The measurement and analysis of cortical networks

Summary form only given. Technological advances over the past five years have led to an unprecedented level of volume and detail in the acquisition of neuroscientific data relating to the mammalian brain. However, this creates significant challenges in the processing and interpretation of the data. We will adopt a network-centric approach to tackle this, as it matches the physical structure of the brain. We present methods to extract functional brain networks from spatio-temporal time series that describe neural activity, such as in functional magnetic resonance imaging (fMRI). These networks capture intrinsic brain dynamics. We describe computational methods to extract topological regularities in such networks, including motifs and cycles. We analyze the relations hip between the structure of the network, as represented by its motifs, and its function. For instance, example hub neurons in the hippocampus promote synchrony and shortest loops act as pacemakers of neural activity. We demonstrate the relevance of the network analysis techniques in understanding specific brain-related disorders such as schizophrenia and autism. For instance, the disruption of cortical networks involved in synchronization may be a contributor to autism and schizophrenic patients which have been shown to have higher connectivity within the default mode network.