BEBOP: Bidirectional dEep Brain cOnnectivity maPping

Functional connectivity mapping provides information about correlated brain areas, useful for many applications such as on mental disorders. This work aims to improve this mapping by using deep metric learning considering the directionality of information flow and time-domain features. To deal with the computational cost of a complete pairwise combination network, we trained a network able to recognize similar signals and, after training, feed it with all combinations of signals from each brain area. The labels of similarity or dissimilarity are determined by agglomerative clustering using the Jensen-Shannon Distance as a metric. To validate our approach we employed a resting-state eye-open functional MRI dataset from ADHD and healthy subjects. Once registered, the signals are filtered and averaged by area with a functional trimmed mean. After obtaining the connectivity maps from each subject, we perform a feature importance selection using logistic regression. The ten most promising areas were extracted, such as the frontal cortex and the limbic system. These results are in complete agreement with previous literature. It is well known those areas are mainly involved in attention and impulsivity.