BrainLang DL: A Deep Learning Approach to FMRI for Unveiling Neural Correlates of Language across Cultures

Abstract: Employing deep learning techniques on fMRI data enables the exploration of universal and culturally specific neural correlates underlying language processing across diverse populations. The study presents "BrainLang DL," a novel deep learning (DL) approach leveraging functional Magnetic Resonance Imaging (fMRI) data to unveil neural correlates of language processing across diverse cultural backgrounds. To bridge the knowledge gap in the universal and culture-specific aspects of language processing, we engaged participants from various cultural groups in a series of linguistic tasks while recording their brain activity using fMRI. Our rigorous data preprocessing pipeline included steps such as motion correction, slice timing correction, and spatial smoothing to enhance data quality for subsequent analysis. For feature extraction, research utilized the Crocodile Hunting Optimization (CHO) algorithm to pinpoint critical brain regions and connectivity patterns linked to language functions. To capture the temporal dynamics of neural activity related to language processing, we deployed advanced recurrent neural networks, specifically Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) models. These techniques enabled us to unravel how linguistic information is encoded and processed over time. Our findings reveal both common and unique neural activation patterns in language processing across different cultures. Universally shared neural mechanisms highlight the fundamental aspects of language processing, while distinct variations underscore the influence of cultural context on brain activity. Furthermore, we employed Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) networks to analyze the temporal dynamics of language-related neural activity, uncovering how linguistic information is represented and processed over time. By integrating DL with fMRI analysis, our study provides a nuanced understanding of the neural correlates of language across cultures. It reveal both shared neural mechanisms underlying language processing across diverse populations and culturally specific variations in brain activation patterns. These findings contribute to a more comprehensive understanding of the neural basis of language and its modulation by cultural factors. Ultimately, our approach offers insights into the complex interplay between language, cognition, and culture, with implications for fields such as linguistics, neuroscience, and cross-cultural psychology.

A. Greeni, Yousef A.Baker El-Ebiary, G. Venkata Krishna, G. Vikram, Kuchipudi Prasanth Kumar, Ravikiran K and B Kiran Bala. “BrainLang DL: A Deep Learning Approach to FMRI for Unveiling Neural Correlates of Language across Cultures”. International Journal of Advanced Computer Science and Applications (IJACSA) 15.6 (2024). http://dx.doi.org/10.14569/IJACSA.2024.01506114

@article{Greeni2024,
title = {BrainLang DL: A Deep Learning Approach to FMRI for Unveiling Neural Correlates of Language across Cultures},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.01506114},
url = {http://dx.doi.org/10.14569/IJACSA.2024.01506114},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {6},
author = {A. Greeni and Yousef A.Baker El-Ebiary and G. Venkata Krishna and G. Vikram and Kuchipudi Prasanth Kumar and Ravikiran K and B Kiran Bala}
}