Variability and stability of large-scale cortical oscillation patterns

Individual differences in brain organization exist at many spatiotemporal scales and underlie the diversity of human thought and behavior. Oscillatory neural activity is crucial for these processes, but how such rhythms are expressed across the cortex within and across individuals is poorly understood. We conducted a systematic characterization of brain-wide activity across frequency bands and oscillatory features during rest and task execution. We found that oscillatory profiles exhibit sizable group-level similarities, indicating the presence of common templates of oscillatory organization. Nonetheless, well-defined subject-specific network profiles were discernible beyond the structure shared across individuals. These individualized patterns were sufficiently stable to recognize individuals several months later. Moreover, network structure of rhythmic activity varied considerably across distinct oscillatory frequencies and features, indicating the existence of several parallel information processing streams embedded in distributed electrophysiological activity. These findings suggest that network similarity analyses may be useful for understanding the role of large-scale brain oscillations in physiology and behavior. Neural oscillations are critical for the human brain’s ability to optimally respond to complex environmental input. However, relatively little is known about the network properties of these oscillatory rhythms. We used electroencephalography (EEG) to analyze large-scale brain wave patterns, focusing on multiple frequency bands and several key features of oscillatory communication. We show that networks defined in this manner are, in fact, distinct, suggesting that EEG activity encompasses multiple, parallel information processing streams. Remarkably, the same networks can be used to uniquely identify individuals over a period of approximately half a year, thus serving as neural fingerprints. These findings indicate that investigating oscillatory dynamics from a network perspective holds considerable promise as a tool to understand human cognition and behavior

The roles of item exposure and visualization success in the consolidation of memories across wake and sleep

Memory consolidation during sleep does not benefit all memories equally. Initial encoding strength appears to play a role in governing where sleep effects are seen, but it is unclear whether sleep preferentially consolidates weaker or stronger memories. We manipulated encoding strength along two dimensions—the number of item presentations, and success at visualizing each item, in a sample of 82 participants. Sleep benefited memory of successfully visualized items only. Within these, the sleep–wake difference was largest for more weakly encoded information. These results suggest that the benefit of sleep on memory is seen most clearly for items that are encoded to a lower initial strength

Aiding first incident responders using a decision support system based on live drone feeds

In case of a dangerous incident, such as a fire, a collision or an earthquake, a lot of contextual data is available for the first incident responders when handling this incident. Based on this data, a commander on scene or dispatchers need to make split-second decisions to get a good overview on the situation and to avoid further injuries or risks. Therefore, we propose a decision support system that can aid incident responders on scene in prioritizing the rescue efforts that need to be addressed. The system collects relevant data from a custom designed drone by detecting objects such as firefighters, fires, victims, fuel tanks, etc. The drone autonomously observes the incident area, and based on the detected information it proposes a prioritized based action list on e.g. urgency or danger to incident responders

Plasma Biomarkers of Brain Atrophy in Alzheimer's Disease

Peripheral biomarkers of Alzheimer's disease (AD) reflecting early neuropathological change are critical to the development of treatments for this condition. The most widely used indicator of AD pathology in life at present is neuroimaging evidence of brain atrophy. We therefore performed a proteomic analysis of plasma to derive biomarkers associated with brain atrophy in AD. Using gel based proteomics we previously identified seven plasma proteins that were significantly associated with hippocampal volume in a combined cohort of subjects with AD (N = 27) and MCI (N = 17). In the current report, we validated this finding in a large independent cohort of AD (N = 79), MCI (N = 88) and control (N = 95) subjects using alternative complementary methods—quantitative immunoassays for protein concentrations and estimation of pathology by whole brain volume. We confirmed that plasma concentrations of five proteins, together with age and sex, explained more than 35% of variance in whole brain volume in AD patients. These proteins are complement components C3 and C3a, complement factor-I, γ-fibrinogen and alpha-1-microglobulin. Our findings suggest that these plasma proteins are strong predictors of in vivo AD pathology. Moreover, these proteins are involved in complement activation and coagulation, providing further evidence for an intrinsic role of these pathways in AD pathogenesis

Learning and Representation of Recent Structure in the Environment: Behavioral, Neuroimaging, and Computational Investigations

Environmental statistics gradually come to be represented in cortical areas of the brain after extensive experience and long periods of time. In many contexts, however, we are exposed to new environmental regularities that influence our behavior very rapidly. What kinds of neural processes and representations support such rapid statistical learning? The medial temporal lobe (MTL) can learn new information rapidly, but it is traditionally thought to specialize in learning new arbitrary - not structured - information. Much of this dissertation work investigates whether this rapid learning ability may in fact extend to learning new structured information. In support of this idea, we found that representations of objects that appear nearby in time become more similar to each other throughout the MTL. Beyond indicating that the MTL is involved, these findings begin to suggest what kinds of representations it may construct to support statistical learning. We found the same kind of representational similarity in the hippocampus in a paradigm with more complex structure. In this paradigm, stimulus sequences were generated by a graph with community structure, where the strength of transition probabilities - a cue commonly considered to be critical for event parsing - was uniform, and therefore uninformative for parsing. We found that participants learned the structure nonetheless, as evidenced by event parsing behavior, and that representations of items from the same community came to be represented more similarly than items from different communities in the hippocampus, as well as in the inferior frontal gyrus, anterior temporal lobe, and superior temporal gyrus. Connectivity analyses suggest that the hippocampus may be a central hub in the network of regions involved in learning new events. We additionally found that a patient with MTL damage failed to learn new temporal regularities, providing evidence that the area is necessary for this form of learning. Finally, we ran experiments and developed a computational model suggesting that sleep may help consolidate recently learned structured information. This work begins to characterize the neural mechanisms underlying our ability to rapidly extract and consolidate regularities in a new environment