Vishnu Moorthigari and Rohan Gandhi working under the supervision of Dr. Vinoo Alluri presented their research work virtually at the 13th International Brain Informatics Conference (BI 2020) on 19 September at Padua, Italy.
- Differential Effects of Trait Empathy on Functional Network Centrality, Moorthigari V, Carlson E, Toiviainen P, Brattico E, Alluri V
Research work as explained by Dr. Alluri Vinoo and her research team:
Previous research has shown that empathy, a fundamental component of human social functioning, is engaged when listening to music. Neuroimaging studies of empathy processing in music have, however, been limited. fMRI analysis methods based on graph theory have recently gained popularity as they are capable of illustrating global patterns of functional connectivity, which could be very useful in studying complex traits such as empathy. The current study examines the role of trait empathy, including cognitive and affective facets, on whole-brain functional network centrality in 36 participants listening to music in a naturalistic setting. Voxel-wise eigenvector centrality mapping was calculated as it provides us with an understanding of globally distributed centres of coordination associated with the processing of empathy. Partial correlation between Eigenvector centrality and measures of empathy showed that cognitive empathy is associated with higher centrality in the sensorimotor regions responsible for motor mimicry while affective empathy showed higher centrality in regions related to auditory affect processing. Results are discussed in relation to various theoretical models of empathy and music cognition.
More details at: https://doi.org/10.1007/978-3-030-59277-6_10
- Dynamic Functional Connectivity Captures Individuals’ Unique Brain Signatures, Gandhi R, Garimella A, Toiviainen P, Alluri V
Research work as explained by Dr. Alluri Vinoo and her research team:
Recent neuroimaging evidence suggest that there exists a unique individual-specific functional connectivity (FC) pattern consistent across tasks. The objective of our study is to utilize FC patterns to identify an individual using a supervised machine learning approach. To this end, we use two previously published data sets that comprises resting-state and task-based fMRI responses. We use static FC measures as input to a linear classifier to evaluate its performance. We additionally extend this analysis to capture dynamic FC using two approaches: the common sliding window approach and the more recent phase synchrony-based measure. We found that the classification models using dynamic FC patterns as input outperform their static analysis counterpart by a significant margin for both data sets. Furthermore, sliding window-based analysis proved to capture more individual-specific brain connectivity patterns than phase synchrony measures for resting-state data while the reverse pattern was observed for the task-based data set. Upon investigating the effects of feature reduction, we found that feature elimination significantly improved results upto a point with near-perfect classification accuracy for the task-based data set while a gradual decrease in the accuracy was observed for resting-state data set. The implications of these findings are discussed. The results we have are promising and present a novel direction to investigate further
More details at: https://doi.org/10.1007/978-3-030-59277-6_9
The Brain Informatics (BI) conference series has established itself as the world’s premier research forum on Brain Informatics, which is an emerging interdisciplinary and multidisciplinary research field with joint efforts from neuroscience, cognitive science, medicine and life sciences, data science, artificial intelligence, neuroimaging technologies, and information and communication technologies.
The 13th International Conference on Brain Informatics (BI2020) provided a premier international forum and brought together researchers and practitioners from diverse fields for presentation of original research results, as well as exchange and dissemination of innovative and practical development experiences on Brain Informatics research, brain-inspired technologies and brain/mental health applications. The theme of BI2020 is: Brain Informatics in the Virtual World.