Background: When two acoustic sinusoids of slightly different frequencies are presented in the same ear, the listener perceives a beating sound with a rate the difference frequency between the two sinusoids. In the dichotic presentation of two acoustic sinusoids with slightly different frequencies, separately at the two ears, the listener perceives beats at the difference frequency and these beats are called Binaural Beats. As opposed to the Monaural Beats the Binaural Beats require the combined action of the two ears in order to be perceived. Monaural and Binaural Beats can be assessed with EEG recordings of the Auditory Steady-Steady Response. While the Monaural Beats are already created at the level of the cochlea, the Binaural Beats are caused by bilateral inputs at higher levels of the auditory processing chain. This suggest that possible differences between the generators of the ASSRs elicited by Monaural and by Binaural Beats can help in the understanding of the binaural processing of sound. Objective: The objective of this study is to localize the generators of ASSRs evoked by MBs and by BBs, with the goal to detect possible spatial differences between their sources.
Methods: EEG recordings from 11 participants in response to multiple sinusoidal acoustic stimuli were used. Monaural beats, binaural beats, as well as other higher order monaural and binaural interactions between the input acoustic sinusoids were detected as ASSRs at the respective frequency of the beat or at the frequency of the other interactions. All the significant ASSRs were identified and then a spatial filtering technique was applied for the localization of their generators in the brain. The Dynamic Imaging of Coherent Sources (DICS) beamformer with left/right symmetric scanning dipoles was used to scan the whole brain and estimate the sources of the ASSRs. Results: The sources of the ASSRs evoked by monaural beats of 33Hz and 39Hz were identified in the auditory cortex. Maximum activation of the brain was found in the auditory cortex contralateral to the side of the monaural beat stimulus. ASSRs of 4 Hz Binaural Beat and 6Hz binaural interaction were localized in the right auditory cortex, slightly lower than the source of the Monaural Beats. Finally, sources of ASSRs evoked by other binaural beats and other monaural and binaural interactions were found in the following brain areas: temporal middle gyrus, parietal lobule, frontal gyrus, and precentral gyrus. Based on comparisons with results from other studies, the localization accuracy of subject-level source estimates was estimated 24.7mm. Conclusion: Generators of ASSRs evoked by monaural and binaural beats were estimated in the auditory cortex. The overall source results of the study suggest right-asymmetric contribution to the ASSR from the auditory cortex. Areas not belonging to the auditory system were also localized as sources of ASSRs. This suggests that brain areas outside the auditory system might be involved in the processing of the sound and sound perception. Finally, the results of this study were not enough to answer the question whether there are differences between the generators of ASSRs evoked by MBs and by BBs.

Background: Imaging in the field of epilepsy has been a lasting challenge. In genetic generalised epilepsy (GGE) patients, conventional neuroimaging methods such as the MRI often appear normal and even in focal epilepsy (FE) an obvious structural abnormality is not always visualised. The objective of this exploratory case study is to identify white and grey abnormalities in epilepsy patients based on diffusion weighted imaging in comparison with healthy subjects.
Methods: Diffusion tensor imaging (DTI) and diffusion tensor tractography (DTT) at 3 Tesla is performed on 2 genetic generalised epilepsy patients, 1 focal epilepsy patient and 2 healthy subjects. Four DTI metrics (Fractional Anisotropy (FA), Mean Diffusivity (MD), Radial Diffusivity (RD) and Axial Diffusivity (AD) are compared based on the mean per region of interest between subjects and between hemispheres. A DTT map of the thalamo-cortical radiations is constructed based on a probabilistic tractography (iFOD2) as implemented in MRTrix. Based on this data the structural connectivity matrix is generated. A graph theoretical analysis is performed comparing node degree, global and local efficiency, characteristic path length, (average) clustering coefficient and (average) betweenness centrality. Constructed connectomes are compared for significant different connections between nodes using network based statistics.
Results: Decreased FA and MD values are reported in the hippocampus of the FE patient. DTT showed decreased tract density in the thalamo-cortical radiations in one of the GGE patients and the FE patient, together with a decreased mean streamline length compared to the two healthy subjects, which can be interpreted as a reduced structural connectivity. Structural connectivity is decreased in the FE patient compared to the other four subjects based on graph analysis. This is demonstrated by the relatively high average clustering coefficient and characteristic path length, together with a low node degree, high local efficiency and high clustering coefficient in the thalamus, the basal ganglia and the hippocampus. Conclusion: These results are potentially of aid in identification of white and grey matter abnormalities in epilepsy patients. Especially in the FE brain several abnormalities in both DTI, DTT and graph analysis are observed compared to the healthy and GGE brain. However, for the use of DTI, DTT and graph analysis as a possible imaging biomarker, a standard processing pipeline with validated reference values ranges from the healthy population needs to be developed. Also the biological interpretation of changes in network measures needs to be validated.