Project Summary/Abstract
The idiosyncrasies of the human brain require that individualized mapping of functional regions be performed
before surgical interventions for cancer or epilepsy. The success of this mapping procedure has direct effects
on surgical outcomes and preserving cognitive and sensory function post-surgery. Current gold standard
procedures for pre-surgical mapping are invasive, time-consuming, and technically demanding. Several
non-invasive procedures have emerged in recent years; however, they have not yet displaced the gold
standard procedures. Task-based functional magnetic resonance imaging (t-fMRI), the most widely used
non-invasive pre-surgical mapping technique, requires that patients perform cognitive or motor tasks
while in the scanner—a time-consuming and expensive procedure. Also, not all patients can perform fMRI
tasks due to language barriers, sensory deficits, being unconscious, etc. Connectome Fingerprinting (CF) is a
recently developed technique that uses machine learning to train a model capable of predicting
functional brain activation from task-free resting-state fMRI (rs-fMRI). Once trained on a set of t-fMRI and rs-
fMRI data, an unseen subject's unique pattern of brain activation can be predicted using only an rs-fMRI scan
of their brain—therefore eliminating the need to perform tasks during the fMRI scan. Despite the promise of
CF, the accuracy of the current best practice modeling techniques is not high enough yet to be clinically
useful and studies applying CF have nearly always used healthy populations. Much research remains to be
done to increase the accuracy of CF models before they can be deployed for pre-surgical mapping.
The long-term objective of the research proposed here is to develop a software application that combines
applied machine learning with medical imaging to provide a non-invasive means for mapping the brains of
neurosurgical patients before surgery. Importantly, we aim to increase the accuracy of CF modeling by
expanding the modeling efforts to probabilistic Bayesian approaches that leverage prior information from the
structure of the data. We will test a wide array of tunable data and model parameters to arrive at a current
recommendation for best practices in CF research and applications. Finally, we will test our modeling procedures
with a dataset of healthy control and pre-surgical patients diagnosed with brain tumors. We will test the software's
ability to accurately predict functional brain organization in these patients and adaptively retrain the models to
produce the most accurate results. This work has the potential to revolutionize pre-surgical brain mapping and
expand its applicability to a greater number of patients.
