Accuracy analysis of fMRI and MEG activations determined by intraoperative mapping.

OBJECTIVE: By looking at how the accuracy of preoperative brain mapping methods vary according to differences in the distance from the activation clusters used for the analysis, the present study aimed to elucidate how preoperative functional neuroimaging may be used in such a way that maximizes the mapping accuracy. METHODS: The eloquent function of 19 patients with a brain tumor or cavernoma was mapped prior to resection with both functional MRI (fMRI) and magnetoencephalography (MEG). The mapping results were then validated using direct cortical stimulation mapping performed immediately after craniotomy and prior to resection. The subset of patients with equivalent MEG and fMRI tasks performed for motor (n = 14) and language (n = 12) were evaluated as both individual and combined predictions. Furthermore, the distance resulting in the maximum accuracy, as evaluated by the J statistic, was determined by plotting the sensitivities and specificities against a linearly increasing distance threshold. RESULTS: fMRI showed a maximum mapping accuracy at 5 mm for both motor and language mapping. MEG showed a maximum mapping accuracy at 40 mm for motor and 15 mm for language mapping. At the standard 10-mm distance used in the literature, MEG showed a greater specificity than fMRI for both motor and language mapping but a lower sensitivity for motor mapping. Combining MEG and fMRI showed a maximum accuracy at 15 mm and 5 mm-MEG and fMRI distances, respectively-for motor mapping and at a 10-mm distance for both MEG and fMRI for language mapping. For motor mapping, combining MEG and fMRI at the optimal distances resulted in a greater accuracy than the maximum accuracy of the individual predictions. CONCLUSIONS: This study demonstrates that the accuracy of language and motor mapping for both fMRI and MEG is heavily dependent on the distance threshold used in the analysis. Furthermore, combining MEG and fMRI showed the potential for increased motor mapping accuracy compared to when using the modalities separately.Clinical trial registration no.: NCT01535430 (clinicaltrials.gov).

Anti-Fragmentation of Resting-State Functional Magnetic Resonance Imaging Connectivity Networks with Node-Wise Thresholding.

Functional magnetic resonance imaging (fMRI)-based functional connectivity networks are often constructed by thresholding a correlation matrix of nodal time courses. In a typical thresholding approach known as hard thresholding, a single threshold is applied to the entire correlation matrix to identify edges representing superthreshold correlations. However, hard thresholding is known to produce a network with uneven allocation of edges, resulting in a fragmented network with a large number of disconnected nodes. It is suggested that an alternative network thresholding approach, node-wise thresholding, is able to overcome these problems. To examine this, various network characteristics were compared between networks constructed by hard thresholding and node-wise thresholding, with publicly available resting-state fMRI data from 123 healthy young subjects. It was found that networks constructed with hard thresholding included a large number of disconnected nodes, while such network fragmentation was not observed in networks formed with node-wise thresholding. Moreover, in hard thresholding networks, fragmentized modular organization was observed, characterized by a large number of small modules. On the contrary, such modular fragmentation was not observed in node-wise thresholding networks, producing modules that were robust at any threshold and highly consistent across subjects. These results indicate that node-wise thresholding may lead to less fragmented networks. Moreover, node-wise thresholding enables robust characterization of network properties without much influence by the selection of a threshold.

Enhancing tissue permeability with MRI guided preclinical focused ultrasound system in rabbit muscle: From normal tissue to VX2 tumor.

High Intensity Focused Ultrasound (HIFU) is an emerging noninvasive, nonionizing physical energy based modality to ablate solid tumors with high power, or increase local permeability in tissues/tumors in pulsed mode with relatively low power. Compared with traditional ablative HIFU, nondestructive pulsed HIFU (pHIFU) is present in the majority of novel applications recently developed for enhancing the delivery of drugs and genes. Previous studies have demonstrated the capability of pHIFU to change tissue local permeability for enhanced drug delivery in both mouse tumors and mouse muscle. Further study based on bulk tissues in large animals and clinical HIFU system revealed correlation between therapeutic effect and thermal parameters, which was absent in the previous mouse studies. In this study, we further investigated the relation between the therapeutic effect of pHIFU and thermal parameters in bulky normal muscle tissues based on a rabbit model and a preclinical HIFU system. Correlation between therapeutic effect and thermal parameters was confirmed in our study on the same bulk tissues although different HIFU systems were used. Following the study in bulky normal muscle tissues, we further created bulky tumor model with VX2 tumors implanted on both hind limbs of rabbits and investigated the feasibility to enhance tumor permeability in bulky VX2 tumors in a rabbit model using pHIFU technique. A radiolabeled peptidomimetic integrin antagonist, 111In-DOTA-IA, was used following pHIFU treatment in our study to target VX2 tumor and serve as the radiotracer for follow-up single-photon emission computed tomography (SPECT) scanning. The results have shown significantly elevated uptake of 111In-DOTA-IA in the area of VX2 tumors pretreated by pHIFU compared with the control VX2 tumors not being pretreated by pHIFU, and statistical analysis revealed averaged 34.5% enhancement 24h after systematic delivery of 111In-DOTA-IA in VX2 tumors pretreated by pHIFU compared with the control VX2 tumors.

Relative differences in resting-state brain connectivity associated with long term intensive lifestyle intervention.

A number of studies have reported that type 2 diabetes mellitus (T2DM) is associated with alterations in resting-state activity and connectivity in the brain. There is also evidence that interventions involving physical activity and weight loss may affect brain functional connectivity. In this study, we examined the effects of nearly 10 years of an intensive lifestyle intervention (ILI), designed to induce and sustain weight loss through lower caloric intake and increased physical activity, on resting-state networks in adults with T2DM. We performed a cross-sectional comparison of global and local characteristics from functional brain networks between individuals who had been randomly assigned to ILI or a control condition of health education and support. Upon examining brain networks from 312 participants (average age: 68.8 for ILI and 67.9 for controls), we found that ILI participants (N=160) had attenuated local efficiency at the network-level compared with controls (N=152). Although there was no group difference in the network-level global efficiency, we found that, among ILI participants, nodal global efficiency was elevated in left fusiform gyrus, right middle frontal gyrus, and pars opercularis of right inferior frontal gyrus. These effects were age-dependent, with more pronounced effects for older participants. Overall these results indicate that the individuals assigned to the ILI had brain networks with less regional and more global connectivity, particularly involving frontal lobes. Such patterns would support greater distributed information processing. Future studies are needed to determine if these differences are associated with age-related compensatory function in the ILI group or worse pathology in the control group.

Remote spatiotemporally controlled and biologically selective permeabilization of blood-brain barrier.

The blood-brain barrier (BBB), comprised of brain endothelial cells with tight junctions (TJ) between them, regulates the extravasation of molecules and cells into and out of the central nervous system (CNS). Overcoming the difficulty of delivering therapeutic agents to specific regions of the brain presents a major challenge to treatment of a broad range of brain disorders. Current strategies for BBB opening are invasive, not specific, and lack precise control over the site and timing of BBB opening, which may limit their clinical translation. In the present report, we describe a novel approach based on a combination of stem cell delivery, heat-inducible gene expression and mild heating with high-intensity focused ultrasound (HIFU) under MRI guidance to remotely permeabilize BBB. The permeabilization of the BBB will be controlled with, and limited to where selected pro-inflammatory factors will be secreted secondary to HIFU activation, which is in the vicinity of the engineered stem cells and consequently both the primary and secondary disease foci. This therapeutic platform thus represents a non-invasive way for BBB opening with unprecedented spatiotemporal precision, and if properly and specifically modified, can be clinically translated to facilitate delivery of different diagnostic and therapeutic agents which can have great impact in treatment of various disease processes in the central nervous system.

Functional brain networks formed using cross-sample entropy are scale free.

Over the previous decade, there has been an explosion of interest in network science, in general, and its application to the human brain, in particular. Most brain network investigations to date have used linear correlations (LinCorr) between brain areas to construct and then interpret brain networks. In this study, we applied an entropy-based method to establish functional connectivity between brain areas. This method is sensitive to both nonlinear and linear associations. The LinCorr-based and entropy-based techniques were applied to resting-state functional magnetic resonance imaging data from 10 subjects, and the resulting networks were compared. The networks derived from the entropy-based method exhibited power-law degree distributions. Moreover, the entropy-based networks had a higher clustering coefficient and a shorter path length compared with that of the LinCorr-based networks. While the LinCorr-based networks were assortative, with nodes with similar degrees preferentially connected, the entropy-based networks were disassortative, with high-degree hubs directly connected to low-degree nodes. It is likely that the differences in clustering and assortativity are due to "mega-hubs" in the entropy-based networks. These mega-hubs connect to a large majority of the nodes in the network. This is the first work clearly demonstrating differences between functional brain networks using linear and nonlinear techniques. The key finding is that the nonlinear technique produced networks with scale-free degree distributions. There remains debate among the neuroscience community as to whether human brains are scale free. These data support the argument that at least some aspects of the human brain are perhaps scale free.

Age-related differences in advantageous decision making are associated with distinct differences in functional community structure.

Human decision making is dependent on not only the function of several brain regions but also their synergistic interaction. The specific function of brain areas within the ventromedial prefrontal cortex has long been studied in an effort to understand choice evaluation and decision making. These data specifically focus on whole-brain functional interconnectivity using the principles of network science. The Iowa Gambling Task (IGT) was the first neuropsychological task used to model real-life decisions in a way that factors reward, punishment, and uncertainty. Clinically, it has been used to detect decision-making impairments characteristic of patients with prefrontal cortex lesions. Here, we used performance on repeated blocks of the IGT as a behavioral measure of advantageous and disadvantageous decision making in young and mature adults. Both adult groups performed poorly by predominately making disadvantageous selections in the beginning stages of the task. In later phases of the task, young adults shifted to more advantageous selections and outperformed mature adults. Modularity analysis revealed stark underlying differences in visual, sensorimotor and medial prefrontal cortex community structure. In addition, changes in orbitofrontal cortex connectivity predicted behavioral deficits in IGT performance. Contrasts were driven by a difference in age but may also prove relevant to neuropsychiatric disorders associated with poor decision making, including the vulnerability to alcohol and/or drug addiction.

A permutation testing framework to compare groups of brain networks.

Brain network analyses have moved to the forefront of neuroimaging research over the last decade. However, methods for statistically comparing groups of networks have lagged behind. These comparisons 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. Current comparison approaches generally either rely on a summary metric or on mass-univariate nodal or edge-based comparisons that ignore the inherent topological properties of the network, yielding little power and failing to make network level comparisons. Gleaning deeper insights into normal and abnormal changes in complex brain function demands methods that take advantage of the wealth of data present in an entire brain network. Here we propose a permutation testing framework that allows comparing groups of networks while incorporating topological features inherent in each individual network. We validate our approach using simulated data with known group differences. We then apply the method to functional brain networks derived from fMRI data.

The influence of subclinical cardiovascular disease and related risk factors on cognition in type 2 diabetes mellitus: The DHS-Mind study.

We hypothesized that measures of coronary artery calcified plaque (CAC) collected at baseline from the Diabetes Heart Study (DHS) would explain associations between cognition and diabetes collected at follow-up approximately 7 years later. The DHS is a sibling study of cardiovascular disease (CVD) in a cohort with a high prevalence of type 2 diabetes (~80%). Associations between baseline CAC and cognitive performance were tested using generalized estimating equations and mixed effects models to adjust for familial relationships. Diabetes status was associated (p<0.05) with poorer performance on tests of verbal memory, processing speed, and semantic fluency adjusting for age, sex, education, and hypertension status. As hypothesized, including CAC in the statistical model attenuated this association. Additionally, CAC and fasting glucose predicted performance in tasks not associated with diabetes status in this study (Stroop Task, Phonemic Fluency). These results confirm work attributing the heterogeneity of cognitive outcomes in type 2 diabetes to subclinical risk factors that combine to affect different aspects of brain function. Importantly, these results imply that risk factor intervention should begin before comorbidities, particularly CVD, become clinically apparent.

The human functional brain network demonstrates structural and dynamical resilience to targeted attack.

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.

Functional connectivity networks with and without global signal correction.

In functional connectivity analyses in BOLD (blood oxygenation level dependent) fMRI data, there is an ongoing debate on whether to correct global signals in fMRI time series data. Although the discussion has been ongoing in the fMRI community since the early days of fMRI data analyses, this subject has gained renewed attention in recent years due to the surging popularity of functional connectivity analyses, in particular graph theory-based network analyses. However, the impact of correcting (or not correcting) for global signals has not been systematically characterized in the context of network analyses. Thus, in this work, I examined the effect of global signal correction on an fMRI network analysis. In particular, voxel-based resting-state fMRI networks were constructed with and without global signal correction. The resulting functional connectivity networks were compared. Without global signal correction, the distributions of the correlation coefficients were positively biased. I also found that, without global signal correction, nodes along the interhemisphic fissure were highly connected whereas some nodes and subgraphs around white-matter tracts became disconnected from the rest of the network. These results from this study show differences between the networks with or without global signal correction.

Consistency of network modules in resting-state FMRI connectome data.

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.

Complexity in a brain-inspired agent-based model.

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.

Development of PowerMap: a software package for statistical power calculation in neuroimaging studies.

Although there are a number of statistical software tools for voxel-based massively univariate analysis of neuroimaging data, such as fMRI (functional MRI), PET (positron emission tomography), and VBM (voxel-based morphometry), very few software tools exist for power and sample size calculation for neuroimaging studies. Unlike typical biomedical studies, outcomes from neuroimaging studies are 3D images of correlated voxels, requiring a correction for massive multiple comparisons. Thus, a specialized power calculation tool is needed for planning neuroimaging studies. To facilitate this process, we developed a software tool specifically designed for neuroimaging data. The software tool, called PowerMap, implements theoretical power calculation algorithms based on non-central random field theory. It can also calculate power for statistical analyses with FDR (false discovery rate) corrections. This GUI (graphical user interface)-based tool enables neuroimaging researchers without advanced knowledge in imaging statistics to calculate power and sample size in the form of 3D images. In this paper, we provide an overview of the statistical framework behind the PowerMap tool. Three worked examples are also provided, a regression analysis, an ANOVA (analysis of variance), and a two-sample T-test, in order to demonstrate the study planning process with PowerMap. We envision that PowerMap will be a great aide for future neuroimaging research.

Power of food moderates food craving, perceived control, and brain networks following a short-term post-absorptive state in older adults.

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.

Assessing the consistency of community structure in complex networks.

In recent years, community structure has emerged as a key component of complex network analysis. As more data have been collected, researchers have begun investigating changing community structure across multiple networks. Several methods exist to analyze changing communities, but most of these are limited to evolution of a single network over time. In addition, most of the existing methods are more concerned with change at the community level than at the level of the individual node. In this paper, we introduce scaled inclusivity, which is a method to quantify the change in community structure across networks. Scaled inclusivity evaluates the consistency of the classification of every node in a network independently. In addition, the method can be applied cross sectionally as well as longitudinally. In this paper, we calculate the scaled inclusivity for a set of simulated networks of United States cities and a set of real networks consisting of teams that play in the top division of American college football. We found that scaled inclusivity yields reasonable results for the consistency of individual nodes in both sets of networks. We propose that scaled inclusivity may provide a useful way to quantify the change in a network's community structure.

Universal fractal scaling of self-organized networks.

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.

A network of genes, genetic disorders, and brain areas.

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.

Exponential random graph modeling for complex brain networks.

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.

Oscillatory MEG motor activity reflects therapy-related plasticity in stroke patients.

BACKGROUND: A goal of stroke rehabilitation is to harness the capacity of the brain to reorganize following neurological damage and enable restoration of function. OBJECTIVE: To understand how neural oscillatory motor responses change following a therapeutic intervention and to illuminate whether these neurophysiological alterations correlate with improvements on behavioral measurements. METHODS: Magnetoencephalography (MEG) was used to evaluate plasticity in motor networks following 2 weeks of intensive task-oriented therapy, which was paired with sham or peripheral nerve stimulation (PNS). Patients completed unilateral finger tapping before and 3 weeks after therapy as whole-head MEG data were acquired. MEG data were imaged using beamforming, and the resulting event-related synchronizations and desynchronizations (ERSs/ERDs) were subjected to region-of-interest (ROI) analyses. For each ROI, the authors compared the baseline and postintervention MEG response amplitude, volume, and peak location for premovement ß ERD, movement-onset γ ERS, and postmovement ß ERS. RESULTS: Following therapy, all patients showed reduced postmovement ß ERS response amplitudes in bilateral precentral gyri and reduced γ ERS amplitudes in the precentral gyrus of the affected hemisphere. This latter response also distinguished treatment groups, as the posttherapy γ reduction was greater in patients who received PNS. Finally, both ß and γ response amplitudes were significantly correlated with improvement on several behavioral indices of motor function. DISCUSSION: These case-series data indicate that oscillatory MEG responses may be useful in gauging plasticity in motor cortices following therapy in stroke patients.