Inference of Language Functional Network in Healthy, Cancerous and Bilingual Brains by fMRI and Network Modeling

Inference of functional language networks from functional Magnetic Resonant Imaging and network theory by Qiongge Li Adviser: Professor Hernan Makse We study the underlying mechanism by which language processing occurs in the human brain using inference methods on functional magnetic resonance imaging data. The data analyzed stems from several cohorts of subjects; a monolingual group, a bilingual group, a healthy control group and one diseased case. We applied a complex statistical inference pipeline to determine the network structure of brain components involved with language. This healthy network reveals a fully connected triangular relationship between the preSupplementary Motor Area (pre-SMA), the Broca’s Area (BA), and the ventral Pre-Motor Area (PreMA) in the left hemisphere. This “triangle” shows consistently in all the healthy subjects (100%) we analyzed regardless of their monoor multi-lingual status. In addition, we found that Wernicke’s Area (WA) on the left hemisphere connects with BA and PreMA to form a “V” shape connectivity across 75% of the monolinguals, 50% of the bilinguals speaking a second language and 100% of the bilinguals speaking their native language. By comparing the quantified link weights, we found that the strongest link is between BA and PreMA, followed by pre-SMA and PreMA, and then pre-SMA and BA. This is consistent for all healthy subjects (p < 0.05). Furthermore, we conducted a k-core analysis testing the resiliency of subnetworks in the three groups. Our results show that nodes in the three triangle areas belong mostly to the maximum shell, whereas WA populates mostly in the lower shell, consistently across the data.