Browsing by Author "Dzirasa, Kafui"
Results Per Page
Sort Options
Item Embargo A Data-Driven Approach to Uncovering the Neural Dynamics of Anxiety(2022) Hughes, DaltonAnxiety is a behavioral state induced by low-threat, uncertain situations in which perceived danger is diffuse. The anxiety state is then accompanied by increased vigilance and risk assessment to one’s surroundings. Recent studies have shown that the brain regions responsible for encoding anxiety are widely located in the frontal cortex and extended limbic system; however, the network architecture responsible for hypervigilance has yet to be elucidated. Here, I propose to employ a data-driven method of using in vivo recordings of electrical activity across multiple brain regions concurrently as mice freely explore classic ethological anxiety-related behavioral assays and are administered pharmacological agents that modulate the anxiety state. Using novel machine-learning techniques, I have generated neural models that reflect the network-level activity engaged during the performance of these tasks. I have then validated the structure of this anxiety network in its ability to generalize to other anxiety-related tasks and models of disease. I anticipate that this strategy will discover an independent network that is correlated with anxiety-related behaviors. Thus, successful completion of the proposed work will lead to a network-level understanding of anxiety. Furthermore, the framework discovered through this study has the potential to facilitate the development of new revolutionary approaches for anxiety disorders.
Item Open Access Altered mGluR5-Homer scaffolds and corticostriatal connectivity in a Shank3 complete knockout model of autism.(Nat Commun, 2016-05-10) Wang, Xiaoming; Bey, Alexandra L; Katz, Brittany M; Badea, Alexandra; Kim, Namsoo; David, Lisa K; Duffney, Lara J; Kumar, Sunil; Mague, Stephen D; Hulbert, Samuel W; Dutta, Nisha; Hayrapetyan, Volodya; Yu, Chunxiu; Gaidis, Erin; Zhao, Shengli; Ding, Jin-Dong; Xu, Qiong; Chung, Leeyup; Rodriguiz, Ramona M; Wang, Fan; Weinberg, Richard J; Wetsel, William C; Dzirasa, Kafui; Yin, Henry; Jiang, Yong-HuiHuman neuroimaging studies suggest that aberrant neural connectivity underlies behavioural deficits in autism spectrum disorders (ASDs), but the molecular and neural circuit mechanisms underlying ASDs remain elusive. Here, we describe a complete knockout mouse model of the autism-associated Shank3 gene, with a deletion of exons 4-22 (Δe4-22). Both mGluR5-Homer scaffolds and mGluR5-mediated signalling are selectively altered in striatal neurons. These changes are associated with perturbed function at striatal synapses, abnormal brain morphology, aberrant structural connectivity and ASD-like behaviour. In vivo recording reveals that the cortico-striatal-thalamic circuit is tonically hyperactive in mutants, but becomes hypoactive during social behaviour. Manipulation of mGluR5 activity attenuates excessive grooming and instrumental learning differentially, and rescues impaired striatal synaptic plasticity in Δe4-22(-/-) mice. These findings show that deficiency of Shank3 can impair mGluR5-Homer scaffolding, resulting in cortico-striatal circuit abnormalities that underlie deficits in learning and ASD-like behaviours. These data suggest causal links between genetic, molecular, and circuit mechanisms underlying the pathophysiology of ASDs.Item Open Access Characterizing antipsychotic behavioral and corticostriatal neurophysiological effects to psychotomimetic challenge(2022) Thomas, Gwenaëlle E.Schizophrenia is marked by significant disruptions to dopaminergic signaling across the mesolimbic and mesocortical circuits. Antipsychotic drugs have been largely unsuccessfully treating cognitive symptoms that debilitate the schizophrenia patient population. Dopamine 2 Receptor (D2R)- βeta arrestin 2 (βarr2) biased signaling, independent of the canonical G protein signaling, has emerged as a potential mechanism for antipsychotic drugs to restore dopaminergic signaling and improve treatment resistant cognitive symptoms. In the following experiments, I described gene editing tools to systematically investigate D2R signaling in a region or cell specific manner. Next, I evaluated the behavioral effects of two functionally selective D2-like βarr2 biased ligands against psychotomimetic challenge from phencyclidine or amphetamine. Then I employed chemogenetics to perform synthetic pharmacology experiments e.g. studying the signaling cascade of a drug without using the drug, to discover how D2- R βarr2 signaling produces antipsychotic effects in the prefrontal cortex. Lastly, I characterized the neurophysiological changes induced by phencyclidine and a D2R βarr2 biased ligand within relevant brain regions in the meso -limbic and -cortical circuits. Our results determined antipsychotic like activity is 1) regulated by excitation-inhibitory balance maintained by cortical GABA interneurons 2) dependent on βarr2.
Item Embargo Dissecting Network-Level Consequences of Anhedonia in Reward Approach(2023) Adamson, EliseIdentification and pursuit of available rewards is critical for survival, driving associative learning and modulating adaptive behaviors. A feature of reward anticipation is the ability to track and encode progression toward a reward and modulate goal-directed actions accordingly. While many studies have identified the contribution of individual brain regions to goal-directed behavior, identifying the mechanism by which the brain integrates and organizes this information has not been investigated brain-wide. We used multi-site electrophysiology recordings in mice and advanced computational techniques to identify coordinated activity predictive of progression toward a goal in both space and time. The Goal Progress Network (GPN) exhibits activity correlated with neuronal firing and generalizes across several reward contexts. This network demonstrates immediate utility to assess the effect of chronic stress on goal progress and the alterations in neural activity that may underlie anhedonia, a key symptom of major depressive disorder.
Item Open Access Electrophysiology of Gαz protein as a mediator for seizure susceptibility(2016-05-06) Boms, OkechiSeizures are marked by a state of irregular, recurrent neuronal activity in the brain. Seizures are typical across a wide range of disorders including epilepsy, autism, and they are high comorbidity with anxiety disorders. In the mouse model, increased levels of brain-derived neurotrophic factor (BDNF) have been linked to increased seizure susceptibility. Gαz, a member of the G-protein family, is important for the negative regulation of BDNF; Gαz-null show more BDNF-regulated axon growth. We postulated that since Gαz-null mice have increased levels of BDNF, Gαz might play a role in mediating seizure susceptibility. A previous study from our lab showed that Gαz -null mice were in fact more susceptible to seizures than wildtype (WT) mice. This study was conducted to characterize neuronal seizure activity and progression across different brain regions for this genetic model. Electrodes were implanted into the brains of WT and Gαz -null mice to record the local field potential (LFPs), proxy for relative activity, during induced seizure by the pilocarpine (180mg/kg) drug. LFP data was recorded simultaneously from 6 brain regions: amygdala, dorsal hippocampus, motor cortex, somatosensory cortex, ventral hippocampus, and thalamus. The Gαz -null mice had more severe seizure behavior and more robust electrographic activity in comparison to the WT group. The site of seizure onset and progression for the WT group closely matches the pattern from other studies, while the Gαz -null mice showed a novel pattern. The behavioral and electrographic results confirm the role of Gαz in mediating seizure severity and susceptibility; further studies will be needed to confirm the seizure progression pattern noted for the WT and Gαz-null groups.Item Open Access Functional Brain Networks Underlying Anticipation in Motivated Behavior(2018) Vu, Mai-Anh ThiAnticipation is a state of expectancy for something that will happen, and it allows us to use past learning to prepare for and make predictions about the future. Studies have shown that anticipation influences behavioral performance, learning, and memory, and studies implicate reward-related brain circuitry. However, few studies have investigated the neural underpinnings of anticipation on a brain-wide network scale . In this set of experiments, I take an interdisciplinary cross-species approach, using in-vivo electrophysiology in mice and functional magnetic resonance imaging (fMRI) in humans, to investigate brain networks underlying anticipation in motivated behavior. Using a data-driven machine learning approach, I characterize the anticipatory network in mice running through a T-maze, and show how it is affected by behavioral perturbation in the form of a task reversal, and circuit perturbation in the form of a genetic mutant mouse line. I also validate this network in a separate cohort of mice in a variation of the T-maze task that varies in difficulty, and show how activity in this network is modulated by task difficulty and intermediate instrumental goals. Finally, I investigate this network using fMRI in human subjects performing a trivia-based task to show how this network links curiosity, a more intrinsic form of motivation, to memory. The findings from these studies provide evidence at multiple levels and across multiple species for an anticipatory network that links motivational state to cognitive performance.
Item Open Access Interpretable Factor Models of Latent Brain Networks Associated with Stress and Depression(2021) Gallagher, NeilA major component of most psychiatric disorders is that they affect the internal mental state of the individual.Modern psychiatric research primarily depends on self-report to measure internal state in humans and on behavior to measure internal state in animals. Both of these can be unreliable measures of internal mental state. In order to facilitate psychiatric research, we need models of mental state that are based on neural activity. Such models have proven challenging to design because they must be able to distill the complexity of neural activity distributed across many brain regions. This dissertation describes models that solve this problem, by representing such neural activity as a sum of contributions from many distinct sub-networks in the brain. We call these sub-network electrical functional connectome (electome) networks. Here, I show that electome networks can be used to distinguish between mental states in mice. One of these electome networks distinguishes mice that show depressive symptoms from those that do not, and can be used as a measure depressive phenotype. Electome networks represent a new class of tools that give brain researchers a new way to measure internal mental state and relate it back to brain activity.
Item Open Access Localization of Metal Electrodes in the Intact Rat Brain Using Registration of 3D Microcomputed Tomography Images to a Magnetic Resonance Histology Atlas.(eNeuro, 2015-07) Borg, Jana Schaich; Vu, Mai-Anh; Badea, Cristian; Badea, Alexandra; Johnson, G Allan; Dzirasa, KafuiSimultaneous neural recordings taken from multiple areas of the rodent brain are garnering growing interest due to the insight they can provide about spatially distributed neural circuitry. The promise of such recordings has inspired great progress in methods for surgically implanting large numbers of metal electrodes into intact rodent brains. However, methods for localizing the precise location of these electrodes have remained severely lacking. Traditional histological techniques that require slicing and staining of physical brain tissue are cumbersome, and become increasingly impractical as the number of implanted electrodes increases. Here we solve these problems by describing a method that registers 3-D computerized tomography (CT) images of intact rat brains implanted with metal electrode bundles to a Magnetic Resonance Imaging Histology (MRH) Atlas. Our method allows accurate visualization of each electrode bundle's trajectory and location without removing the electrodes from the brain or surgically implanting external markers. In addition, unlike physical brain slices, once the 3D images of the electrode bundles and the MRH atlas are registered, it is possible to verify electrode placements from many angles by "re-slicing" the images along different planes of view. Further, our method can be fully automated and easily scaled to applications with large numbers of specimens. Our digital imaging approach to efficiently localizing metal electrodes offers a substantial addition to currently available methods, which, in turn, may help accelerate the rate at which insights are gleaned from rodent network neuroscience.Item Open Access Rat intersubjective decisions are encoded by frequency-specific oscillatory contexts.(Brain Behav, 2017-06) Schaich Borg, Jana; Srivastava, Sanvesh; Lin, Lizhen; Heffner, Joseph; Dunson, David; Dzirasa, Kafui; de Lecea, LuisINTRODUCTION: It is unknown how the brain coordinates decisions to withstand personal costs in order to prevent other individuals' distress. Here we test whether local field potential (LFP) oscillations between brain regions create "neural contexts" that select specific brain functions and encode the outcomes of these types of intersubjective decisions. METHODS: Rats participated in an "Intersubjective Avoidance Test" (IAT) that tested rats' willingness to enter an innately aversive chamber to prevent another rat from getting shocked. c-Fos immunoreactivity was used to screen for brain regions involved in IAT performance. Multi-site local field potential (LFP) recordings were collected simultaneously and bilaterally from five brain regions implicated in the c-Fos studies while rats made decisions in the IAT. Local field potential recordings were analyzed using an elastic net penalized regression framework. RESULTS: Rats voluntarily entered an innately aversive chamber to prevent another rat from getting shocked, and c-Fos immunoreactivity in brain regions known to be involved in human empathy-including the anterior cingulate, insula, orbital frontal cortex, and amygdala-correlated with the magnitude of "intersubjective avoidance" each rat displayed. Local field potential recordings revealed that optimal accounts of rats' performance in the task require specific frequencies of LFP oscillations between brain regions in addition to specific frequencies of LFP oscillations within brain regions. Alpha and low gamma coherence between spatially distributed brain regions predicts more intersubjective avoidance, while theta and high gamma coherence between a separate subset of brain regions predicts less intersubjective avoidance. Phase relationship analyses indicated that choice-relevant coherence in the alpha range reflects information passed from the amygdala to cortical structures, while coherence in the theta range reflects information passed in the reverse direction. CONCLUSION: These results indicate that the frequency-specific "neural context" surrounding brain regions involved in social cognition encodes outcomes of decisions that affect others, above and beyond signals from any set of brain regions in isolation.Item Open Access Supervised Autoencoders Learn Robust Joint Factor Models of Neural Activity.(CoRR, 2020) Talbot, Austin; Dunson, David; Dzirasa, Kafui; Carlson, DavidFactor models are routinely used for dimensionality reduction in modeling of correlated, high-dimensional data. We are particularly motivated by neuroscience applications collecting high-dimensional `predictors' corresponding to brain activity in different regions along with behavioral outcomes. Joint factor models for the predictors and outcomes are natural, but maximum likelihood estimates of these models can struggle in practice when there is model misspecification. We propose an alternative inference strategy based on supervised autoencoders; rather than placing a probability distribution on the latent factors, we define them as an unknown function of the high-dimensional predictors. This mapping function, along with the loadings, can be optimized to explain variance in brain activity while simultaneously being predictive of behavior. In practice, the mapping function can range in complexity from linear to more complex forms, such as splines or neural networks, with the usual tradeoff between bias and variance. This approach yields distinct solutions from a maximum likelihood inference strategy, as we demonstrate by deriving analytic solutions for a linear Gaussian factor model. Using synthetic data, we show that this function-based approach is robust against multiple types of misspecification. We then apply this technique to a neuroscience application resulting in substantial gains in predicting behavioral tasks from electrophysiological measurements in multiple factor models.