Complex network analysis and topology-based data analysis for identifying risk factors of delirium

의과대학/박사Objective The human brain is a complex network of interlinked regions and composed of the structural (or backbone) network and dynamic functional networks. Studies revealed that the abnormal brain network has associated with the emergence of brain diseases. Delirium is one of brain diseases, and its etiologies are multifactorial. To identify risk factors of delirium, this study aimed to investigate neural substrates of delirium using complex network analysis and investigate phenotypic subgroups of delirium using topology-based data analysis Materials and Methods A total of fifty-eight hip fracture patients were recruited in this study. Structural and functional neuroimaging data from all participants were acquired before hip fracture surgery. Among the participants, only thirty-two patients were scanned for postoperative MRI data acquisition. Neural substrates in preoperative delirium, and functional connectivity re-organization during an episode of delirium were studied using a framework of complex network analysis. In topological data analysis, cognitive impairment, personality scales such as neuroticism and conscientiousness, and delirium rating scale were considered to identify phenotypic subgroups of delirium. Results Among fifty-eight patients, twenty-five patients were identified as delirium after hip fracture surgery. In the study of neural substrates of delirium, the significant increase of characteristic path length in structural network was observed in preoperative delirium (P<0.05). Also, increased structural path length densities connecting frontal to subcortical and visual sensory regions were played a pivotal role in characterizing delirious patients (corrected P<0.05). Furthermore, functional connectivity density between the prefrontal cortex and audiovisual sensory areas were significantly increased in preoperative delirium (corrected P<0.05). Interestingly, functional connectivity density between the visual cortex and the frontal and auditory areas were strongly suppressed in during-delirum patients (P<0.05). Finally, topology-based data analysis identified three subgroups of delirium in dimensions of cognitive function and personality. Conclusion This study investigated neuroimaging-based neural risk factors for delirium. The increased path length of structural network in preoperative delirium implies that there existed disruptions of the connection weights such as a fractional anisotropy and the number of streamlines in the backbone network. The significantly suppressed functional connectivity from the visual cortex to the auditory cortex and frontal regions may play a pivotal role in characterizing the delirious phenomena such as dysfunction in perception, a deficit in sustaining the conscious mental state, and hallucination. Lastly, topological data analysis suggests that neural substrates of delirium could be different for phenotypic subgroups of delirium.ope

Disruption to control network function correlates with altered dynamic connectivity in the wider autism spectrum.

Autism is a common developmental condition with a wide, variable range of co-occurring neuropsychiatric symptoms. Contrasting with most extant studies, we explored whole-brain functional organization at multiple levels simultaneously in a large subject group reflecting autism's clinical diversity, and present the first network-based analysis of transient brain states, or dynamic connectivity, in autism. Disruption to inter-network and inter-system connectivity, rather than within individual networks, predominated. We identified coupling disruption in the anterior-posterior default mode axis, and among specific control networks specialized for task start cues and the maintenance of domain-independent task positive status, specifically between the right fronto-parietal and cingulo-opercular networks and default mode network subsystems. These appear to propagate downstream in autism, with significantly dampened subject oscillations between brain states, and dynamic connectivity configuration differences. Our account proposes specific motifs that may provide candidates for neuroimaging biomarkers within heterogeneous clinical populations in this diverse condition

Network theory approach for data evaluation in the dynamic force spectroscopy of biomolecular interactions

Investigations of molecular bonds between single molecules and molecular complexes by the dynamic force spectroscopy are subject to large fluctuations at nanoscale and possible other aspecific binding, which mask the experimental output. Big efforts are devoted to develop methods for effective selection of the relevant experimental data, before taking the quantitative analysis of bond parameters. Here we present a methodology which is based on the application of graph theory. The force-distance curves corresponding to repeated pulling events are mapped onto their correlation network (mathematical graph). On these graphs the groups of similar curves appear as topological modules, which are identified using the spectral analysis of graphs. We demonstrate the approach by analyzing a large ensemble of the force-distance curves measured on: ssDNA-ssDNA, peptide-RNA (system from HIV1), and peptide-Au surface. Within our data sets the methodology systematically separates subgroups of curves which are related to different intermolecular interactions and to spatial arrangements in which the molecules are brought together and/or pulling speeds. This demonstrates the sensitivity of the method to the spatial degrees of freedom, suggesting potential applications in the case of large molecular complexes and situations with multiple binding sites

Analyzing overlapping communities in networks using link communities

One way to analyze the structure of a network is to identify its communities, groups of related nodes that are more likely to connect to one another than to nodes outside the community. Commonly used algorithms for obtaining a network’s communities rely on clustering of the network’s nodes into a community structure that maximizes an appropriate objective function. However, defining communities as a partition of a network’s nodes, and thus stipulating that each node belongs to exactly one community, precludes the detection of overlapping communities that may exist in the network. Here we show that by defining communities as partition of a network’s links, and thus allowing individual nodes to appear in multiple communities, we can quantify the extent to which each pair of communities in a network overlaps. We define two measures of community overlap and apply them to the community structure of five networks from different disciplines. In every case, we note that there are many pairs of communities that share a significant number of nodes. This highlights a major advantage of using link partitioning, as opposed to node partitioning, when seeking to understand the community structure of a network. We also observe significant differences between overlap statistics in real-world networks as compared with randomly-generated null models. By virtue of their contexts, we expect many naturally-occurring networks to have very densely overlapping communities. Therefore, it is necessary to develop an understanding of how to use community overlap calculations to draw conclusions about the underlying structure of a network

Parasympathetic functions in children with sensory processing disorder.

The overall goal of this study was to determine if parasympathetic nervous system (PsNS) activity is a significant biomarker of sensory processing difficulties in children. Several studies have demonstrated that PsNS activity is an important regulator of reactivity in children, and thus, it is of interest to study whether PsNS activity is related to sensory reactivity in children who have a type of condition associated with sensory processing disorders termed sensory modulation dysfunction (SMD). If so, this will have important implications for understanding the mechanisms underlying sensory processing problems of children and for developing intervention strategies to address them. The primary aims of this project were: (1) to evaluate PsNS activity in children with SMD compared to typically developing (TYP) children, and (2) to determine if PsNS activity is a significant predictor of sensory behaviors and adaptive functions among children with SMD. We examine PsNS activity during the Sensory Challenge Protocol; which includes baseline, the administration of eight sequential stimuli in five sensory domains, recovery, and also evaluate response to a prolonged auditory stimulus. As a secondary aim we examined whether subgroups of children with specific physiological and behavioral sensory reactivity profiles can be identified. Results indicate that as a total group the children with severe SMD demonstrated a trend for low baseline PsNS activity, compared to TYP children, suggesting this may be a biomarker for SMD. In addition, children with SMD as a total group demonstrated significantly poorer adaptive behavior in the communication and daily living subdomains and in the overall Adaptive Behavior Composite of the Vineland than TYP children. Using latent class analysis, the subjects were grouped by severity and the severe SMD group had significantly lower PsNS activity at baseline, tones and prolonged auditory. These results provide preliminary evidence that children who demonstrate severe SMD may have physiological activity that is different from children without SMD, and that these physiological and behavioral manifestations of SMD may affect a child\u27s ability to engage in everyday social, communication, and daily living skills