Neuroscience seminars

Brain-machine interfaces (BMIs) change how the brain sends and receives information from the environment, opening new ways to probe brain function. For instance, motor BMIs allow us to precisely define and manipulate the sensorimotor loop which has enabled new insights into motor control and learning. In this talk, I’ll first present an example study where sensory-motor loop manipulations in BMI allowed us to probe feed-forward and feedback control mechanisms in ways that are not possible in the natural motor system. This study shed light on sensorimotor processing, and in turn led to state-of-the-art neural interface performance. I’ll then survey recent work that highlights the likelihood that BMIs, much like natural motor learning, engages multiple distributed learning mechanisms that can be carefully interrogated with BMI.

Learning is a population phenomenon. Since it is the organized activity of populations of neurons that cause movement, learning a new skill must involve reshaping those population activity patterns. Seeing how the brain does this has been elusive, but a brain-computer interface approach can yield new insight. We presented monkeys with novel BCI mappings that we knew would be difficult for them to learn how to control. Over several days, we observed the emergence of new patterns of neural activity that endowed the animals with the ability to perform better at the BCI task. We speculate that there also exists a direct relationship between new patterns of neural activity and new abilities during natural movements, but it is much harder to see in that setting.

The brain is remarkably flexible, and appears to instantly reconfigure its processing depending on what’s needed to solve a task at hand: fMRI studies indicate that distal brain areas appear to fluidly couple and decouple with one another depending on behavioral context. We investigated how the brain coordinates its activity across areas to inform complex, top-down control behaviors. Animals were trained to perform a novel brain machine interface task to guide a visual cursor to a reward zone, using activity recorded with widefield calcium imaging. This allowed us to screen for cortical areas implicated in causal neural control of the visual object. Animals could decorrelate normally highly-correlated areas to perform the task, and used an explore-exploit search in neural activity space to discover successful strategies. Higher visual and parietal areas were more active during the task in expert animals. Single unit recordings targeted to these areas indicated that the sensory representation of an object was sensitive to an animal’s subjective sense of controlling it.

By studying the neural correlates of hand augmentation, we are exploring the boundaries of neuroplasticity seeing how it can be harnessed to improve the usability and control of prosthetic devices. Tamar Makin and Dani Clode each discuss their research and perspectives within the field of prosthetics that has led to this unique collaboration and exploration of motor augmentation and the brain.

Brain-computer interfaces (BCIs) have afforded paralysed users “mental control” of computer cursors and robots, and even of electrical stimulators that reanimate their own limbs. Most existing BCIs map the activity of hundreds of motor cortical neurons recorded with implanted electrodes into control signals to drive these devices. Despite these impressive advances, the field is facing a number of challenges that need to be overcome in order for BCIs to become widely used during daily living. In this talk, I will focus on two such challenges: 1) having BCIs that allow performing a broad range of actions; and 2) having BCIs whose performance is robust over long time periods. I will present recent studies from our group in which we apply neuroscientific findings to address both issues. This research is based on an emerging view about how the brain works. Our proposal is that brain function is not based on the independent modulation of the activity of single neurons, but rather on specific population-wide activity patters —which mathematically define a “neural manifold”. I will provide evidence in favour of such a neural manifold view of brain function, and illustrate how advances in systems neuroscience may be critical for the clinical success of BCIs.

Emerging genetic studies of late-onset Alzheimer’s Disease implicate the brain’s resident macrophages in the pathogenesis of AD. More than half the risk genes associated with late-onset AD are selectively expressed in microglia and peripheral myeloid cells; yet we know little about the underlying biology or how myeloid cells contribute to AD pathogenesis. Using single-cell RNA sequencing and spatial transcriptomics we identified molecular signatures that can be used to localize and monitor distinct microglia functional states in the human and mouse brain. Our results show that microglia assume diverse functional states in development, aging and injury, including populations corresponding to known microglial functions including proliferation, migration, inflammation, and synaptic phagocytosis. We identified several innate immune pathways by which microglia recognize and prune synapses during development and in models of Alzheimer’s disease, including the classical complement cascade. Illuminating the mechanisms by which developing synaptic circuits are sculpted is providing important insight on understanding how to protect synapses in Alzheimer’s and other neurodegenerative diseases of synaptic dysfunction.

Behavior varies even among genetically identical animals raised in the same environment. However, little is known about the circuit or anatomical underpinnings of this individuality, though previous work implicates sensory periphery. Drosophila olfaction presents an ideal model to study the biological basis of behavioral individuality, because while the neural circuit underlying olfactory behavior is well-described and highly stereotyped, persistent idiosyncrasy in behavior, neural coding, and neural wiring have also been described. Projection neurons (PNs), which relay odor signals sensed by olfactory receptor neurons (ORNs) to deeper brain structures, exhibit variable calcium responses to identical odor stimuli across individuals, but how these idiosyncrasies relate to individual behavioral responses remains unknown. Here, using paired behavior and two-photon imaging measurements, we show that idiosyncratic calcium dynamics in both ORNs and PNs predict individual preferences for an aversive monomolecular odorant versus air, suggesting that variation at the periphery of the olfactory system determines individual preference for an odor’s presence. In contrast, PN, but not ORN, calcium responses predict individual preferences in a two-odor choice assay. Furthermore, paired behavior and immunohistochemistry measurements reveal that variation in ORN presynaptic density also predicts two-odor preference, suggesting this site is a locus of individuality where microscale circuit variation gives rise to idiosyncrasy in behavior. Our results demonstrate how a neural circuit may vary functionally and structurally to produce variable behavior among individuals.

Glioblastoma cells trigger pharmacoresistant seizures that may promote tumor growth and diminish the quality of remaining life. To define the relationship between growth of glial tumors and their neuronal microenvironment, and to identify genomic biomarkers and mechanisms that may point to better prognosis and treatment of drug resistant epilepsy in brain cancer, we are analyzing a new generation of genetically defined CRISPR/in utero electroporation inborn glioblastoma (GBM) tumor models engineered in mice. The molecular pathophysiology of glioblastoma cells and surrounding neurons and untransformed astrocytes are compared at serial stages of tumor development. Initial studies reveal that epileptiform EEG spiking is a very early and reliable preclinical signature of GBM expansion in these mice, followed by rapidly progressive seizures and death within weeks. FACS-sorted transcriptomic analysis of cortical astrocytes reveals the expansion of a subgroup enriched in pro-synaptogenic genes that may drive hyperexcitability, a novel mechanism of epileptogenesis. Using a prototypical GBM IUE model, we systematically define and correlate the earliest appearance of cortical hyperexcitability with progressive cortical tumor cell invasion, including spontaneous episodes of spreading cortical depolarization, innate inflammation, and xCT upregulation in the peritumoral microenvironment. Blocking this glutamate exporter reduces seizure load. We show that the host genome contributes to seizure risk by generating tumors in a monogenic deletion strain (MapT/tau -/-) that raises cortical seizure threshold. We also show that the tumor variant profile determines epilepsy risk. Our genetic dissection approach sets the stage to broadly explore the developmental biology of personalized tumor/host interactions in mice engineered with novel human tumor mutations in specified glial cell lineages.

Parkinson’s disease is affects millions of people around the world. The disease is characterized by typical movement defects that are caused by the loss of dopaminergic neurons, but several very debilitating non-motor symptoms occur more than 10 years before the motor symptoms. I will discuss how we study these non-motor symptoms including sleep disturbances and olfactory defects using large collections of knock in fruit flies that model the numerous familial forms of Parkinson’s disease as well as using human iPS cells from patients. A common emerging theme are defects in protein homeostasis that in specific neuronal cell types, cause cellular defects that explain the Parkinson-relevant phenotypes. Our work reveals the mechanisms that cause early defects in Parkinson’s disease and it opens therapeutic avenues to start tackling this disease.

Hydra is a fascinating model organism for neuroscience. It is transparent; new genetic lines allow one to image activity in both neurons (Dupre and Yuste, 2017) and muscle cells (Szymanski and Yuste, 2019) ; it exhibits rich behavior, and it continually rebuilds itself. Hydra’s fairly simply physical structure as a two-layered fluid-filled hydrostat and the accessibility of information about neural and muscle activity opens the possibility of a complete model of neural control of behavior. This requires understanding the transformations that occur in the muscle cell layers and a biomechanical model of the body column. We show that we can use this modeling to reverse engineer how neural activity drives behavior.

The hippocampus is a well-studied structure, important for spatial navigation, learning, and memory. The hippocampus, however, still contains secrets and does not work in a vacuum. LINCs are a novel form of long-range inhibitory neuron in the hippocampus, which may be important for coordinating activity between the hippocampus and downstream structures. The cerebellum, while classically viewed as a motor structure, is being increasingly recognized for its impact on cognitive domains. Recent work has demonstrated that the cerebellum can influence the hippocampus, including place cells.

Immune cells and their derived molecules have major impact on brain function. Mice deficient in adaptive immunity have impaired cognitive and social function compared to that of wild-type mice. Importantly, replenishment of the T cell compartment in immune deficient mice restored proper brain function. Despite the robust influence on brain function, T cells are not found within the brain parenchyma, a fact that only adds more mystery into these enigmatic interactions between T cells and the brain. Our results suggest that meningeal space, surrounding the brain, is the site where CNS-associated immune activity takes place. We have recently discovered a presence of meningeal lymphatic vessels that drain CNS molecules and immune cells to the deep cervical lymph nodes. This communication between the CNS and the peripheral immunity is playing a key role in neurophysiology and in several CNS disorders. Interestingly, meningeal lymphatics are impaired in aging and their dysfunction may be related to age-related cognitive decline as well as to Alzheimer’s pathology. In addition to providing new insights into age-related disorders, meningeal lymphatics may also serve as a novel therapeutic target for these diseases and are worth of in-depth mechanistic exploration.

Neuroscience is witnessing extraordinary progress in experimental techniques, especially at the neural circuit level. These advances are largely aimed at enabling us to understand precisely how neural circuit computations mechanistically cause behavior. Establishing this type of causal understanding will require multiple perturbational (e.g optogenetic) experiments. It has been unclear exactly how many such experiments are needed and how this number scales with the size of the nervous system in question. Here, using techniques from Theoretical Computer Science, we prove that establishing the most extensive notions of understanding need exponentially-many experiments in the number of neurons, in many cases, unless a widely-posited hypothesis about computation is false (i.e. unless P = NP). Furthermore, using data and estimates, we demonstrate that the feasible experimental regime is typically one where the number of experiments performable scales sub-linearly in the number of neurons in the nervous system. This remarkable gulf between the worst-case and the feasible suggests an algorithmic barrier to such an understanding. Determining which notions of understanding are algorithmically tractable to establish in what contexts, thus, becomes an important new direction for investigation. TL; DR: Non-existence of tractable algorithms for neural circuit interrogation could pose a barrier to comprehensively understanding how neural circuits cause behavior. Preprint: https://biorxiv.org/content/10.1101/639724v1/…