Biology subjects

Sawan, M.

Publications and source records attributed to Sawan, M..

3 recordsLinked to original sources

bioRxiv2023

Investigation of contributions from cortical and subcortical brain structures for speech decoding

Language impairments often arise from severe neurological disorders, prompting the development of neural prosthetics based on electrophysiological signals for the restoration of comprehensible language information. Previous decoding efforts have focused mainly on signals from the cerebral cortex, neglecting the potential contributions of subcortical brain structures to speech decoding in brain-computer interfaces (BCIs). This study aims to explore the role of subcortical structures for speech decoding by utilizing stereotactic electroencephalography (sEEG). Two native Mandarin Chinese speakers, who underwent sEEG implantation for pharmaco-resistant epilepsy, participated in this study. sEEG contacts were primarily located in the superior temporal gyrus, middle temporal gyrus, inferior temporal gyrus, thalamus, hippocampus, insular gyrus, amygdala, and parahippocampal gyrus. The participants were asked to read Chinese text, which included 407 Chinese characters (covering all Chinese syllables), displayed on a screen after receiving prompts. 1-30, 30-70 and 70-150 Hz frequency band powers of sEEG signals were used as key features. A deep learning model based on long short-term memory (LSTM) was developed to evaluate the contribution of different brain structures during encoding of speech. Prediction of speech characteristics of consonants (articulatory place and manner) and tone within single words based on the selected features and electrode contact locations was made. Cortical signals were generally better at articulatory place prediction (86.5% accuracy, chance level = 12.5%), while cortical and subcortical signals predicted articulatory manner at similar level (51.5% vs 51.7% accuracy, respectively, chance level = 14.3%). Subcortical signals generated better prediction for tone (around 58.3% accuracy, chance level = 25%). Superior temporal gyrus remains highly relevant during speech decoding for both consonants and tone. Prediction reached the highest level when cortical and subcortical inputs were combined, especially for tone prediction. Our findings indicate that both cortical and subcortical structures can play crucial roles for speech decoding, each contributing to different aspects of speech.

neuroscience↗

bioRxiv2023

A high-performance brain-sentence communication designed for logosyllabic language

Many severe neurological diseases, such as stroke and amyotrophic lateral sclerosis, can impair or destroy the ability of verbal communication. Recent advances in brain-computer interfaces (BCIs) have shown promise in restoring communication by decoding neural signals related to speech or motor activities into text. Existing research on speech neuroprosthesis has predominantly focused on alphabetic languages, leaving a significant gap of logosyllabic languages such as Mandarin Chinese which are spoken by more than 15% of the world population. Logosyllabic languages pose unique challenges to brain-to-text decoding due to extended character sets (e.g., 50,000+ for Mandarin Chinese) and complex mapping between characters and pronunciation. To address these challenges, we established a speech BCI designed for Mandarin, decoding speech-related stereoelectroencephalography (sEEG) signals into coherent sentences. We leverage the unique acoustic features of Mandarin Chinese syllables, constructing prediction models for syllable components (initials, tones, and finals), and employ a language model to resolve pronunciation to character ambiguities according to the semantic context. This method leads to a high-performance decoder with a median character accuracy of 71.00% over the full character set, demonstrating huge potentials for clinical application. To our knowledge, we are the first to report brain-to-sentence decoding for logosyllabic languages over full character set with a large intracranial electroencephalography dataset.

neuroscience↗

bioRxiv2022

Uniformity of spheroid-on-chip by surface treatment of PDMS microfluidic platforms

Spheroids have emerged as a more reliable model for drug screening when compared with 2D culture models. Microfluidic based biochips have many advantages over other 3D cell culture models for drug testing on spheroids, including precise control of the cellular microenvironment. The control of the cell adhesion to the surface is one of the most important challenges affecting the size and the geometry of the spheroids which could be controlled by appropriate surface engineering methods. We have studied the modification of the PDMS surface properties treated by applying different concentrations of the two anti-fouling coatings (BSA and Pluronic F-68). The desired treatment of PDMS surface effectively inhibits cell adhesion to the surface and promotes cells self-aggregations to form more uniform and healthy spheroids for a longer period of time. The microscopic observations with qualitative and quantitate data revealed that surface properties drastically affect the number of the spheroids formed on-chip and their geometry. We used human breast cancer cell line (MDA-MB-231-GFP) while the concentration of the chemical coatings and incubation time were adjusted. Proper repellent PDMS surfaces were provided with minimum cell attachment and facilitated spheroid formation when compared with non-treated PDMS. The results demonstrate fundamental and helpful patterns for microfluidic based cell culture applications to improve the quantity and quality of spheroid formation on-chip which are strongly manipulated by surface properties (i.e., morphology, roughness, wettability and etc.)

bioengineering↗