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ABSTRACT: Complex functional brain network analyses have exploded over the last eight
years, gaining traction due to their profound clinical implications. The
application of network science (an interdisciplinary offshoot of graph theory)
has facilitated these analyses and enabled examining the brain as an integrated
system that produces complex behaviors. While the field of statistics has been
integral in advancing activation analyses and some connectivity analyses in
functional neuroimaging research, it has yet to play a commensurate role in
complex network analyses. Fusing novel statistical methods with network-based
functional neuroimage analysis will engender powerful analytical tools that
will aid in our understanding of normal brain function as well as alterations
due to various brain disorders. Here we survey widely used statistical and
network science tools for analyzing fMRI network data and discuss the
challenges faced in filling some of the remaining methodological gaps. When
applied and interpreted correctly, the fusion of network scientific and
statistical methods has a chance to revolutionize the understanding of brain
function.
02/2013;
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ABSTRACT: Network science is a rapidly emerging analysis method for investigating complex systems, such as the brain, in terms of their components and the interactions among them. Within the brain, music affects an intricate set of complex neural processing systems. These include structural components as well as functional elements such as memory, motor planning and execution, cognition and mood fluctuation. Because music affects such diverse brain systems, it is an ideal candidate for applying network science methods. Using as naturalistic an approach as possible, the authors investigated whether listening to different genres of music affected brain connectivity. Here the authors show that varying levels of musical complexity affect brain connectivity. These results suggest that network science offers a promising new method to study the dynamic impact of music on the brain.
Leonardo 02/2013; 45(3):282-283.
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ABSTRACT: In recent years, the field of network science has enabled researchers to represent the highly complex interactions in the brain in an approachable yet quantitative manner. One exciting finding since the advent of brain network research was that the brain network can withstand extensive damage, even to highly connected regions. However, these highly connected nodes may not be the most critical regions of the brain network, and it is unclear how the network dynamics are impacted by removal of these key nodes. This work seeks to further investigate the resilience of the human functional brain network. Network attack experiments were conducted on voxel-wise functional brain networks and region-of-interest (ROI) networks of 5 healthy volunteers. Networks were attacked at key nodes using several criteria for assessing node importance, and the impact on network structure and dynamics was evaluated. The findings presented here echo previous findings that the functional human brain network is highly resilient to targeted attacks, both in terms of network structure and dynamics.
PLoS Computational Biology 01/2013; 9(1):e1002885. · 5.22 Impact Factor
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ABSTRACT: The application of graph theory to brain networks has become increasingly popular in the neuroimaging community. These investigations and analyses have led to a greater understanding of the brain's complex organization. More importantly, it has become a useful tool for studying the brain under various states and conditions. With the ever expanding popularity of network science in the neuroimaging community, there is increasing interest to validate the measurements and calculations derived from brain networks. Underpinning these studies is the desire to use brain networks in longitudinal studies or as clinical biomarkers to understand changes in the brain. A highly reproducible tool for brain imaging could potentially prove useful as a clinical tool. In this review, we examine recent studies in network reproducibility and their implications for analysis of brain networks.
Frontiers in Neuroscience 01/2013; 7:67.
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ABSTRACT: An agent-based model consists of a set of agents representing the components of a system. These agents interact with each other according to rules designed with knowledge of the system in mind. Although rules control the low-level interactions of agents, these models often exhibit emergent behavior at the system level. We apply the agent-based modeling framework to functional brain imaging data. In this model, agents are defined by network nodes and represent brain regions, and links representing functional connectivity between nodes dictate which agents interact. A link between two regions may be positive or negative, depending on the correlation in functional activity between the two regions. Agents are either active or inactive, and systematically update based on the activity of their immediate neighbors. Their dynamics are observed over a certain time period starting from predetermined initial configurations. While the information received by each node is limited by the number of other nodes connected to it, we have shown that this model is capable of producing emergent behavior dependent on global information transfer. Specifically, the system is capable of solving well-described test problems, such as the density classification and synchronization problems. The model is capable of producing a wide range of behaviors varying greatly in complexity, including oscillations with cycles ranging from a few steps to hundreds, and non-repeating patterns over hundreds of thousands of time steps. We believe this wide dynamic range may impart the potential for this system to produce a myriad of brain-like functional states.
Neural networks: the official journal of the International Neural Network Society 06/2012; 33:275-90. · 1.88 Impact Factor
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ABSTRACT: The Power of Food Scale (PFS) is a new measure that assesses the drive to consume highly palatable food in an obesogenic food environment. The data reported in this investigation evaluate whether the PFS moderates state cravings, control beliefs, and brain networks of older, obese adults following either a short-term post-absorptive state, in which participants were only allowed to consume water, or a short-term energy surfeit treatment condition, in which they consumed BOOST®. We found that the short-term post-absorptive condition, in which participants consumed water only, was associated with increases in state cravings for desired food, a reduction in participants' confidence related to the control of eating behavior, and shifts in brain networks that parallel what is observed with other addictive behaviors. Furthermore, individuals who scored high on the PFS were at an increased risk for experiencing these effects. Future research is needed to examine the eating behavior of persons who score high on the PFS and to develop interventions that directly target food cravings.
Appetite 02/2012; 58(3):806-13. · 2.59 Impact Factor
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ABSTRACT: Group-based brain connectivity networks have great appeal for researchers interested in gaining further insight into complex brain function and how it changes across different mental states and disease conditions. Accurately constructing these networks presents a daunting challenge given the difficulties associated with accounting for inter-subject topological variability. Viable approaches to this task must engender networks that capture the constitutive topological properties of the group of subjects' networks that it is aiming to represent. The conventional approach has been to use a mean or median correlation network (Achard et al., 2006; Song et al., 2009; Zuo et al., 2011) to embody a group of networks. However, the degree to which their topological properties conform with those of the groups that they are purported to represent has yet to be explored. Here we investigate the performance of these mean and median correlation networks. We also propose an alternative approach based on an exponential random graph modeling framework and compare its performance to that of the aforementioned conventional approach. Simpson et al. (2011) illustrated the utility of exponential random graph models (ERGMs) for creating brain networks that capture the topological characteristics of a single subject's brain network. However, their advantageousness in the context of producing a brain network that "represents" a group of brain networks has yet to be examined. Here we show that our proposed ERGM approach outperforms the conventional mean and median correlation based approaches and provides an accurate and flexible method for constructing group-based representative brain networks.
NeuroImage 01/2012; 60(2):1117-26. · 5.89 Impact Factor
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ABSTRACT: The obesity epidemic had spawned considerable interest in understanding peoples' responses to palatable food cues that are plentiful in obesogenic environments. In this paper we examine how trait mindfulness of older, obese adults may moderate brain networks that arise from exposure to such cues. Nineteen older, obese adults came to our laboratory on two different occasions. Both times they ate a controlled breakfast meal and then were restricted from eating for 2.5 h. After this brief period of food restriction, they had an fMRI scan in which they were exposed to food cues and then underwent a 5 min recovery period to evaluate brain networks at rest. On one day they consumed a BOOST® liquid meal prior to scanning, whereas on the other day they only consumed water (NO BOOST® condition). We found that adults high in trait mindfulness were able to return to their default mode network (DMN), as indicated by greater global efficiency in the precuneus, during the post-exposure rest period. This effect was stronger for the BOOST® than NO BOOST® treatment condition. Older adults low in trait mindfulness did not exhibit this pattern in the DMN. In fact, the brain networks of those low on the MAAS suggests that they continued to be pre-occupied with the elaboration of food cues even after cue exposure had ended. Further work is needed to examine whether mindfulness-based therapies alter brain networks to food cues and whether these changes are related to eating behavior.
Frontiers in aging neuroscience. 01/2012; 4:13.
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ABSTRACT: Recently, we introduced a dynamic functional model of the human brain. This model, representing functional connectivity in the brain, is generated from subject-specific physiological data collected using functional magnetic resonance imaging (fMRI). The dynamics of this model are examined using agent-based modeling techniques, wherein a collection of binary agents are embedded as nodes in the network. This model is capable of producing a wide variety of complex behaviors. In this work, we use machine learning techniques to drive the model to produce desired behaviors. The solution space of the model is unreasonably large for a brute-force approach, but we demonstrate that genetic algorithms (GAs) are able to locate optimal model parameters within this space to achieve the desired behavior. We detail the design of a GA specifically suited for this model, and discuss the relevant issues that arise in GA design. Specifically, we explore several fitness functions to accurately quantify the suitability of each potential solution. We examine their strengths and weaknesses, and identify an optimal fitness function for this system. We validate the GA with the optimal fitness function by showing that it can drive the system to produce pre-defined behaviors. The ability of the model to produce pre-defined behaviors indicates that it may be possible to produce physiologically relevant outputs. The model may be very useful for studying the changes in brain dynamics due to neurological diseases or conditions. Additionally, this powerful dynamic brain model may be instrumental in many artificial intelligence settings.
Biomedical sciences instrumentation 01/2012; 48:210-7.
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ABSTRACT: At rest, spontaneous brain activity measured by fMRI is summarized by a number of distinct resting state networks (RSNs) following similar temporal time courses. Such networks have been consistently identified across subjects using spatial ICA (independent component analysis). Moreover, graph theory-based network analyses have also been applied to resting-state fMRI data, identifying similar RSNs, although typically at a coarser spatial resolution. In this work, we examined resting-state fMRI networks from 194 subjects at a voxel-level resolution, and examined the consistency of RSNs across subjects using a metric called scaled inclusivity (SI), which summarizes consistency of modular partitions across networks. Our SI analyses indicated that some RSNs are robust across subjects, comparable to the corresponding RSNs identified by ICA. We also found that some commonly reported RSNs are less consistent across subjects. This is the first direct comparison of RSNs between ICAs and graph-based network analyses at a comparable resolution.
PLoS ONE 01/2012; 7(8):e44428. · 4.09 Impact Factor
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ABSTRACT: There is an abundance of literature on complex networks describing a variety of relationships among units in social, biological, and technological systems. Such networks, consisting of interconnected nodes, are often self-organized, naturally emerging without any overarching designs on topological structure yet enabling efficient interactions among nodes. Here we show that the number of nodes and the density of connections in such self-organized networks exhibit a power law relationship. We examined the size and connection density of 47 self-organizing networks of various biological, social, and technological origins, and found that the size-density relationship follows a fractal relationship spanning over 6 orders of magnitude. This finding indicates that there is an optimal connection density in self-organized networks following fractal scaling regardless of their sizes.
Physica A Statistical and Theoretical Physics 10/2011; 390(20):3608-3613. · 1.37 Impact Factor
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ABSTRACT: Exponential random graph models (ERGMs), also known as p* models, have been utilized extensively in the social science literature to study complex networks and how their global structure depends on underlying structural components. However, the literature on their use in biological networks (especially brain networks) has remained sparse. Descriptive models based on a specific feature of the graph (clustering coefficient, degree distribution, etc.) have dominated connectivity research in neuroscience. Corresponding generative models have been developed to reproduce one of these features. However, the complexity inherent in whole-brain network data necessitates the development and use of tools that allow the systematic exploration of several features simultaneously and how they interact to form the global network architecture. ERGMs provide a statistically principled approach to the assessment of how a set of interacting local brain network features gives rise to the global structure. We illustrate the utility of ERGMs for modeling, analyzing, and simulating complex whole-brain networks with network data from normal subjects. We also provide a foundation for the selection of important local features through the implementation and assessment of three selection approaches: a traditional p-value based backward selection approach, an information criterion approach (AIC), and a graphical goodness of fit (GOF) approach. The graphical GOF approach serves as the best method given the scientific interest in being able to capture and reproduce the structure of fitted brain networks.
PLoS ONE 01/2011; 6(5):e20039. · 4.09 Impact Factor
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ABSTRACT: The network-based approach has been used to describe the relationship among genes and various phenotypes, producing a network describing complex biological relationships. Such networks can be constructed by aggregating previously reported associations in the literature from various databases. In this work, we applied the network-based approach to investigate how different brain areas are associated to genetic disorders and genes. In particular, a tripartite network with genes, genetic diseases, and brain areas was constructed based on the associations among them reported in the literature through text mining. In the resulting network, a disproportionately large number of gene-disease and disease-brain associations were attributed to a small subset of genes, diseases, and brain areas. Furthermore, a small number of brain areas were found to be associated with a large number of the same genes and diseases. These core brain regions encompassed the areas identified by the previous genome-wide association studies, and suggest potential areas of focus in the future imaging genetics research. The approach outlined in this work demonstrates the utility of the network-based approach in studying genetic effects on the brain.
PLoS ONE 01/2011; 6(6):e20907. · 4.09 Impact Factor
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ABSTRACT: The study of the brain as a whole system can be accomplished using network theory principles. Research has shown that human functional brain networks during a resting state exhibit small-world properties and high degree nodes, or hubs, localized to brain areas consistent with the default mode network. However, the study of brain networks across different tasks and or cognitive states has been inconclusive. Research in this field is important because the underpinnings of behavioral output are inherently dependent on whether or not brain networks are dynamic. This is the first comprehensive study to evaluate multiple network metrics at a voxel-wise resolution in the human brain at both the whole-brain and regional level under various conditions: resting state, visual stimulation, and multisensory (auditory and visual stimulation). Our results show that despite global network stability, functional brain networks exhibit considerable task-induced changes in connectivity, efficiency, and community structure at the regional level.
Frontiers in Human Neuroscience 01/2011; 5:83. · 2.34 Impact Factor
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CoRR. 01/2011; abs/1106.0041.
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ABSTRACT: Although graph theory has been around since the 18th century, the field of network science is more recent and continues to gain popularity, particularly in the field of neuroimaging. The field was propelled forward when Watts and Strogatz introduced their small-world network model, which described a network that provided regional specialization with efficient global information transfer. This model is appealing to the study of brain connectivity, as the brain can be viewed as a system with various interacting regions that produce complex behaviors. In practice, graph metrics such as clustering coefficient, path length, and efficiency measures are often used to characterize system properties. Centrality metrics such as degree, betweenness, closeness, and eigenvector centrality determine critical areas within the network. Community structure is also essential for understanding network organization and topology. Network science has led to a paradigm shift in the neuroscientific community, but it should be viewed as more than a simple "tool du jour." To fully appreciate the utility of network science, a greater understanding of how network models apply to the brain is needed. An integrated appraisal of multiple network analyses should be performed to better understand network structure rather than focusing on univariate comparisons to find significant group differences; indeed, such comparisons, popular with traditional functional magnetic resonance imaging analyses, are arguably no longer relevant with graph-theory based approaches. These methods necessitate a philosophical shift toward complexity science. In this context, when correctly applied and interpreted, network scientific methods have a chance to revolutionize the understanding of brain function.
Brain connectivity. 01/2011; 1(4):295-308.
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ABSTRACT: Small-world networks, according to Watts and Strogatz, are a class of networks that are "highly clustered, like regular lattices, yet have small characteristic path lengths, like random graphs." These characteristics result in networks with unique properties of regional specialization with efficient information transfer. Social networks are intuitive examples of this organization, in which cliques or clusters of friends being interconnected but each person is really only five or six people away from anyone else. Although this qualitative definition has prevailed in network science theory, in application, the standard quantitative application is to compare path length (a surrogate measure of distributed processing) and clustering (a surrogate measure of regional specialization) to an equivalent random network. It is demonstrated here that comparing network clustering to that of a random network can result in aberrant findings and that networks once thought to exhibit small-world properties may not. We propose a new small-world metric, ω (omega), which compares network clustering to an equivalent lattice network and path length to a random network, as Watts and Strogatz originally described. Example networks are presented that would be interpreted as small-world when clustering is compared to a random network but are not small-world according to ω. These findings have important implications in network science because small-world networks have unique topological properties, and it is critical to accurately distinguish them from networks without simultaneous high clustering and short path length.
Brain connectivity. 01/2011; 1(5):367-75.
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ABSTRACT: In addition to cognitive and emotional processing dysfunction, chronic cocaine users are also impaired at simple sensorimotor tasks. Many diseases characterized by compulsive movements, repetitive actions, impaired attention and planning are associated with dysfunction in frontal-striatal circuits. The aim of this study was to determine whether cocaine users had impaired frontal-striatal connectivity during a simple movement task and whether this was associated with sensorimotor impairment.
Functional MRI data were collected from 14 non-treatment seeking cocaine users and 15 healthy controls as they performed a finger-tapping task. Functional coupling was quantified by correlating the timecourses of each pair of anatomically connected regions of interest. Behavioral performance was correlated with all functional coupling coefficients.
In controls there was a significant relationship between the primary motor cortex and the supplementary motor area (SMA), as well as the SMA and the dorsal striatum during ongoing movement. Cocaine users exhibited weaker fronto-striatal coupling than controls, while the cortical-cortical coupling was intact. Coupling strength between the SMA and the caudate was negatively correlated with reaction time in the users.
The observation that cocaine users have impaired cortical-striatal connectivity during simple motor performance, suggests that these individuals may have a fundamental deficit in information processing that influences more complex cognitive processes.
Drug and alcohol dependence 12/2010; 115(3):240-3. · 3.60 Impact Factor
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Tennille D Presley,
Ashley R Morgan,
Erika Bechtold,
William Clodfelter,
Robin W Dove,
Janine M Jennings,
Robert A Kraft,
S Bruce King, Paul J Laurienti,
W Jack Rejeski,
Jonathan H Burdette,
Daniel B Kim-Shapiro,
Gary D Miller
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ABSTRACT: Poor blood flow and hypoxia/ischemia contribute to many disease states and may also be a factor in the decline of physical and cognitive function in aging. Nitrite has been discovered to be a vasodilator that is preferentially harnessed in hypoxia. Thus, both infused and inhaled nitrite are being studied as therapeutic agents for a variety of diseases. In addition, nitrite derived from nitrate in the diet has been shown to decrease blood pressure and improve exercise performance. Thus, dietary nitrate may also be important when increased blood flow in hypoxic or ischemic areas is indicated. These conditions could include age-associated dementia and cognitive decline. The goal of this study was to determine if dietary nitrate would increase cerebral blood flow in older adults.
In this investigation we administered a high vs. low nitrate diet to older adults (74.7±6.9 years) and measured cerebral perfusion using arterial spin labeling magnetic resonance imaging. We found that the high nitrate diet did not alter global cerebral perfusion, but did lead to increased regional cerebral perfusion in frontal lobe white matter, especially between the dorsolateral prefrontal cortex and anterior cingulate cortex.
These results suggest that dietary nitrate may be useful in improving regional brain perfusion in older adults in critical brain areas known to be involved in executive functioning.
Nitric Oxide 10/2010; 24(1):34-42. · 3.55 Impact Factor
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Barry E Stein,
David Burr,
Christos Constantinidis, Paul J Laurienti,
M Alex Meredith,
Thomas J Perrault,
Ramnarayan Ramachandran,
Brigitte Röder,
Benjamin A Rowland,
K Sathian,
Charles E Schroeder,
Ladan Shams,
Terrence R Stanford,
Mark T Wallace,
Liping Yu,
David J Lewkowicz
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ABSTRACT: There is now a good deal of data from neurophysiological studies in animals and behavioral studies in human infants regarding the development of multisensory processing capabilities. Although the conclusions drawn from these different datasets sometimes appear to conflict, many of the differences are due to the use of different terms to mean the same thing and, more problematic, the use of similar terms to mean different things. Semantic issues are pervasive in the field and complicate communication among groups using different methods to study similar issues. Achieving clarity of communication among different investigative groups is essential for each to make full use of the findings of others, and an important step in this direction is to identify areas of semantic confusion. In this way investigators can be encouraged to use terms whose meaning and underlying assumptions are unambiguous because they are commonly accepted. Although this issue is of obvious importance to the large and very rapidly growing number of researchers working on multisensory processes, it is perhaps even more important to the non-cognoscenti. Those who wish to benefit from the scholarship in this field but are unfamiliar with the issues identified here are most likely to be confused by semantic inconsistencies. The current discussion attempts to document some of the more problematic of these, begin a discussion about the nature of the confusion and suggest some possible solutions.
European Journal of Neuroscience 05/2010; 31(10):1713-20. · 3.63 Impact Factor
