Microglia are the brain macrophages, eliciting multifaceted functions to maintain brain homeostasis across lifetime. To achieve this, microglia are able to sense a plethora of signals in their close environment. In the lab, we investigate the effect of nutrients on microglia function for several reasons: 1) Microglia express all the cellular machinery required to sense nutrients; 2) Eating habits have changed considerably over the last century, towards diets rich in fats and sugars; 3) This so-called "Western diet" is accompanied by an increase in the occurrence of neuropathologies, in which microglia are known to play a role. In my talk, I will present data showing how variations in nutrient intake alter microglia function, including exacerbation of synaptic pruning, with profound consequences for neuronal activity and behavior. I will also show unpublished data on the mechanisms underlying the effects of nutrients on microglia, notably through the regulation of their metabolic activity.

Gestational exposure to environmental toxins, infections, and stressors are epidemiologically linked to neurodevelopmental disorders with strong male-bias, such as autism spectrum disorder. We modeled some of these prenatal risk factors in mice, by co-exposing pregnant dams to an environmental pollutant and limited-resource stress, which robustly dysregulated the maternal immune system. Male but not female offspring displayed long-lasting behavioral abnormalities and alterations in the activity of brain networks encoding social interactions, along with disruptions of gut structure and microbiome composition. Cellularly, prenatal stressors impaired microglial synaptic pruning in males during early postnatal development. Precise inhibition of microglial phagocytosis during the same critical period mimicked the impact of prenatal stressors on the male-specific social deficits. Conversely, modifying the gut microbiome rescued the social and cellular deficits, indicating that environmental stressors alter neural circuit formation in males via impairing microglia function during development, perhaps via a gut-brain disruption.

In this lecture, Dr Stevens will discuss recent work that implicates brain immune cells, called microglia, in sculpting of synaptic connections during development and their relevance to autism, schizophrenia and other brain disorders. Her recent work revealed a key role for microglia and a group of immune related molecules called complement in normal developmental synaptic pruning, a normal process required to establish precise brain wiring. Emerging evidence suggests aberrant regulation of this pruning pathway may contribute to synaptic and cognitive dysfunction in a host of brain disorders, including schizophrenia. Recent research has revealed that a person’s risk of schizophrenia is increased if they inherit specific variants in complement C4, gene plays a well-known role in the immune system but also helps sculpt developing synapses in the mouse visual system (Sekar et al., 2016). Together these findings may help explain known features of schizophrenia, including reduced numbers of synapses in key cortical regions and an adolescent age of onset that corresponds with developmentally timed waves of synaptic pruning in these regions. Stevens will discuss this and ongoing work to understand the mechanisms by which complement and microglia prune specific synapses in the brain. A deeper understanding of how these immune mechanisms mediate synaptic pruning may provide novel insight into how to protect synapses in autism and other brain disorders, including Alzheimer’s and Huntington’s Disease.

Brain and neuronal network development depend on a complex sequence of events, which include neurogenesis, migration, differentiation, synaptogenesis, and synaptic pruning. Perturbations to any of these processes, for example associated with ion channel gene mutations (i.e., channelopathies), can underlie neurodevelopmental disorders such as neonatal and infantile epilepsies, strongly impair psychomotor development and cause persistent deficits in cognition, motor skills, or motor control. The therapeutic options available are very limited, and prophylactic therapies for patients at an increased risk of developing such epilepsies do not exist yet. By using genetic mouse models in which we controlled the activities of Kv7/M or HCN/h-channels during different developmental periods, we obtained offspring with distinct neurological phenotypes that could not simply be reversed by the re-introduction of the affected ion channel in juvenile or adult animals. The results indicate that channelopathy/mutation-specific treatments of neonatal and infantile epilepsies and their comorbidities need to be targeted to specific sensitive periods.

Many networks in the brain are sparsely connected, and the brain eliminates synapses during development and learning. How could the brain decide which synapses to prune? In a recurrent network, determining the importance of a synapse between two neurons is a difficult computational problem, depending on the role that both neurons play and on all possible pathways of information flow between them. Noise is ubiquitous in neural systems, and often considered an irritant to be overcome. In the first part of this talk, I will suggest that noise could play a functional role in synaptic pruning, allowing the brain to probe network structure and determine which synapses are redundant. I will introduce a simple, local, unsupervised plasticity rule that either strengthens or prunes synapses using only synaptic weight and the noise-driven covariance of the neighboring neurons. For a subset of linear and rectified-linear networks, this rule provably preserves the spectrum of the original matrix and hence preserves network dynamics even when the fraction of pruned synapses asymptotically approaches 1. The plasticity rule is biologically-plausible and may suggest a new role for noise in neural computation. Time permitting, I will then turn to the problem of extracting structure from neural population data sets using dimensionality reduction methods. I will argue that nonlinear structures naturally arise in neural data and show how these nonlinearities cause linear methods of dimensionality reduction, such as Principal Components Analysis, to fail dramatically in identifying low-dimensional structure.