Saliency mapping of RS-fMRI data in GCNs for sex and brain age prediction
Typ
Examensarbete för masterexamen
Program
Publicerad
2021
Författare
Andersson, Kevin
Lindgren, Eric
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
Insight into how biological sex and healthy ageing affects the human brain are important
for an increased understanding of the brain. Healthy ageing insights are
also useful for clinical applications, for instance in identifying unhealthy ageing due
to neurodegenerative disease. To this end, several studies in the last few years have
used machine learning methods on neuroscientific data to predict subject sex and
brain age. One particularly interesting approach has been to represent functionally
connected networks in the brain as graphs, and apply Graph Convolutional Networks
(GCNs). To investigate which functional brain networks are connected with
sex and age, we develop and analyse GCN-based models that predict sex and age
from resting-state fMRI data. The analysis of the models is done using saliency
mapping techniques that give insight into which functional brain networks in the
data are relevant for the predictions. With this approach, we obtain a sex prediction
accuracy of up to 79% and an age prediction MAE of 5.9 years. Furthermore,
we find indications that the Somatomotor Medial Network and the cerebellum are
among the more important functional brain networks for predicting sex and brain
age.
Beskrivning
Ämne/nyckelord
machine learning , supervised learning , GNN , GCN , explainability in AI , graph theory , population graphs , brain age , sex , functional connectivity , restingstate fMRI , saliency mapping