Scientific Research

Approaching autism spectrum disorder as a systems engineering problem

We are embarking on a first-of-its kind study to investigate the potential of cannabidiol (CBD) as a treatment for autism spectrum disorder (ASD). The groundbreaking nature of this new study is its multi—disciplinary approach, and how it uniquely pairs a clinical trial with detailed neurobehavioral observation, as well as basic science studies to determine if CBD holds therapeutic promise, and if so, via what mechanisms. As such, part of the goal of our lab in collaboration with Prof. Alysson Muorti is to understand and test a number of hypotheses about the functional differences associated with how ASD neurons and networks process information and carry out neural computations relative to neurons from typical individuals. Prof Muorti is a colleague in the Departments of Pediatrics and Cellular and Molecular Medicine at the University of California San Diego and is the Director of the UCSD Stem Cell Program. Our work will involve using patient specific derived networks in a dish, by taking advantage of induced pluripotent stem cells (iPSC). By introducing a number of chemical factors, iPSC technologies allow taking a fully differentiated cell from the body (e.g. a fibroblast from a cheek swab), de-differentiating it, and differentiating it again down a different developmental lineage, such as a neuron. Prof. Muorti has pioneered iPSC models of autism-like phenotypes. Starting with these cell models, we will be able to study the computational properties of these neurons and the networks they form derived from ASD patients (and typical controls).

ASD is a highly complex and heterogeneous set of related neurodevelopmental disorders with widely varied clinical characteristics. In fact, the most recent version of the Diagnostic and Statistical Manual of Mental Disorders (DSM-V), diagnosis ASD not by known cellular or pathophysiological processes, but by criteria for social and functional deficits. This in part reflects an admission that we do not understand the causative mechanisms associated with ASD from a systems neuroscience perspective, which would provide insights into how information processing in the brain of ASD patients differs from typical individuals. Despite a significant degree of known genetic heterogeneity responsible for ASD, many of the known mutations seem to involve canonical processes associated with the formation of neurons and how they ‘wire up’ into neural circuits and networks, including neurogenesis, axon guidance, and synapse formation. Presumably, this suggests that cellular and network changes among neurons encoding and processing information are critical to the eventual social, behavioral, and other functional clinical manifestations of ASD. But exactly how and why remains a mystery.

Over the last several decades, considerable progress has been made in understanding the endocannabinoid system (ECS). ECS signaling pathways are physiologically ubiquitous, and can have wide ranging effects on a number of disorders, including in the brain and central nervous system. In particular, there is data showing that the ECS system affects a number of important processes that likely contribute to the clinical aspects of ASD, including social reward responsivity, neural development, circadian rhythms, and neural circuits associated with anxiety. As such, increasing our understanding of how the ECS system interacts with the brain and neural signaling offers possible opportunities to exploit new pharmacological and therapeutic targets.

The analysis we will pursue is based on new theory and the construction of a mathematical framework from our lab that models the competing dynamics of incident signals into nodes along directed edges in a network. The framework models how the timing of different signals compete to ‘activate’ nodes they connect into. We have shown previously how the interplay between temporal latencies of propagating discrete signaling events on the network relative to the internal dynamics of the individual nodes can have profound effects on the dynamics of the network. In geometric networks temporal latencies are due to the relationship between signaling speeds (conduction velocities) and the geometry or shape of the edges (links or connections) on the network. The theory also takes into account that each node has associated with it a refractory period or refractory state. The refractory period is a value that reflects the internal state of the node in response to its activation by an upstream winning node (or set of nodes). It is a period of time during which the node is not capable of responding to other input signals from nodes that connect into it. In other words, it is refractory to such inputs. The theory in part showed how in order for such a geometric network to be able to signal and process information efficiently, a balance is necessitated between the internal dynamics of the participating nodes that make up the network, through the refractory period, and the dynamics of signaling or information flow on the network between connected nodes. Mathematically we can compute this balance as a ratio. In fact, we have recently shown that at least one specific class of neurons in the brain (Basket cell interneurons in the cerebral cortex) seem to be structurally (morphologically) designed to meet the theoretically predicted ideal ratio needed for optimally efficient signaling. And it is precisely this ratio that we will attempt to measure in ASD patient specific derived neurons and networks, in order to see if it is shifted from the ratio associated with typical neurons. If it is, it suggests a well-understood (i.e. mathematical) basis for why information processing in ASD neurons is different. And it also increases our understanding on how the ECS system might play a therapeutically role as we test if cannabinoids are able to reverse this effect. Wherever the findings may lead us, they will clearly serve as very powerful insights towards understanding the pathophysiology of ASD from a systems engineering perspective, offering a brand new way of thinking about what ASD is and how the ECS system could be used address it.

Dr. Gabriel Silva

Author Dr. Gabriel Silva

Professor of Bioengineering and Neurosciences, Vice Chair of Department of Bioengineering, UC San Diego Jacob School of Engineering

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