Convolutional neural networks (CNNs) have recently been used in biomedical imaging applications with great success. In this paper, we investigated the classi?cation performance of CNN models on diffusion weighted imaging (DWI) streamlines de?ned by functional MRI (fMRI) and electrical stimulation mapping (ESM). To learn a set of discriminative and interpretable features from the extremely unbalanced dataset, we evaluated different CNN architectures with multiple loss functions (e.g., focal loss and center loss) and a soft attention mechanism, and compared our models with current state-ofthe-art methods. Through extensive experiments on streamlines collected from 70 healthy children and 70 children with focal epilepsy, we demonstrated that our deep CNN model with focal and central losses and soft attention outperforms all existing models in the literature and provides clinically acceptable accuracy (73 -100%) for the objective detection of functionally-important white matter pathways including ESM determined eloquent areas such as primary motor, aphasia, speech arrest, auditory, and visual functions. The ?ndings of this study encourage further investigations to determine if DWICNN analysis can serve as a noninvasive diagnostic tool during pediatric presurgical planning by estimating not only the location of essential cortices at the gyral level, but also the underlying ?bers connecting these cortical areas, to minimize or predict postsurgical functional de?cits. This study translates an advanced CNN model to clinical practice in the pediatric population where currently available approaches (e.g., ESM, fMRI) are suboptimal. The implementation will be released at https://github. com/HaotianMXu/Brain-?ber-classi?cation-using-CNNs.