Individual-Specific Characterization of Event-Related Hemodynamic Responses during an Auditory Task: An Exploratory Study

fNIRS-based evaluation of the impact of SARS-CoV-2 infection central auditory processing

OBJECTIVES

Coronavirus disease-2019 due to SARS-CoV-2 infection has been associated with neurological and neuropsychiatric illnesses as well as auditory system problems. In this study, we aimed to evaluate the impact of SARS-CoV-2 infection on the central auditory system by assessing the hemodynamic activation changes using functional near-infrared spectroscopy (fNIRS).

METHODS

Three participants who had SARS-CoV-2 infection (study group) and four participants who had no SARS-CoV-2 infection (control group) were included in the study. During the auditory oddball task in which two different frequencies of tonal stimulation were presented at 80 dB HL, the participants were asked to pay attention to the rare tonal stimulation and mentally count these target stimuli throughout the task. During this task, oxygenated hemodynamic response functions were evaluated with fNIRS.

RESULTS

Significantly increased oxygenated hemodynamic responses were observed in both groups during the task (p < .05), which was significantly higher in the study group (p < .05). Significantly more HbO activation was observed in the vmPFC, superior temporal gyrus, and medial temporal gyrus in the study group compared to controls (p < .05). Significantly higher hemodynamic activation was observed in the right hemisphere in both groups, which was significantly higher in the study group (p < .05).

CONCLUSION

SARS-CoV-2 infections may impact on central auditory processing or auditory attention due to changes in oxyhemoglobin levels in the frontal and temporal brain regions. It seems that SARS-CoV-2 infection is associated with an additional load on neural activity, and difficulties in focusing in auditory attention, following speech and hearing in noise as well as increased effort to perceive auditory cues.

Context-aware Multimodal Auditory BCI Classification through Graph Neural Networks

The prospect of electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) in the presence of topological information of participants is often left unexplored in most of the brain-computer interface (BCI) systems. Additionally, the usage of these modalities together in the field of multimodality analysis to support multiple brain signals toward improving BCI performance is not fully examined. This study first presents a multimodal data fusion framework to exploit and decode the complementary synergistic properties in multimodal neural signals. Moreover, the relations among different subjects and their observations also play critical roles in classifying unknown subjects. We developed a context-aware graph neural network (GNN) model utilizing the pairwise relationship among participants to investigate the performance on an auditory task classification. We explored standard and deviant auditory EEG and fNIRS data where each subject was asked to perform an auditory oddball task and has multiple trials regarded as context-aware nodes in our graph construction. In experiments, our multimodal data fusion strategy showed an improvement up to 8.40% via SVM and 2.02% via GNN, compared to the single-modal EEG or fNIRS. In addition, our context-aware GNN achieved 5.3%, 4.07% and 4.53% higher accuracy for EEG, fNIRS and multimodal data based experiments, compared to the baseline models.