The brain exists in a state of constant activity in the absence of any external sensory input. The spatiotemporal patterns of this spontaneous brain activity have been studied using various recording and imaging techniques. This has enabled considerable progress to be made in elucidating the cellular and network mechanisms that are involved in the observed spatiotemporal dynamics. This mini-review outlines different spatiotemporal dynamic patterns that have been identified in four commonly used modalities: electrophysiological recordings, optical imaging, functional magnetic resonance imaging, and electroencephalography. Signal sources for each modality, possible sources of the observed dynamics, and future directions are also discussed.
Handedness is one of the most obvious functional asymmetries, but its relation to anatomical asymmetry in the brain has not yet been clearly demonstrated. However, there is no significant evidence to prove or disprove this structure-function correlation, thus left-handed patients are often excluded from magnetic resonance imaging (MRI) studies. MRI classification of left and right hemispheres is a difficult task on its own due to the complexity of the images and the structural similarities between the two halves. We demonstrate a deep artificial neural network approach in connection with a detailed preprocessing pipeline for the classification of lateralization in T1-weighted MR images of the human brain. Preprocessing includes bias field correction and registration on the MNI template. Our classifier is a convolutional neural network (CNN) that was trained on 287 images. Each image was duplicated and mirrored on the mid-sagittal plane. The best model reached an accuracy of 97.594% with a mean of 95.42% and standard deviation of 1.37%. Additionally, our model’s performance was evaluated on an independent set of 118 images and reached a classification accuracy of 97%. In a larger study we tested the model on grey-matter images of 927 left and 927 right-handed patients from the UK Biobank. Here all right-handed images and all left-handed images were classified as belonging to one class. The results suggest that there is no structural difference in grey-matter between the two hemispheres that can be distinguished by the deep learning classifier.
The dynamics of large-scale neural circuits is known to play an important role in both aberrant and normal cognitive functioning. Describing these phenomena is extremely important when we want to get an understand- ing of the aging processes and for neurodegenerative disease evolution. Modern systems and control theory offers a wealth of methods and concepts that can be easily applied to facilitate an insight into the dynamic processes governing disease evolution at the patient level, treatment response evaluation and revealing some central mechanism in a network that drives alterations in these diseases. Past research has shown that two types of controllability - the modal and average controllability - are key components when it comes to the mechanistic explanation of how the brain operates in different cognitive states. The average controllability describes the role of a brain network’s node in driving the system to many easily reachable states. On the other hand, the modal controllability is employed to identify the states that are difficult to control. The first controllability type favors highly connected areas while the latter weakly connected areas of the brain. Certain areas of the brain or nodes in the connectivity graph (structural or functional) can act as drivers and move the system (brain) into specific states of action. To determine these areas we apply the novel concept of exact controllability and determine the minimum set and the location of driver nodes for dementia networks. Our results applied on structural brain networks in dementia suggest that this novel technique can accurately describe the different node roles in controlling trajectories of brain networks, and show the transition of some driver nodes and the conservation of others in the course of this disease.
Leader-follower controllability in brain networks which are affected neurodegenerative diseases can provide important biomarkers relevant for disease evolution. The brain network is viewed as a dynamic system where the nodes interact via neighbor-based Laplacian feedback rules. The network has cooperative connections between the nodes described by positive weights along with competitive connections which are described by negative connection weights. The nodes take the role of either leaders or followers, thus forming a leader-follower signed dynamic graph network. The results of this analysis can be easily generalized on unsigned brain networks. We apply the leader-follower concept to structural and functional brain networks with neurodegenerative diseases (dementia) and show that the found leaders represent important biomarkers for disease evolution. In other words, the leader nodes drive the network towards deteriorating cognitive states.
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