Effects of Age and Type of Stimulus on the Cortical Auditory Evoked Potential in Healthy Malaysian Children

P50 inhibition defects, psychopathology and gray matter volume in patients with first-episode drug-naive schizophrenia

BACKGROUND

Sensory gating deficits and gray matter volume (GMV) abnormalities have been found to be associated with the pathogenesis and psychopathology of patients with schizophrenia (SCZ). However, no studies have investigated their interrelationship in first-episode treatment-naive (FETN) SCZ patients.

METHODS

We recruited 52 FETN SCZ patients and 57 healthy controls. The Positive and Negative Syndrome Scale (PANSS) was used to measure the psychopathology of the patients. We collected magnetic resonance imaging and P50 inhibition data from all participants.

RESULTS

Compared to healthy controls, patients had shorter S1 and S2 latencies but larger S2 amplitudes and P50 ratio (Bonferroni adjusted all p < 0.01). In patients, S2 latency was independently associated with PANSS total score, negative symptoms and general psychopathology (t = 2.26-2.58, both P < 0.05), whereas S1 (t = 2.44, P < 0.05) and S2 latencies (t = 2.13, P < 0.05) were associated with PANSS cognitive factor. Moreover, GMV in the left inferior temporal gyrus, left lingual gyrus and right superior occipital gyrus, and bilateral dorsolateral superior frontal gyrus were each associated with the P50 components (all p < 0.05). In addition, GMV associated with S2 latency was negatively correlated with PANSS general psychopathology (t = -2.46, p < 0.05) and total score (t = -2.34, p < 0.05).

CONCLUSIONS

Our findings indicate that FETN SCZ patients exhibit deficits in P50 inhibition and GMV of brain regions associated with these deficits may be associated with their psychopathological symptoms, suggesting that brain structures associated with P50 components may be important biomarkers of SCZ psychopathology. Future studies could use a prospective longitudinal design to investigate the potential causal relationship of brain structures associated with P50 components in the psychopathological symptoms of SCZ patients.

The emergence of machine learning in auditory neural impairment: A systematic review

Hearing loss is a common neurodegenerative disease that can start at any stage of life. Misalignment of the auditory neural impairment may impose challenges in processing incoming auditory stimulus that can be measured using electroencephalography (EEG). The electrophysiological behaviour response emanated from EEG auditory evoked potential (AEP) requires highly trained professionals for analysis and interpretation. Reliable automated methods using techniques of machine learning would assist the auditory assessment process for informed treatment and practice. It is thus highly required to develop models that are more efficient and precise by considering the characteristics of brain signals. This study aims to provide a comprehensive review of several state-of-the-art techniques of machine learning that adopt EEG evoked response for the auditory assessment within the last 13 years. Out of 161 initially screened articles, 11 were retained for synthesis. The outcome of the review presented that the Support Vector Machine (SVM) classifier outperformed with over 80% accuracy metric and was recognized as the best suited model within the field of auditory research. This paper discussed the comprehensive iterative properties of the proposed computed algorithms and the feasible future direction in hearing impaired rehabilitation.