Functional Brain Networks and Brain Influencers

Tumor affected brains vs. healthy control brains – funded by NIH-NIBIB R01 EB02272001

We wish to present a functional connectivity analysis of tasked-based fMRI data of healthy subjects who perform a specific language task designed for clinical applications. The motivation for this study is to use the resulting functional connectivity of these healthy individuals as a benchmark for clinical studies.

Tasked-based fMRI is a powerful imaging technique used to localize brain areas involved in specific functions. This technique, yet, is task-specific. This means that, even within the realm of language tasks, the fMRI activation is subject to variability given by the specific task under use.

For clinical purposes, medical doctors use a class of specific tasks to facilitate the robust activation of important language area for patients with brain impairment. Yet, brain pathologies (e.g. brain tumors, strokes, epilepsy) affect functional connectivity by disrupting functional links and suppressing the activation of brain areas. Thus, although clinical tasks are designed to guarantee robust activation, the functional connectivity of patients with brain pathologies is ultimately damaged by brain impairments.

To better quantify what is the damage produced by the brain pathology on the functional connectivity it is paramount to have, as a benchmark, functional networks of healthy individuals who perform a task for clinical cases.

We analyze 20 healthy individuals, all right-handed, and from our study we unveil a persistent functional architecture, beyond the inter-subject variability, which wires together Broca’s area, Wenicke’s area, the PreMotor area, and the pre-Supplementary Motor Area.

Furthermore, we investigate the functional connections of the subdivisions of the Broca’s area (opercularis and triangularis Broca’s, Brodmann area 44 and 45, respectively) and we discuss our result within the context of other studies on structural connectivity of these areas. Overall, our findings identify a group of functional regions of interest linked together in a functional circuitry that have a decisive role for the language task used in clinical applications.

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Language networks: bilingual vs. monolingual – funded by NIH U54CA137788, U54CA132378

We wish to present a functional connectivity analysis of tasked-based fMRI data of healthy monolingual and bilingual subjects who perform a specific language task designed for clinical applications. Our motivation was to determine if the results from clinically employed tasks (the ones which the surgeon would actually see in clinical practice) would differ between bilinguals and monolinguals. Secondly, we sought to study the network architecture of the functional networks in each group (monolinguals, bilinguals speaking Spanish, and bilinguals speaking English) and characterize any differences arising from network centrality measurements.

Particularly, we sought to assess the k core, which is emerging as an important topological measure of networks since it reveals a robust and highly connected sub-network, called the k max core; k core has previously been employed to measure the stability of the most resilient functional structures in the brain and may provide useful insights in addition to the functional connectivity map.

We analyzed fMRI scans from 16 healthy subjects: eight bilinguals and eight monolinguals. For every bilingual subject, we conducted two scans, where the language task was conducted in Spanish (L1) and in English (L2). From our study we unveil a persistent functional architecture, the “common network”, beyond inter-subject variability, which wires together Broca’s area, Wenicke’s area (WA), the PreMotor area, and the pre-Supplementary Motor Area. This structure displays differential connectivity of Wernicke’s area between groups. The k core centrality measure shows several areas belong to the maximum k core while WA and other fROIs’ shell occupancy varies across groups. We also found higher common link weights in the L2 group compared to L1 and monolingual groups.

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Inflencers: funded by NSF-IIS 1515022

Background Theory

The theory of optimal percolation is currently being applied to understand the structure of the brain following our recent studies in Morone, Makse, Nature (Aug 2015). We have developed the analytical solution to the problem of identifying of the most influential nodes (essential nodes) in complex networks [3]; a long-standing problem in data science [8]. This problem belongs to the class of non-deterministic polynomial-time hard problems (NP-hard), and therefore it is analogous to the hardest problems in computational science and spin glasses.


Fig. 1: In brain networks, some nodes are more important than others. Morone and Makse, Nature 2015 [3]. The most important nodes are those whose elimination induces the network’s collapse, and identifying them is crucial in many circumstances, for example, to understand critical areas in the brain. But this is a hard task, and most methods available for the purpose are essentially based on trial-and-error. Morone and I have devised a rigorous method to determine the most influential nodes in net-works. We find that low-degree nodes surrounded by hubs as shown here play a much more important role in the network than previously thought.

Our group has developed a large-scale search engine for localizing the most influential nodes in networks: the Collective Influence (CI) theory. CI combines optimality, scalability and versatility, making it applicable to a large class of problems, e.g., from finding influential areas in the brain, to containing epidemic outbreaks through targeted immunizations, maximizing the spreading of information in social media, or quantifying systemic risk in banking systems. We show that tools of combinatorial optimization, spectral theory, spin glasses and machine learning can be used to find the essential nodes in the network. The theory provides a ranking of nodes whose inhibition produces the largest disrupting cascading effect extending to a large portion of the brain. Applying the theory of collective influence in the brain is the focus of our current and future research program as discussed next.

Theory of Collective Influence in the Brain

Accumulating evidence suggests that alterations in the functional brain network may lie behind a number of neurological and neuropsychiatric conditions. Existing interventional techniques (e.g. deep brain stimulation, transcranial magnetic stimulation) are currently being applied without taking into account the alterations in network dynamics thought to be at the root of neurological maladies. Our main hypothesis is that a therapeutic window exist to control network dynamics with targeted interventions to essential node’s activity rigorously predicted by our network theory of Collective Influence in the brain. The rationale is that the activation/deactivation patterns applied to the essential influential nodes predicted by our graph optimization theory will propagate through the central nervous system to impact and modulate brain network dynamics. Our theoretical advances allows researchers and treating clinicians to accurately identify new targets for focal therapy and to move away from “trial and error” therapeutic approach often currently employed. Our theories will allow neuroradiologists, neurosurgeons and the broad neuroscience community to identify and analyze the most influential parts of the brain in various disease states. The tools developed by us will aid in the understanding, diagnosis, and therapy of brain disorders thought to be due to disruptions of brain connectivity (e.g. brain tumors, Alzheimer’s disease, ADHD, strokes or traumatic brain injury). To this end, we are following two complementary research programs in rodents and humans that are providing the scaffold to test our hypotheses.

A. Manipulating essential nodes for integration in a in-vivo animal model of learning and memory guided by network optimization theory (NSF-CRCNS)

Our goal is to identify and manipulate nodes in the brain that are essential for global integration of information. This is crucial for a fundamental understanding of how the brain works as well as to treat neurological and psychiatric conditions. Evidence suggests that a few essential nodes in the brain control a number of crucial functions, such as memory formation, conscious perception and attention, as well as critically participating in brain dysfunctions. However, finding and manipulating essential nodes to control brain network dynamics has not been possible due to the lack of a theoretical framework that could guide prospective interventions.

Selection of targets and stimulation protocols in most studies are based on a questionable trial-and-error approach. What is fundamentally lacking is a network theory to guide these interventions. Advanced network theory applied to the brain will allow researchers and clinicians to prescribe targeted interventions to critical brain nodes.

Our Collective Influence network optimization theory [3, 2] allows to accurately predict the effect of targeted inactivation of essential nodes in the network. Specifically, we use optimal percolation theory to identify the most influential nodes in a system-wide brain network formed in an in-vivo animal model of learning and memory (Fig. 2). Our network optimization theory predicts that the nucleus accumbens (NAc), a well-known structure in the meso-cortico-limbic system, is the most influential node for the interaction between the hippocampus (HC) and the prefrontal cortex (PFC) and the formation of an integrated memory network. This prediction is confirmed by the inhibition of a single core node in the NAc, using a targeted pharmacogenetic intervention, which remarkably, completely eliminates the formation of the memory (Fig. 3) [1].


Fig. 2: Collective Influence theory [3] predicts the es-sential nodes in a memory network induced by LTP in rats. a, Relative size of the giant connected component G of the memory network formed by HC, PFC and NAc under LTP as a function of the fraction of inactivated nodes, q. Shown are two strategies for choosing the essential nodes: Hub-inactivation (triangles) and CI-inactivation (circles). Most of the hubs (red symbols) are located in HC, yet, they are not essential for integra-tion: the hub-inactivation curve shows minimal damage to G upon removal of

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