Brain Imaging

We developed a deep learning model for detecting the diagonal sulcus (ds), a tertiary brain sulcus critical for language processing. Using a fine-tuned convolutional autoencoder, our approach achieved state-of-the-art accuracy, outperforming traditional software and other deep learning models. The method leverages self-supervised learning and provides interpretable results aligned with expert annotations, enabling population studies of ds prevalence.

The paper presents a novel method for learning normal asymmetry patterns in brain structures. It accurately characterizes normal asymmetries and detects pathological alterations without relying on diseased cases for training. The approach shows promise in improving the identification of neurodegenerative conditions..

NORHA is a novel index for quantifying hippocampal asymmetries in neurodegenerative conditions. It shows promise as a biomarker for detecting unilateral abnormalities, such as hippocampal sclerosis, and correlates positively with the functional cognitive test CDR-SB, indicating its potential in dementia diagnosis.