[month] [year]

Sasanka G R S

Sasanka G R S supervised by Dr. Santosh Nannuru received his Master of Science – Dual Degree  in  Electronics and Communication Engineering (ECE). Here’s a summary of his research work on Graph learning for functional brain connectivity: An empathy network study:

Functional Magnetic Resonance Imaging (fMRI) research employing naturalistic stimuli, particularly movies, examines brain network interactions underlying complex cognitive processes such as empathy. Leveraging graph learning methods applied to whole-brain time-series signals, a novel processing pipeline is proposed, integrating high-pass filtering, voxel-level clustering, and windowed graph learning with a sparsity-based approach. The study involves the analysis of fMRI data collected from healthy participants while watching empathetic movies and during resting-state conditions. Key brain regions implicated in the empathy network, including the Insula, Pre-Frontal Cortex (PFC), Anterior Cingulate Cortex (ACC), and parietal regions, are examined. Results of the exploratory analysis reveal that the sparsity-based graph learning method consistently outperforms others in capturing graph cluster label variations in comparison with the emotion contagion scale, achieving over 88% match across participants. The analysis demonstrates a gradual induction of empathy with a match after 150 seconds through the stimulus. Additionally, edge-weight dynamics analysis of the edge between empathy supporting areas underscores the superiority of sparsity-based learning, with some providing noisy activations. Connectome-network analysis highlights the pivotal role of the Insula, Amygdala, and Thalamus in empathy, with lateral brain connections facilitating synchronized responses. Spectral filtering analysis emphasizes the significance of the band-pass filter in isolating regions linked to emotional and empathetic processing during high emotional states. Strong similarities across movies in graph cluster labels, connectome-network analysis, and spectral filtering based analyses reveal robust neural correlates of empathy. Furthermore, a comparative study of task and resting-state conditions reveals alignment with the resting-state during low emotional valence intervals of the movie but diverges notably during high emotional valence intervals, suggesting a shared connectivity pattern between stimulus-induced directed (controlled) mind wandering (bottom-up process) and resting-state activity (top-down process). The sparsity-based method shows a 98% match with viewer ratings on the emotion contagion scale, surpassing the 84% match achieved by Pearson’s correlation-based method. This nuanced understanding of neural dynamics in empathy-related tasks versus resting-state enhances the understanding of the networks underlying cognitive processes in real-life situations (naturalistic) and the use of resting-state for the same, paving way for targeted interventions and treatments for conditions associated with empathetic processing and offering significant real-life applications and impact.

 

 June 2024