Measuring embeddedness: Hierarchical scale-dependent information exchange efficiency of the human brain connectome

The oblique effect: The relationship between profiles of visuospatial preference, cognition, and brain connectomics in older adults

The oblique effect (OE) describes the visuospatial advantage for identifying stimuli oriented horizontally or vertically rather than diagonally; little is known about brain aging and the OE. We investigated this relationship using the Judgment of Line Orientation (JLO) in 107 older adults (∼age = 67.8 ± 6.6; 51% female) together with neuropsychological tests of executive functioning (EF), attention/information processing (AIP), and neuroimaging. Only JLO lines falling between 36-54° or 126-144° were considered oblique. To quantify the oblique effect, we calculated z-scores for oblique errors (zOblique = #oblique errors/#oblique lines), and similarly, horizontal + vertical line errors (zHV), and a composite measure of oblique relative to HV errors (zOE). Composite z-scores of EF and AIP reflected domains associated with JLO performance. Graph theory analysis integrated T1-derived volumetry and diffusion MRI-derived white matter tractography into connectivity matrices analyzed for select network properties. Participants produced more zOblique than zHV errors (p < 0.001). Age was not associated with zOE adjusting for sex, education, and MMSE. Similarly adjusted linear regression models revealed that lower EF was associated with a larger oblique effect (p < 0.001). Modular analyses of neural connectivity revealed a differential patterns of network affiliation that varied by high versus low group status determined via median split of zOblique and zHV errors, separately. Older adults exhibit the oblique effect and it is associated with specific cognitive processes and regional brain networks that may facilitate future investigations of visuospatial preference in aging.

NeuroCave: A web-based immersive visualization platform for exploring connectome datasets

We introduce NeuroCave, a novel immersive visualization system that facilitates the visual inspection of structural and functional connectome datasets. The representation of the human connectome as a graph enables neuroscientists to apply network-theoretic approaches in order to explore its complex characteristics. With NeuroCave, brain researchers can interact with the connectome-either in a standard desktop environment or while wearing portable virtual reality headsets (such as Oculus Rift, Samsung Gear, or Google Daydream VR platforms)-in any coordinate system or topological space, as well as cluster brain regions into different modules on-demand. Furthermore, a default side-by-side layout enables simultaneous, synchronized manipulation in 3D, utilizing modern GPU hardware architecture, and facilitates comparison tasks across different subjects or diagnostic groups or longitudinally within the same subject. Visual clutter is mitigated using a state-of-the-art edge bundling technique and through an interactive layout strategy, while modular structure is optimally positioned in 3D exploiting mathematical properties of platonic solids. NeuroCave provides new functionality to support a range of analysis tasks not available in other visualization software platforms.

Baseline connectome modular abnormalities in the childhood phase of a longitudinal study on individuals with chromosome 22q11.2 deletion syndrome

Occurring in at least 1 in 3,000 live births, chromosome 22q11.2 deletion syndrome (22q11DS) produces a complex phenotype that includes a constellation of medical complications such as congenital cardiac defects, immune deficiency, velopharyngeal dysfunction, and characteristic facial dysmorphic features. There is also an increased incidence of psychiatric diagnosis, especially intellectual disability and ADHD in childhood, lifelong anxiety, and a strikingly high rate of schizophrenia spectrum disorders, which occur in around 30% of adults with 22q11DS. Using innovative computational connectomics, we studied how 22q11DS affects high-level network signatures of hierarchical modularity and its intrinsic geometry in 55 children with confirmed 22q11DS and 27 Typically Developing (TD) children. Results identified 3 subgroups within our 22q11DS sample using a K-means clustering approach based on several midline structural measures-of-interests. Each subgroup exhibited distinct patterns of connectome abnormalities. Subtype 1, containing individuals with generally healthy-looking brains, exhibited no significant differences in either modularity or intrinsic geometry when compared with TD. By contrast, the more anomalous 22q11DS Subtypes 2 and 3 brains revealed significant modular differences in the right hemisphere, while Subtype 3 (the most anomalous anatomy) further exhibited significantly abnormal connectome intrinsic geometry in the form of left-right temporal disintegration. Taken together, our findings supported an overall picture of (a) anterior-posteriorly differential interlobar frontotemporal/frontoparietal dysconnectivity in Subtypes 2 and 3 and (b) differential intralobar dysconnectivity in Subtype 3. Our ongoing studies are focusing on whether these subtypes and their connnectome signatures might be valid biomarkers for predicting the degree of psychosis-proneness risk found in 22q11DS. Hum Brain Mapp 39:232-248, 2018. © 2017 Wiley Periodicals, Inc.

Hierarchical dynamics of informational patterns and decision-making

Traditional studies on the interaction of cognitive functions in healthy and disordered brains have used the analyses of the connectivity of several specialized brain networks-the functional connectome. However, emerging evidence suggests that both brain networks and functional spontaneous brain-wide network communication are intrinsically dynamic. In the light of studies investigating the cooperation between different cognitive functions, we consider here the dynamics of hierarchical networks in cognitive space. We show, using an example of behavioural decision-making based on sequential episodic memory, how the description of metastable pattern dynamics underlying basic cognitive processes helps to understand and predict complex processes like sequential episodic memory recall and competition among decision strategies. The mathematical images of the discussed phenomena in the phase space of the corresponding cognitive model are hierarchical heteroclinic networks. One of the most important features of such networks is the robustness of their dynamics. Different kinds of instabilities of these dynamics can be related to 'dynamical signatures' of creativity and different psychiatric disorders. The suggested approach can also be useful for the understanding of the dynamical processes that are the basis of consciousness.

The significance of negative correlations in brain connectivity

Understanding the modularity of functional magnetic resonance imaging (fMRI)-derived brain networks or "connectomes" can inform the study of brain function organization. However, fMRI connectomes additionally involve negative edges, which may not be optimally accounted for by existing approaches to modularity that variably threshold, binarize, or arbitrarily weight these connections. Consequently, many existing Q maximization-based modularity algorithms yield variable modular structures. Here, we present an alternative complementary approach that exploits how frequent the blood-oxygen-level-dependent (BOLD) signal correlation between two nodes is negative. We validated this novel probability-based modularity approach on two independent publicly-available resting-state connectome data sets (the Human Connectome Project [HCP] and the 1,000 functional connectomes) and demonstrated that negative correlations alone are sufficient in understanding resting-state modularity. In fact, this approach (a) permits a dual formulation, leading to equivalent solutions regardless of whether one considers positive or negative edges; (b) is theoretically linked to the Ising model defined on the connectome, thus yielding modularity result that maximizes data likelihood. Additionally, we were able to detect novel and consistent sex differences in modularity in both data sets. As data sets like HCP become widely available for analysis by the neuroscience community at large, alternative and perhaps more advantageous computational tools to understand the neurobiological information of negative edges in fMRI connectomes are increasingly important.

Scale-integrated Network Hubs of the White Matter Structural Network

The 'human connectome' concept has been proposed to significantly increase our understanding of how functional brain states emerge from their underlying structural substrates. Especially, the network hub has been considered one of the most important topological properties to interpret a network as a complex system. However, previous structural brain connectome studies have reported network hub regions based on various nodal resolutions. We hypothesized that brain network hubs should be determined considering various nodal scales in a certain range. We tested our hypothesis using the hub strength determined by the mean of the "hubness" values over a range of nodal scales. Some regions of the precuneus, superior occipital gyrus, and superior parietal gyrus in a bilaterally symmetric fashion had a relatively higher level of hub strength than other regions. These regions had a tendency of increasing contributions to local efficiency than other regions. We proposed a methodological framework to detect network hubs considering various nodal scales in a certain range. This framework might provide a benefit in the detection of important brain regions in the network.

Multimodal evaluation of the amygdala's functional connectivity

The amygdala is one of the most extensively studied human brain regions and undisputedly plays a central role in many psychiatric disorders. However, an outstanding question is whether connectivity of amygdala subregions, specifically the centromedial (CM), laterobasal (LB) and superficial (SF) nuclei, are modulated by brain state (i.e., task vs. rest). Here, using a multimodal approach, we directly compared meta-analytic connectivity modeling (MACM) and specific co-activation likelihood estimation (SCALE)-derived estimates of CM, LB and SF task-based co-activation to the functional connectivity of these nuclei as assessed by resting state fmri (rs-fmri). Finally, using a preexisting resting state functional connectivity-derived cortical parcellation, we examined both MACM and rs-fmri amygdala subregion connectivity with 17 large-scale networks, to explicitly address how the amygdala interacts with other large-scale neural networks. Analyses revealed strong differentiation of CM, LB and SF connectivity patterns with other brain regions, both in task-dependent and task-independent contexts. All three regions, however, showed convergent connectivity with the right ventrolateral prefrontal cortex (VLPFC) that was not driven by high base rate levels of activation. Similar patterns of connectivity across rs-fmri and MACM were observed for each subregion, suggesting a similar network architecture of amygdala connectivity with the rest of the brain across tasks and resting state for each subregion, that may be modified in the context of specific task demands. These findings support animal models that posit a parallel model of amygdala functioning, but importantly, also modify this position to suggest integrative processing in the amygdala.

Elucidating neural network functional connectivity abnormalities in bipolar disorder: toward a harmonized methodological approach

Bipolar disorder (BD), a mood disorder characterized by emotional lability and dysregulation, is associated with alterations in functional connectivity, particularly as assessed using functional MRI. Here, we provide an overview of the extant literature, and themes that have emerged within it. We identified published research describing functional connectivity in BD using PubMed and follow-up searches. The most consistent evidence favors abnormally heightened functional connectivity between the amygdala and the lateral regions of the ventral prefrontal cortex (PFC), both during rest or emotional processing. Altered interactions between the amygdala and more medial PFC regions have been implicated in BD, but are less consistently related to core symptoms and are sometimes associated with mood state or psychosis. Interactions between medial and lateral ventral PFC have also been reported to be altered in BD, and may mediate estimates of amygdala/vlPFC connectivity. We also describe other themes, including an emerging literature examining reward circuitry, which has highlighted abnormal functional interactions between the ventral striatum and medial prefrontal cortex, as well as the advent of examining global network abnormalities in BD. Functional connectivity studies in BD have established altered interactions between PFC and the amygdala. To address the inconsistencies in the literature, we suggest avenues for the adoption of large scale, and network-based analysis of connectivity, the integration of structural connectivity and the acknowledgement of dynamic and context-related shifts in functional connectivity as a means of clarifying the abnormal neural circuitry in the disorder.

Cognitive and connectome properties detectable through individual differences in graphomotor organization

We investigated whether graphomotor organization during a digitized Clock Drawing Test (dCDT) would be associated with cognitive and/or brain structural differences detected with a tractography-derived structural connectome of the brain. 72 non-demented/non-depressed adults were categorized based on whether or not they used 'anchor' digits (i.e., 12, 3, 6, 9) before any other digits while completing dCDT instructions to "draw the face of a clock with all the numbers and set the hands to 10 after 11". 'Anchorers' were compared to 'non-anchorers' across dCDT, additional cognitive measures and connectome-based metrics. In the context of grossly intact clock drawings, anchorers required fewer strokes to complete the dCDT and outperformed non-anchorers on executive functioning and learning/memory/recognition tasks. Anchorers had higher local efficiency for the left medial orbitofrontal and transverse temporal cortices as well as the right rostral anterior cingulate and superior frontal gyrus versus non-anchorers suggesting better regional integration within local networks involving these regions; select aspects of which correlated with cognition. Results also revealed that anchorers' exhibited a higher degree of modular integration among heteromodal regions of the ventral visual processing stream versus non-anchorers. Thus, an easily observable graphomotor distinction was associated with 1) better performance in specific cognitive domains, 2) higher local efficiency suggesting better regional integration, and 3) more sophisticated modular integration involving the ventral ('what') visuospatial processing stream. Taken together, these results enhance our knowledge of the brain-behavior relationships underlying unprompted graphomotor organization during dCDT.

The intrinsic geometry of the human brain connectome

This paper describes novel methods for constructing the intrinsic geometry of the human brain connectome using dimensionality-reduction techniques. We posit that the high-dimensional, complex geometry that represents this intrinsic topology can be mathematically embedded into lower dimensions using coupling patterns encoded in the corresponding brain connectivity graphs. We tested both linear and nonlinear dimensionality-reduction techniques using the diffusion-weighted structural connectome data acquired from a sample of healthy subjects. Results supported the nonlinearity of brain connectivity data, as linear reduction techniques such as the multidimensional scaling yielded inferior lower-dimensional embeddings. To further validate our results, we demonstrated that for tractography-derived structural connectome more influential regions such as rich-club members of the brain are more centrally mapped or embedded. Further, abnormal brain connectivity can be visually understood by inspecting the altered geometry of these three-dimensional (3D) embeddings that represent the topology of the human brain, as illustrated using simulated lesion studies of both targeted and random removal. Last, in order to visualize brain's intrinsic topology we have developed software that is compatible with virtual reality technologies, thus allowing researchers to collaboratively and interactively explore and manipulate brain connectome data.