Neural network-based anomaly detection remains challenging in clinical applications with little or no supervised information and subtle anomalies such as hardly visible brain lesions. Among unsupervised methods, patch-based auto-encoders with their efficient representation power provided by their latent space, have shown good results for visible lesion detection. However, the commonly used reconstruction error criterion may limit their performance when facing less obvious lesions. In this work, we design two alternative detection criteria. They are derived from multivariate analysis and can more directly capture information from latent space representations. Their performance compares favorably with two additional supervised learning methods, on a difficult de novo Parkinson Disease (PD) classification task.

Northwestern investigators have developed a deep learning-based method that can predict cognitive function capacity based on brain shape and structure, detailed in a study published in Scientific Reports. The method, which uses graph convolutional neural networks (gCNNs), may also reveal new insights into the relationship between brain morphology and different cognitive functions as well as the decline of brain function. "When we apply the rich capabilities of CNNs to graph representation of the brain, we can explore the brain as an image in a previously unexplored way," said S. Kathleen Bandt, MD, assistant professor of Neurological Surgery and a co-author of the study. Understanding how the relationship between brain structure and cognitive function changes throughout the life course has remained elusive. However, previous work suggests that fluid intelligence--the ability to problem solve and think and reason abstractly--depends heavily on two regions of the brain: the prefrontal cortex and parietal cortex, both of which are involved in decision-making and sensory perception, among other functions.

Music can spark emotion on the listener's face, but scientists discovered they can'see' the type of melody being played when looking at the individual's brain. Using machine learning and functional magnetic resonance imaging, researchers at the University of Turku found that the auditory and motor cortex were activated when happy or sad music is played. The auditory cortex processes the acoustic elements, such as rhythm and melody, and the motor cortex could be related to the fact that music inspires feelings of movement. The study also looked at music that induces fear, revealing it correlates with subcortical structures involved with memory, emotion and pleasure. 'Music can induce strong subjective experience of emotions, but it is debated whether these responses engage the same neural circuits as emotions elicited by biologically significant events,' researchers shared in the study published in Oxford Academic.