Abstract
INTRODUCTION: Machine learning (ML) is an established technique that uses sets of training data to develop algorithms and perform data classification without using human intervention/supervision. This study aims to determine how functional and anatomical brain connectivity (FC and SC) data can be used to classify voiding dysfunction (VD) in female MS patients using ML. METHODS: Twenty-seven ambulatory MS individuals with lower urinary tract dysfunction were recruited and divided into two groups (Group 1: voiders [V, n = 14]; Group 2: VD [n = 13]). All patients underwent concurrent functional MRI/urodynamics testing. RESULTS: Best-performing ML algorithms, with highest area under the curve (AUC), were partial least squares (PLS, AUC = 0.86) using FC alone and random forest (RF) when using SC alone (AUC = 0.93) and combined (AUC = 0.96) as inputs. Our results show 10 predictors with the highest AUC values were associated with FC, indicating that although white matter was affected, new connections may have formed to preserve voiding initiation. CONCLUSIONS: MS patients with and without VD exhibit distinct brain connectivity patterns when performing a voiding task. Our results demonstrate FC (grey matter) is of higher importance than SC (white matter) for this classification. Knowledge of these centres may help us further phenotype patients to appropriate centrally focused treatments in the future.