Statistical Methods for finding Functional Connectivity

Author/​Artist
Lee, Katherine [Browse]
Format
Senior thesis
Language
English

Details

Advisor(s)
Liu, Han [Browse]
Department
Princeton University. Department of Operations Research and Financial Engineering [Browse]
Certificate
Princeton University. Program in Robotics and Intelligent Systems [Browse]
Class year
2017
Summary note
We currently do not understand how the brain integrates information in real time to understand stimuli. The study of functional connectivity aims to understand which regions of the brain interact. However, current methods in functional connectivity are limited by requiring prior knowledge, such as specifying the number of clusters in the k-means algorithm, or confounding variables that result from simply thresholding the correlation matrix. We propose a method of sparse inverse covariance estimation paired with intersubject functional connectivity (ISFC) to overcome these challenges and give methods for selecting parameters. We show how our methods and prior algorithms perform on two datasets where we aim to understand how brain connections change through time and through changes in our internal motivations.

Supplementary Information