Detecting Gender Differences in HCP Resting

Theoretical Motivation

Currently, most neuroimaging research does not separate datasets by gender to analyze gender-specific results. Our project takes advantage of the large database of subjects from the Human Connectome Project (HCP). We will use resting state (rfMRI) to construct connectomes and compare network measurements such as eigenvector centrality (EC), node degree and strength between genders. Furthermore, we will correlate these measurements with behavioral measurements of interest, to deduce whether personality differences, affective style and fluid intelligence affect aforementioned network measurements. We predict that compared to males, females high in fluid intelligence, extraversion and ability to emotion recognition will exhibit increased EC between orbitofrontal cortex (OFC), anterior cingulate (ACC) and posterior superior temporal sulcus (pSTS).

Research Design

100 subjects (N=50M, 50F) with rFMRI will be downloaded to a cloud-based platform provided by the Online Brain Intensive. A pre-processing pipeline will be used on Nipype for HCP resting state data, adding code to convert the imaging data to matrices for further use in network analysis. We will extract network measurements, which will be further correlated with behavioral measurements of interest: extraversion, emotion recognition capability, social relationships, self-efficacy and fluid intelligence.

Statistical Analysis

Group differences in demographic and behavioral data will be analyzed. Nypype pre-processing pipeline will be used for rMRI data. For imaging data (EC maps), two-sample t-test will be used to identify differential brain regions between the two groups with extraversion, emotion recognition capability, social relationships, self-efficacy and fluid intelligence as covariates. Signals from the significant clusters will be extracted. We will further put the EC values of the significant clusters into regression models as predictors of behavioral variables.

Code Development

Code will be developed in Github under the https://github.com/stoicateo/OBI_HCP_Project repository after the preprocessing of data has been completed. The team will work together to develop code in order to extract and threshold EC maps and regress the resultant values with behavioral variables.

Team: Connection Makers

Contact information: Saige Rutherford, saruther@med.umich.edu

Team members: @maximiliun @teodorast @azmat0009 @saigerutherford @jerez @tjiagom

View Project Github Repository