Identifying temporal correlations between natural single-shot videos and EEG signals

Objective.Electroencephalography (EEG) is a widely used technology for recording brain activity in brain-computer interface (BCI) research, where understanding the encoding-decoding relationship between stimuli and neural responses is a fundamental challenge. Recently, there is a growing interest in encoding-decoding natural stimuli in a single-trial setting, as opposed to traditional BCI literature where multi-trial presentations of synthetic stimuli are commonplace. While EEG responses to natural speech have been extensively studied, such stimulus-following EEG responses to natural video footage remain underexplored.Approach.We collect a new EEG dataset with subjects passively viewing a film clip and extract a few video features that have been found to be temporally correlated with EEG signals. However, our analysis reveals that these correlations are mainly driven by shot cuts in the video. To avoid the confounds related to shot cuts, we construct another EEG dataset with natural single-shot videos as stimuli and propose a new set of object-based features.Main results.We demonstrate that previous video features lack robustness in capturing the coupling with EEG signals in the absence of shot cuts, and that the proposed object-based features exhibit significantly higher correlations. Furthermore, we show that the correlations obtained with these proposed features are not dominantly driven by eye movements. Additionally, we quantitatively verify the superiority of the proposed features in a match-mismatch task. Finally, we evaluate to what extent these proposed features explain the variance in coherent stimulus responses across subjects.Significance.This work provides valuable insights into feature design for video-EEG analysis and paves the way for applications such as visual attention decoding.

Molecular diagnosis of polymicrobial brain abscesses with 16S-rDNA-based next-generation sequencing

OBJECTIVES

Brain abscesses lead to high mortality despite antibiotic and surgical treatment. Identification of causative bacteria is important to guide antibiotic therapy, but culture-based methods and molecular diagnostics by Sanger sequencing of 16S PCR products are hampered by antibiotic treatment and the often polymicrobial nature of brain abscesses. We have applied 16S-rRNA-based next-generation sequencing (NGS) for metagenomic analysis of intracranial abscess (brain and epidural) and meningitis samples.

METHODS

Seventy-nine samples from 54 patients with intracranial abscesses or meningitis were included. DNA was subjected to 16S PCR. Amplicons were analysed with the Illumina MiSeq system, sequence reads were blasted versus the NCBI 16S bacterial database and analysed using MEGAN software. Results were compared to those of gram-staining, culture and Sanger sequencing.

RESULTS

The NGS workflow was successful for 51 intracranial abscesses (46 brain and five epidural) and nine meningitis samples. Inclusion of (mono)bacterial meningitis samples allowed us to establish a cut-off criterion for the exclusion of contaminating sequences. In total 86 bacterial taxa were identified in brain abscesses by NGS, with Streptococcus intermedius and Fusobacterium nucleatum as most prevalent species; Propionibacterium and Staphylococcus spp. were associated with epidural abscesses. NGS identified two or more bacterial taxa in 31/51 intracranial abscesses, revealing the polymicrobial nature of these infections and allowing the discrimination of up to 16 bacterial taxa per sample.

CONCLUSION

These results extend earlier studies showing that NGS methods expand the spectrum of bacteria detected in brain abscesses and demonstrate that the MiSeq platform is suitable for metagenomic diagnostics of this severe infection.