in 40 men comparing the treatment (n = 21) and the control (n = 19) groups (p = 0.028), but not in 54 women comparing 27 women in both groups (p = 0.077). The results were also statistically significant for preservation of postoperative hearing with pre- and postoperative GR 1-3 (p = 0.024). There were no differences in tumor sizes between the treatment and the control groups in men, whereas statistically significant larger tumors were observed in the female treatment group compared with the female control group. Prophylactic nimodipine is safe, and an effect for hearing preservation in 40 men with preoperative hearing ability of GR 1-4 was shown in this retrospective investigation. The imbalance in tumor size with larger tumors in females of the treatment group may falsely suggest a gender-related effect. Further investigations are recommended to clarify whether gender has impact on nimodipine's efficacy.

Inter-Subject Variability of Skull Conductivity and Thickness in Calibrated Realistic Head Models.

Skull conductivity has a substantial influence on EEG and combined EEG and MEG source analysis as well as on optimized transcranial electric stimulation. To overcome the use of standard literature values, we propose a non-invasive two-level calibration procedure to estimate skull conductivity individually in a group study with twenty healthy adults. Our procedure requires only an additional run of combined somatosensory evoked potential and field data, which can be easily integrated in EEG/MEG experiments. The calibration procedure uses the P20/N20 topographies and subject-specific realistic head models from MRI. We investigate the inter-subject variability of skull conductivity and relate it to skull thickness, age and gender of the subjects, to the individual scalp P20/N20 surface distance between the P20 potential peak and the N20 potential trough as well as to the individual source depth of the P20/N20 source. We found a considerable inter-subject variability for (calibrated) skull conductivity (8.44 ± 4.84 mS/m) and skull thickness (5.97 ± 1.19 mm) with a statistically significant correlation between them (rho = 0.52). Age showed a statistically significant negative correlation with skull conductivity (rho = -0.5). Furthermore, P20/N20 surface distance and source depth showed large inter-subject variability of 12.08 ± 3.21 cm and 15.45 ± 4.54 mm, respectively, but there was no significant correlation between them. We also found no significant differences among gender subgroups for the investigated measures. It is thus important to take the inter-subject variability of skull conductivity and thickness into account by means of using subject-specific calibrated realistic head modeling.

A pragmatic algorithm to select appropriate antiseizure medications in patients with epilepsy.

OBJECTIVE: Antiseizure medications (ASMs) are the first-line treatment for epilepsy. Many ASMs are available; this offers the opportunity to improve therapy by tailoring it to individual characteristics, but also increases the possibility of healthcare professionals making inappropriate treatment choices. To assist healthcare professionals, we developed a pragmatic algorithm aimed at facilitating medication selection for individuals whose epilepsy begins at age 10 years and older. METHODS: Utilizing available evidence and a Delphi panel-based consensus process, a group of epilepsy experts developed an algorithm for selection of ASMs, depending on the seizure type(s) and the presence of relevant clinical variables (age, gender, comorbidities, and comedications). The algorithm was implemented into a web-based application that was tested and improved in an iterative process. RESULTS: The algorithm categorizes ASMs deemed to be appropriate for each seizure type or combination of seizure types into three groups, with group 1 ASMs considered preferred, group 2 considered second line, and group 3 considered third line. Depending on the presence of relevant clinical variables, the ranking of individual ASMs is adjusted in the prioritization scheme to tailor recommendations to the characteristics of the individual. The algorithm is available on a web-based application at: https://epipick.org/#/. SIGNIFICANCE: The proposed algorithm is user-friendly, requires less than 2 minutes to complete, and provides the user with a range of appropriate treatment options from which to choose. This should facilitate its broad utilization and contribute to improve epilepsy management for healthcare providers who desire advice, particularly those who lack special expertise in the field.

Magnetoencephalography for epileptic focus localization in a series of 1000 cases.

The aim of epilepsy surgery in patients with focal, pharmacoresistant epilepsies is to remove the complete epileptogenic zone to achieve long-term seizure freedom. In addition to a spectrum of diagnostic methods, magnetoencephalography focus localization is used for planning of epilepsy surgery. We present results from a retrospective observational cohort study of 1000 patients, evaluated using magnetoencephalography at the University Hospital Erlangen over the time span of 28 years. One thousand consecutive cases were included in the study, evaluated at the University Hospital Erlangen between 1990 and 2018. All patients underwent magnetoencephalography as part of clinical workup for epilepsy surgery. Of these, 405 underwent epilepsy surgery after magnetoencephalography, with postsurgical follow-ups of up to 20 years. Sensitivity for interictal epileptic activity was evaluated, in addition to concordance of localization with the consensus of presurgical workup on a lobar level. We evaluate magnetoencephalography characteristics of patients who underwent epilepsy surgery versus patients who did not proceed to surgery. In operated patients, resection of magnetoencephalography localizations were related to postsurgical seizure outcomes, including long-term results after several years. In comparison, association of lesionectomy with seizure outcomes was analysed. Measures of diagnostic accuracy were calculated for magnetoencephalography resection and lesionectomy. Sensitivity for interictal epileptic activity was 72% with significant differences between temporal and extra-temporal lobe epilepsy. Magnetoencephalography was concordant with the presurgical consensus in 51% and showed additional or more focal involvement in an additional 32%. Patients who proceeded to surgery showed a significantly higher percentage of monofocal magnetoencephalography results. Complete magnetoencephalography resection was associated with significantly higher chances to achieve seizure freedom in the short and long-term. Diagnostic accuracy was significant in temporal and extra-temporal lobe cases, but was significantly higher in extra-temporal lobe epilepsy (diagnostic odds ratios of 4.4 and 41.6). Odds ratios were also higher in non-lesional versus lesional cases (42.0 versus 6.2). The results show that magnetoencephalography provides non-redundant information, which significantly contributes to patient selection, focus localization and ultimately long-term seizure freedom after epilepsy surgery. Specifically in extra-temporal lobe epilepsy and non-lesional cases, magnetoencephalography provides excellent accuracy.

The effect of stimulation type, head modeling, and combined EEG and MEG on the source reconstruction of the somatosensory P20/N20 component.

Modeling and experimental parameters influence the Electro- (EEG) and Magnetoencephalography (MEG) source analysis of the somatosensory P20/N20 component. In a sensitivity group study, we compare P20/N20 source analysis due to different stimulation type (Electric-Wrist [EW], Braille-Tactile [BT], or Pneumato-Tactile [PT]), measurement modality (combined EEG/MEG - EMEG, EEG, or MEG) and head model (standard or individually skull-conductivity calibrated including brain anisotropic conductivity). Considerable differences between pairs of stimulation types occurred (EW-BT: 8.7 ± 3.3 mm/27.1° ± 16.4°, BT-PT: 9 ± 5 mm/29.9° ± 17.3°, and EW-PT: 9.8 ± 7.4 mm/15.9° ± 16.5° and 75% strength reduction of BT or PT when compared to EW) regardless of the head model used. EMEG has nearly no localization differences to MEG, but large ones to EEG (16.1 ± 4.9 mm), while source orientation differences are non-negligible to both EEG (14° ± 3.7°) and MEG (12.5° ± 10.9°). Our calibration results show a considerable inter-subject variability (3.1-14 mS/m) for skull conductivity. The comparison due to different head model show localization differences smaller for EMEG (EW: 3.4 ± 2.4 mm, BT: 3.7 ± 3.4 mm, and PT: 5.9 ± 6.8 mm) than for EEG (EW: 8.6 ± 8.3 mm, BT: 11.8 ± 6.2 mm, and PT: 10.5 ± 5.3 mm), while source orientation differences for EMEG (EW: 15.4° ± 6.3°, BT: 25.7° ± 15.2° and PT: 14° ± 11.5°) and EEG (EW: 14.6° ± 9.5°, BT: 16.3° ± 11.1° and PT: 12.9° ± 8.9°) are in the same range. Our results show that stimulation type, modality and head modeling all have a non-negligible influence on the source reconstruction of the P20/N20 component. The complementary information of both modalities in EMEG can be exploited on the basis of detailed and individualized head models.

High-frequency oscillations: The state of clinical research.

Modern electroencephalographic (EEG) technology contributed to the appreciation that the EEG signal outside the classical Berger frequency band contains important information. In epilepsy, research of the past decade focused particularly on interictal high-frequency oscillations (HFOs) > 80 Hz. The first large application of HFOs was in the context of epilepsy surgery. This is now followed by other applications such as assessment of epilepsy severity and monitoring of antiepileptic therapy. This article reviews the evidence on the clinical use of HFOs in epilepsy with an emphasis on the latest developments. It highlights the growing literature on the association between HFOs and postsurgical seizure outcome. A recent meta-analysis confirmed a higher resection ratio for HFOs in seizure-free versus non-seizure-free patients. Residual HFOs in the postoperative electrocorticogram were shown to predict epilepsy surgery outcome better than preoperative HFO rates. The review further discusses the different attempts to separate physiological from epileptic HFOs, as this might increase the specificity of HFOs. As an example, analysis of sleep microstructure demonstrated a different coupling between HFOs inside and outside the epileptogenic zone. Moreover, there is increasing evidence that HFOs are useful to measure disease activity and assess treatment response using noninvasive EEG and magnetoencephalography. This approach is particularly promising in children, because they show high scalp HFO rates. HFO rates in West syndrome decrease after adrenocorticotropic hormone treatment. Presence of HFOs at the time of rolandic spikes correlates with seizure frequency. The time-consuming visual assessment of HFOs, which prevented their clinical application in the past, is now overcome by validated computer-assisted algorithms. HFO research has considerably advanced over the past decade, and use of noninvasive methods will make HFOs accessible to large numbers of patients. Prospective multicenter trials are awaited to gather information over long recording periods in large patient samples.

Towards an optimal paradigm for intraoperative auditory nerve monitoring with auditory steady state responses.

Auditory steady state responses (ASSR) may offer an alternative to brainstem auditory evoked potentials for monitoring of the auditory nerve during surgical procedures. In the current study, we evaluated the influence of noise on ASSR characteristics in total intravenous anesthesia (TIVA). Simulated ASSR in real noise recorded during surgery under TIVA were constructed with known parameters. Influence of amplitude, modulation frequency, averaging sweeps and detection threshold on ASSR were evaluated. High amplitude, more sweeps and a liberal threshold facilitated detection. High amplitude ASSR (80 nV) were detected in up to 45 % with 16 s of data, in 80-90 % with 112 s. Near-threshold ASSR were detected in 0.8-25 %. False positives ranged between 0.3 and 10.3 %. Number of sweeps did not influence false positives. Amplitude errors varied between -61 and +39 % and improved with more averages but not with different thresholds. Modulation rate demonstrated the strongest influence on all parameters. 110 Hz yielded best, 90 Hz the worst results. Choice of parameters strongly influences detection and characteristics of ASSR. Optimal parameters enabled detection after 16 s in 45 %. Due to specific noise characteristics, modulation has a critical impact, which is currently not sufficiently recognized in ASSR studies.

Slow-theta power decreases during item-place encoding predict spatial accuracy of subsequent context recall.

Human hippocampal theta oscillations play a key role in accurate spatial coding. Associative encoding involves similar hippocampal networks but, paradoxically, is also characterized by theta power decreases. Here, we investigated how theta activity relates to associative encoding of place contexts resulting in accurate navigation. Using MEG, we found that slow-theta (2-5Hz) power negatively correlated with subsequent spatial accuracy for virtual contextual locations in posterior hippocampus and other cortical structures involved in spatial cognition. A rare opportunity to simultaneously record MEG and intracranial EEG in an epilepsy patient provided crucial insights: during power decreases, slow-theta in right anterior hippocampus and left inferior frontal gyrus phase-led the left temporal cortex and predicted spatial accuracy. Our findings indicate that decreased slow-theta activity reflects local and long-range neural mechanisms that encode accurate spatial contexts, and strengthens the view that local suppression of low-frequency activity is essential for more efficient processing of detailed information.

A guideline for head volume conductor modeling in EEG and MEG.

For accurate EEG/MEG source analysis it is necessary to model the head volume conductor as realistic as possible. This includes the distinction of the different conductive compartments in the human head. In this study, we investigated the influence of modeling/not modeling the conductive compartments skull spongiosa, skull compacta, cerebrospinal fluid (CSF), gray matter, and white matter and of the inclusion of white matter anisotropy on the EEG/MEG forward solution. Therefore, we created a highly realistic 6-compartment head model with white matter anisotropy and used a state-of-the-art finite element approach. Starting from a 3-compartment scenario (skin, skull, and brain), we subsequently refined our head model by distinguishing one further of the above-mentioned compartments. For each of the generated five head models, we measured the effect on the signal topography and signal magnitude both in relation to a highly resolved reference model and to the model generated in the previous refinement step. We evaluated the results of these simulations using a variety of visualization methods, allowing us to gain a general overview of effect strength, of the most important source parameters triggering these effects, and of the most affected brain regions. Thereby, starting from the 3-compartment approach, we identified the most important additional refinement steps in head volume conductor modeling. We were able to show that the inclusion of the highly conductive CSF compartment, whose conductivity value is well known, has the strongest influence on both signal topography and magnitude in both modalities. We found the effect of gray/white matter distinction to be nearly as big as that of the CSF inclusion, and for both of these steps we identified a clear pattern in the spatial distribution of effects. In comparison to these two steps, the introduction of white matter anisotropy led to a clearly weaker, but still strong, effect. Finally, the distinction between skull spongiosa and compacta caused the weakest effects in both modalities when using an optimized conductivity value for the homogenized compartment. We conclude that it is highly recommendable to include the CSF and distinguish between gray and white matter in head volume conductor modeling. Especially for the MEG, the modeling of skull spongiosa and compacta might be neglected due to the weak effects; the simplification of not modeling white matter anisotropy is admissible considering the complexity and current limitations of the underlying modeling approach.