Motivation: Cognitive impairment is an important symptom of Parkinson’s Disease (PD),
usually having a substantial negative impact on the quality of life of patients, families,
and caregivers. Cognitive Training (CT) have been proven effective in halting the process
of cognitive decline in PD. However, the efficacy of CT is unpredictable from subject to
subject.
Objective: Investigate the possibility of predicting the outcome of CT in PD patients
with Mild Cognitive Impairment using structural and functional Magnetic Resonance
Imaging (MRI) data.
Methods: Before CT, a sample of 42 PD patients underwent structural and functional
MRI. Graph measures were then extracted from their structural and functional con nectomes and used as features for random forest (RFo) and decision tree (DT) machine
learning (ML) regression algorithms with and without prior latent component analysis
(LCA). CT response was evaluated by assessing the outcomes of the Tower of London
task pre- and post-treatment. Finally, the 4 ML models were used to predict CT response
and their performances were assessed. Post hoc analyses were conducted to investigate
whether these algorithms could predict age using connectomic measures on a sample of
80 PD patients.
Results: The performances of the aforementioned algorithms did not differ signifi cantly from the baseline performance predicting the subject-specific CT outcome. The
performance of the RFo without LCA differed significantly from the baseline performance
in the age prediction task for the sample of 80 patients.
Conclusion: Notwithstanding the lack of statistical significance in predicting our
xicognitive outcomes, the relative success of the age prediction task points towards the
potential of this approach. We hypothesise that bigger sample sizes are needed in order
to predict the outcome of CT using ML
