Hierarchical modularity in human brain functional networks

The idea that complex systems have a hierarchical modular organization originates in the early 1960s and has recently attracted fresh support from quantitative studies of large scale, real-life networks. Here we investigate the hierarchical modular (or "modules-within-modules") decomposition of human brain functional networks, measured using functional magnetic resonance imaging (fMRI) in 18 healthy volunteers under no-task or resting conditions. We used a customized template to extract networks with more than 1800 regional nodes, and we applied a fast algorithm to identify nested modular structure at several hierarchical levels. We used mutual information, 0 < I < 1, to estimate the similarity of community structure of networks in different subjects, and to identify the individual network that is most representative of the group. Results show that human brain functional networks have a hierarchical modular organization with a fair degree of similarity between subjects, I=0.63. The largest 5 modules at the highest level of the hierarchy were medial occipital, lateral occipital, central, parieto-frontal and fronto-temporal systems; occipital modules demonstrated less sub-modular organization than modules comprising regions of multimodal association cortex. Connector nodes and hubs, with a key role in inter-modular connectivity, were also concentrated in association cortical areas. We conclude that methods are available for hierarchical modular decomposition of large numbers of high resolution brain functional networks using computationally expedient algorithms. This could enable future investigations of Simon's original hypothesis that hierarchy or near-decomposability of physical symbol systems is a critical design feature for their fast adaptivity to changing environmental conditions

A specific brain structural basis for individual differences in reality monitoring.

Much recent interest has centered on understanding the relationship between brain structure variability and individual differences in cognition, but there has been little progress in identifying specific neuroanatomical bases of such individual differences. One cognitive ability that exhibits considerable variability in the healthy population is reality monitoring; the cognitive processes used to introspectively judge whether a memory came from an internal or external source (e.g., whether an event was imagined or actually occurred). Neuroimaging research has implicated the medial anterior prefrontal cortex (PFC) in reality monitoring, and here we sought to determine whether morphological variability in a specific anteromedial PFC brain structure, the paracingulate sulcus (PCS), might underlie performance. Fifty-three healthy volunteers were selected on the basis of MRI scans and classified into four groups according to presence or absence of the PCS in their left or right hemisphere. The group with absence of the PCS in both hemispheres showed significantly reduced reality monitoring performance and ability to introspect metacognitively about their performance when compared with other participants. Consistent with the prediction that sulcal absence might mean greater volume in the surrounding frontal gyri, voxel-based morphometry revealed a significant negative correlation between anterior PFC gray matter and reality monitoring performance. The findings provide evidence that individual differences in introspective abilities like reality monitoring may be associated with specific structural variability in the PFC

Network Scaling Effects in Graph Analytic Studies of Human Resting-State fMRI Data

Graph analysis has become an increasingly popular tool for characterizing topological properties of brain connectivity networks. Within this approach, the brain is modeled as a graph comprising N nodes connected by M edges. In functional magnetic resonance imaging (fMRI) studies, the nodes typically represent brain regions and the edges some measure of interaction between them. These nodes are commonly defined using a variety of regional parcellation templates, which can vary both in the volume sampled by each region, and the number of regions parcellated. Here, we sought to investigate how such variations in parcellation templates affect key graph analytic measures of functional brain organization using resting-state fMRI in 30 healthy volunteers. Seven different parcellation resolutions (84, 91, 230, 438, 890, 1314, and 4320 regions) were investigated. We found that gross inferences regarding network topology, such as whether the brain is small-world or scale-free, were robust to the template used, but that both absolute values of, and individual differences in, specific parameters such as path length, clustering, small-worldness, and degree distribution descriptors varied considerably across the resolutions studied. These findings underscore the need to consider the effect that a specific parcellation approach has on graph analytic findings in human fMRI studies, and indicate that results obtained using different templates may not be directly comparable

Development of a Silent Speech Interface for Augmented Reality Applications

Adoption of Augmented and Virtual Reality (AR and VR) interfaces in the aerospace and defense fields has been inhibited by conspicuous and cumbersome input mechanisms such as gestures and spoken voice recognition. Silent speech interfaces using non-invasive electromyography (EMG) sensors are posited as a means for controlling AR and VR interfaces with potential for inconspicuous and high bandwidth input. Our objective is to develop a silent speech interface that receives input from subvocalizations via skin surface EMG sensors, which is then decoded into commands for controlling a heads-up-display built on a Microsoft HoloLens. EMG sensors are placed on the Digastric, Stylohyoid, Sternohyoid, and Cricothyroid muscles located on the anterior cervical region. The collected data is used to train a convolutional neural network that functions as a classifier, determining the subject’s subvocal input against a word library. The user will equip the wearable interface and use it to silently send commands through subvocalizations to control an AR device. Effectiveness of the wearable interface will be defined by word recognition accuracies in mouthed trials using the current command library. Future work includes expanding the dataset used to train the recognition model and live demonstration in controlling an augmented reality interface

The impact of input node placement in the controllability of brain networks

Network control theory can be used to model how one should steer the brain between different states by driving a specific region with an input. The needed energy to control a network is often used to quantify its controllability, and controlling brain networks requires diverse energy depending on the selected input region. We use the theory of how input node placement affects the longest control chain (LCC) in the controllability of brain networks to study the role of the architecture of white matter fibers in the required control energy. We show that the energy needed to control human brain networks is related to the LCC, i.e., the longest distance between the input region and other regions in the network. We indicate that regions that control brain networks with lower energy have small LCCs. These regions align with areas that can steer the brain around the state space smoothly. By contrast, regions that need higher energy to move the brain toward different target states have larger LCCs. We also investigate the role of the number of paths between regions in the control energy. Our results show that the more paths between regions, the lower cost needed to control brain networks. We evaluate the number of paths by counting specific motifs in brain networks since determining all paths in graphs is a difficult problem.Comment: 14 pages, 8 figure

Functional and Biochemical Alterations of the Medial Frontal Cortex in Obsessive-Compulsive Disorder

Context: The medial frontal cortex (MFC), including the dorsal anterior cingulate (dAC) and supplementary motor area (SMA), is critical for adaptive and inhibitory control of behaviour. Abnormally high MFC activity has been a consistent finding in functional neuroimaging studies of obsessive-compulsive disorder (OCD). However, the precise regions and the neural alterations associated with this abnormality remain unclear. Objective: To examine the functional and biochemical properties of the MFC in patients with OCD. Design: Cross-sectional design combining volume localized proton magnetic resonance spectroscopy (1H-MRS) and functional MRI (fMRI) with an inhibitory control paradigm (the Multi-Source Interference Task; MSIT) designed to activate the MFC. Setting: Healthy control participants and OCD patients recruited from the general community. Participants: Nineteen OCD patients (10 male, and 9 female) and nineteen age, gender, education and intelligence-matched healthy control participants. Main Outcome Measures: Psychometric measures of symptom severity, MSIT behavioural performance, blood-oxygen-level-dependent (BOLD) activation and 1H-MRS brain metabolite concentrations. Results: MSIT behavioural performance did not differ between OCD patients and control subjects. Reaction-time interference and response errors were correlated with BOLD activation in the dAC region in both groups. Relative to control subjects, OCD patients showed hyper- activation of the SMA during high response-conflict (incongruent > congruent) trials and hyper-activation of the rostral anterior cingulate (rAC) region during low response- conflict (incongruent < congruent) trials. OCD patients also showed reduced levels of neuronal N-acetylaspartate in the dAC region, which was negatively correlated with their BOLD activation of the region. Conclusions: Our findings suggest that hyper-activation of the medial frontal cortex in OCD patients may be a compensatory response to neural pathology in the region. This relationship may partly explain the nature of inhibitory control deficits that are frequently seen in this group and may serve as a focus of future treatment studies

Functional brain networks before the onset of psychosis : a prospective fMRI study with graph theoretical analysis

Individuals with an at-risk mental state (ARMS) have a risk of developing a psychotic disorder significantly greater than the general population. However, it is not currently possible to predict which ARMS individuals will develop psychosis from clinical assessment alone. Comparison of ARMS subjects who do, and do not, develop psychosis can reveal which factors are critical for the onset of illness. In the present study, 37 patients with an ARMS were followed clinically at least 24 months subsequent to initial referral. Functional MRI data were collected at the beginning of the follow-up period during performance of an executive task known to recruit frontal lobe networks and to be impaired in psychosis. Graph theoretical analysis was used to compare the organization of a functional brain network in ARMS patients who developed a psychotic disorder following the scan (ARMS-T) to those who did not become ill during the same follow-up period (ARMS-NT) and aged-matched controls. The global properties of each group's representative network were studied (density, efficiency, global average path length) as well as regionally-specific contributions of network nodes to the organization of the system (degree, farness-centrality, betweenness-centrality). We focused our analysis on the dorsal anterior cingulate cortex (ACC), a region known to support executive function that is structurally and functionally impaired in ARMS patients. In the absence of between-group differences in global network organization, we report a significant reduction in the topological centrality of the ACC in the ARMS-T group relative to both ARMS-NT and controls. These results provide evidence that abnormalities in the functional organization of the brain predate the onset of psychosis, and suggest that loss of ACC topological centrality is a potential biomarker for transition to psychosis

Range of Motion Evaluation of a Final Frontier Design IVA Spacesuit using Motion Capture

Embry-Riddle Aeronautical University’s Spacesuit Utilization of Innovative Technology Laboratory (S.U.I.T. Lab) is focused on improving human performance in spaceflight by concentrating on spacesuit research for intravehicular activities (IVA) and extravehicular activities (EVA). The S.U.I.T. Lab worked with Final Frontier Design (FFD) to provide a quantitative analysis protocol for seated arm mobility of their NASA Flight Opportunities Program (FOP) IVA spacesuit. The lab used reflective tracking markers on three test subjects and recorded a set of arm motions using OptiTrack’s infrared motion capture system. All motions were recorded in three spacesuit conditions including: unsuited; suited unpressurized; and suited pressurized (2.5 psid). Programs were developed in MATLAB to analyze and plot angular metrics as well as three-dimensional reach envelopes. These programs allow the spacesuit manufacturer to visualize the mobility of their spacesuit design and associate qualitative mobility characteristics with quantitative results in the form of angular and volumetric data. Embry-Riddle Aeronautical University’s Spacesuit Utilization of Innovative Technology Laboratory (S.U.I.T. Lab) is focused on improving human performance in spaceflight by concentrating on spacesuit research for intravehicular activities (IVA) and extravehicular activities (EVA). The design and execution of range of motion (ROM) protocols in an experimental setting will provide insight on the functions and restrictions of spacesuits, aiding in current and future designs or modification. The S.U.I.T. Lab worked with Final Frontier Design (FFD) to provide a quantitative analysis protocol for seated arm mobility of their NASA Flight Opportunities Program (FOP) IVA spacesuit. The lab used reflective tracking markers on three test subjects and recorded a set of arm ROMs using OptiTrack’s infrared motion capture system including: shoulder abduction/adduction; vertical and horizontal shoulder flexion/extension; and vertical and horizontal full-arm carveouts. All motions were recorded in three spacesuit conditions including: unsuited; suited unpressurized; and suited pressurized (2.5psid). Motion capture data was edited and filtered for mobility analysis calculations. Programs were developed in MATLAB to analyze and plot angular metrics as well asthree-dimensionalreach envelopes. These programs allow the spacesuit manufacturer to visualize the mobility of their spacesuit design and associate qualitative mobility characteristics with quantitative results in the form of angular and volumetric data.The percentages of mobility retained between all spacesuit conditionsreveal a quantifiable reduction in mobilitygoing from unsuited to suited unpressurized to suited pressurized.Based off the performance of this investigation, FFD gathered preliminary data regarding the mobility of their NASA FOP spacesuit. Improvements to the equipment and protocol used by the lab for motion capture and analysis have been implemented since this study. Expanding from four to nine motion capture cameras, the lab has been able to capture spacesuit mobility data with far greater accuracy and completeness.Updated prescribed motion protocols instruct subjects to maintain straight arms reaching as far as comfortable and across their body in some cases, which is done to characterize shoulder mobility and is not reflective of the spacesuit’s maximum mobility

Consistency and differences between centrality measures across distinct classes of networks

The roles of different nodes within a network are often understood through centrality analysis, which aims to quantify the capacity of a node to influence, or be influenced by, other nodes via its connection topology. Many different centrality measures have been proposed, but the degree to which they offer unique information, and such whether it is advantageous to use multiple centrality measures to define node roles, is unclear. Here we calculate correlations between 17 different centrality measures across 212 diverse real-world networks, examine how these correlations relate to variations in network density and global topology, and investigate whether nodes can be clustered into distinct classes according to their centrality profiles. We find that centrality measures are generally positively correlated to each other, the strength of these correlations varies across networks, and network modularity plays a key role in driving these cross-network variations. Data-driven clustering of nodes based on centrality profiles can distinguish different roles, including topological cores of highly central nodes and peripheries of less central nodes. Our findings illustrate how network topology shapes the pattern of correlations between centrality measures and demonstrate how a comparative approach to network centrality can inform the interpretation of nodal roles in complex networks.Comment: Main text (25 pages, 8 figures, 1 table), supplementary information (16 pages, 2 tables) and supplementary figures (17 figures

Modulation of Brain Resting-State Networks by Sad Mood Induction

BACKGROUND: There is growing interest in the nature of slow variations of the blood oxygen level-dependent (BOLD) signal observed in functional MRI resting-state studies. In humans, these slow BOLD variations are thought to reflect an underlying or intrinsic form of brain functional connectivity in discrete neuroanatomical systems. While these 'resting-state networks' may be relatively enduring phenomena, other evidence suggest that dynamic changes in their functional connectivity may also emerge depending on the brain state of subjects during scanning. METHODOLOGY/PRINCIPAL FINDINGS: In this study, we examined healthy subjects (n = 24) with a mood induction paradigm during two continuous fMRI recordings to assess the effects of a change in self-generated mood state (neutral to sad) on the functional connectivity of these resting-state networks (n = 24). Using independent component analysis, we identified five networks that were common to both experimental states, each showing dominant signal fluctuations in the very low frequency domain (approximately 0.04 Hz). Between the two states, we observed apparent increases and decreases in the overall functional connectivity of these networks. Primary findings included increased connectivity strength of a paralimbic network involving the dorsal anterior cingulate and anterior insula cortices with subjects' increasing sadness and decreased functional connectivity of the 'default mode network'. CONCLUSIONS/SIGNIFICANCE: These findings support recent studies that suggest the functional connectivity of certain resting-state networks may, in part, reflect a dynamic image of the current brain state. In our study, this was linked to changes in subjective mood