Brain Hemispheres Swap Dominance for Processing Semantically Meaningful Pitch

Systemic neurophysiological signals of auditory predictive coding

Predictive coding framework posits that our brain continuously monitors changes in the environment and updates its predictive models, minimizing prediction errors to efficiently adapt to environmental demands. However, the underlying neurophysiological mechanisms of these predictive phenomena remain unclear. The present study aimed to explore the systemic neurophysiological correlates of predictive coding processes during passive and active auditory processing. Electroencephalography (EEG), functional near-infrared spectroscopy (fNIRS), and autonomic nervous system (ANS) measures were analyzed using an auditory pattern-based novelty oddball paradigm. A sample of 32 healthy subjects was recruited. The results showed shared slow evoked potentials between passive and active conditions that could be interpreted as automatic predictive processes of anticipation and updating, independent of conscious attentional effort. A dissociated topography of the cortical hemodynamic activity and distinctive evoked potentials upon auditory pattern violation were also found between both conditions, whereas only conscious perception leading to imperative responses was accompanied by phasic ANS responses. These results suggest a systemic-level hierarchical reallocation of predictive coding neural resources as a function of contextual demands in the face of sensory stimulation. Principal component analysis permitted to associate the variability of some of the recorded signals.

Auditory Discrimination Elicited by Nonspeech and Speech Stimuli in Children With Congenital Hearing Loss

PURPOSE

Congenital deafness not only delays auditory development but also hampers the ability to perceive nonspeech and speech signals. This study aimed to use auditory event-related potentials to explore the mismatch negativity (MMN), P3a, negative wave (Nc), and late discriminative negativity (LDN) components in children with and without hearing loss.

METHOD

Nineteen children with normal hearing (CNH) and 17 children with hearing loss (CHL) participated in this study. Two sets of pure tones (1 kHz vs. 1.1 kHz) and lexical tones (/ba2/ vs. /ba4/) were used to examine the auditory discrimination process.

RESULTS

MMN could be elicited by the pure tone and the lexical tone in both groups. The MMN latency elicited by nonspeech and speech was later in CHL than in CNH. Additionally, the MMN latency induced by speech occurred later in the left than in the right hemisphere in CNH, and the MMN amplitude elicited by speech in CHL produced a discriminative deficiency compared with that in CNH. Although the P3a latency and amplitude elicited by nonspeech in CHL and CNH were not significantly different, the Nc amplitude elicited by speech performed much lower in CHL than in CNH. Furthermore, the LDN latency elicited by nonspeech was later in CHL than in CNH, and the LDN amplitude induced by speech showed higher dominance in the right hemisphere in both CNH and CHL.

CONCLUSION

By incorporating nonspeech and speech auditory conditions, we propose using MMN, Nc, and LDN as potential indices to investigate auditory perception, memory, and discrimination.

Brain hemispheres with right temporal lobe damage swap dominance in early auditory processing of lexical tones

Labor division of the two brain hemispheres refers to the dominant processing of input information on one side of the brain. At an early stage, or a preattentive stage, the right brain hemisphere is shown to dominate the auditory processing of tones, including lexical tones. However, little is known about the influence of brain damage on the labor division of the brain hemispheres for the auditory processing of linguistic tones. Here, we demonstrate swapped dominance of brain hemispheres at the preattentive stage of auditory processing of Chinese lexical tones after a stroke in the right temporal lobe (RTL). In this study, we frequently presented lexical tones to a group of patients with a stroke in the RTL and infrequently varied the tones to create an auditory contrast. The contrast evoked a mismatch negativity response, which indexes auditory processing at the preattentive stage. In the participants with a stroke in the RTL, the mismatch negativity response was lateralized to the left side, in contrast to the right lateralization pattern in the control participants. The swapped dominance of brain hemispheres indicates that the RTL is a core area for early-stage auditory tonal processing. Our study indicates the necessity of rehabilitating tonal processing functions for tonal language speakers who suffer an RTL injury.

12 Plagues of AI in Healthcare: A Practical Guide to Current Issues With Using Machine Learning in a Medical Context

The healthcare field has long been promised a number of exciting and powerful applications of Artificial Intelligence (AI) to improve the quality and delivery of health care services. AI techniques, such as machine learning (ML), have proven the ability to model enormous amounts of complex data and biological phenomena in ways only imaginable with human abilities alone. As such, medical professionals, data scientists, and Big Tech companies alike have all invested substantial time, effort, and funding into these technologies with hopes that AI systems will provide rigorous and systematic interpretations of large amounts of data that can be leveraged to augment clinical judgments in real time. However, despite not being newly introduced, AI-based medical devices have more than often been limited in their true clinical impact that was originally promised or that which is likely capable, such as during the current COVID-19 pandemic. There are several common pitfalls for these technologies that if not prospectively managed or adjusted in real-time, will continue to hinder their performance in high stakes environments outside of the lab in which they were created. To address these concerns, we outline and discuss many of the problems that future developers will likely face that contribute to these failures. Specifically, we examine the field under four lenses: approach, data, method and operation. If we continue to prospectively address and manage these concerns with reliable solutions and appropriate system processes in place, then we as a field may further optimize the clinical applicability and adoption of medical based AI technology moving forward.