David Donoho, Postdoctoral Faculty Sponsor
Identifying Neural Signatures of Dopamine Signaling with Machine Learning.
ACS chemical neuroscience
The emergence of new tools to image neurotransmitters, neuromodulators, and neuropeptides has transformed our understanding of the role of neurochemistry in brain development and cognition, yet analysis of this new dimension of neurobiological information remains challenging. Here, we image dopamine modulation in striatal brain tissue slices with near-infrared catecholamine nanosensors (nIRCat) and implement machine learning to determine which features of dopamine modulation are unique to changes in stimulation strength, and to different neuroanatomical regions. We trained a support vector machine and a random forest classifier to decide whether the recordings were made from the dorsolateral striatum (DLS) versus the dorsomedial striatum (DMS) and find that machine learning is able to accurately distinguish dopamine release that occurs in DLS from that occurring in DMS in a manner unachievable with canonical statistical analysis. Furthermore, our analysis determines that dopamine modulatory signals including the number of unique dopamine release sites and peak dopamine released per stimulation event are most predictive of neuroanatomy. This is in light of integrated neuromodulator amount being the conventional metric used to monitor neuromodulation in animal studies. Lastly, our study finds that machine learning discrimination of different stimulation strengths or neuroanatomical regions is only possible in adult animals, suggesting a high degree of variability in dopamine modulatory kinetics during animal development. Our study highlights that machine learning could become a broadly utilized tool to differentiate between neuroanatomical regions or between neurotypical and disease states, with features not detectable by conventional statistical analysis.
View details for DOI 10.1021/acschemneuro.3c00001
View details for PubMedID 37267623
Machine Learning Identifies a Rat Model of Parkinson's Disease via Sleep-Wake Electroencephalogram.
2023; 510: 1-8
Alpha-synuclein induced degeneration of the midbrain substantia nigra pars compact (SNc) dopaminergic neurons causes Parkinson's disease (PD). Rodent studies demonstrate that nigrostriatal dopamine stimulates pallidal neurons which, via the topographical pallidocortical pathway, regulate cortical activity and functions. We hypothesize that nigrostriatal dopamine acting at the basal ganglia regulates cortical activity in sleep and wake state, and its depletion systemically alters electroencephalogram (EEG) across frequencies during sleep-wake state. Compared to control rats, 6-hydroxydopamine induced selective SNc lesions increased overall EEG power (positive synchronization) across 0.5-60 Hz during wake, NREM (non-rapid eye movement) sleep, and REM sleep. Application of machine learning (ML) to seven EEG features computed at a single or combined spectral bands during sleep-wake differentiated SNc lesions from controls at high accuracy. ML algorithms construct a model based on empirical data to make predictions on subsequent data. The accuracy of the predictive results indicate that nigrostriatal dopamine depletion increases global EEG spectral synchronization in wake, NREM sleep, and REM sleep. The EEG changes can be exploited by ML to identify SNc lesions at a high accuracy.
View details for DOI 10.1016/j.neuroscience.2022.11.035
View details for PubMedID 36470477
Quantitative and Correlational Analysis of Brain and Spleen Immune Cellular Responses Following Cerebral Ischemia.
Frontiers in immunology
2021; 12: 617032
Stroke is a multiphasic process, and the initial ischemic phase of neuronal damage is followed by secondary innate and adaptive responses that unfold over days after stroke, offer a longer time frame of intervention, and represent a novel therapeutic target. Therefore, revealing the distinct functions of immune cells in both brain and periphery is important for identification of immunotherapeutic targets for stroke to extend the treatment time window. In this paper an examination of the cellular dynamics of the immune response in the central nervous system (CNS) and periphery provoked by cerebral ischemia is provided. New data is presented for the number of immune cells in brain and spleen of mice during the 7 days following middle cerebral artery occlusion (MCAO). A novel analysis of the correlation among various cell types in the brain and spleen following stroke is presented. It is found that the infiltrated macrophages in the ischemic hemisphere positively correlate with neutrophils which implies their synergic effect in migrating into the brain after stroke onset. It is noted that during infiltration of adaptive immune cells, the number of neutrophils correlate positively with T cells, which suggests neutrophils contribute to T cell infiltration in the stroked brain. Furthermore, the correlation among neurological deficit and various immune cells suggests that microglia and splenic adaptive immune cells (T and B cells) are protective while infiltrating peripheral myeloid cells (macrophage and neutrophils) worsen stroke outcome. Comprehension of such immune responses post cerebral ischemia is crucial for differentiating the drivers of outcomes and also predicting the stroke outcome.
View details for DOI 10.3389/fimmu.2021.617032
View details for PubMedID 34194419
Object Recognition at Higher Regions of the Ventral Visual Stream via Dynamic Inference.
Frontiers in computational neuroscience
2020; 14: 46
The ventral visual stream (VVS) is a fundamental pathway involved in visual object identification and recognition. In this work, we present a hypothesis of a sequence of computations performed by the VVS during object recognition. The operations performed by the inferior temporal (IT) cortex are represented as not being akin to a neural-network, but rather in-line with a dynamic inference instantiation of the untangling notion. The presentation draws upon a technique for dynamic maximum a posteriori probability (MAP) sequence estimation based on the Viterbi algorithm. Simulation results are presented to show that the decoding portion of the architecture that is associated with the IT can effectively untangle object identity when presented with synthetic data. More importantly, we take a step forward in visual neuroscience by presenting a framework for an inference-based approach that is biologically inspired via attributes implicated in primate object recognition. The analysis will provide insight in explaining the exceptional proficiency of the VVS.
View details for DOI 10.3389/fncom.2020.00046
View details for PubMedID 32655388
View details for PubMedCentralID PMC7325008
A Layered Control Architecture of Sleep and Arousal
FRONTIERS IN COMPUTATIONAL NEUROSCIENCE
2020; 14: 8
Sleep and wakefulness are promoted not by a single neural pathway but via wake or sleep-promoting nodes distributed across layers of the brain. We equate each layer with a brain region in proposing a layered subsumption model for arousal based on a computational architecture. Beyond the brainstem the layers include the diencephalon (hypothalamus, thalamus), basal ganglia, and cortex. In light of existing empirical evidence, we propose that each layer have sleep and wake computations driven by similar high-level architecture and processing units. Specifically, an interconnected wake-promoting system is suggested as driving arousal in each brain layer with the processing converging to produce the features of wakefulness. In contrast, sleep-promoting GABAergic neurons largely project to and inhibit wake-promoting neurons. We propose a general pattern of caudal wake-promoting and sleep-promoting neurons having a strong effect on overall behavior. However, while rostral brain layers have less influence on sleep and wake, through descending projections, they can subsume the activity of caudal brain layers to promote arousal. The two models presented in this work will suggest computations for the layering and hierarchy. Through dynamic system theory several hypotheses are introduced for the interaction of controllers and systems that correspond to the different populations of neurons at each layer. The models will be drawn-upon to discuss future experiments to elucidate the structure of the hierarchy that exists among the sleep-arousal architecture.
View details for DOI 10.3389/fncom.2020.00008
View details for Web of Science ID 000517413600001
View details for PubMedID 32116622
View details for PubMedCentralID PMC7028742
Probabilistic Modeling of Pseudorabies Virus Infection in a Neural Circuit.
Journal of computational biology : a journal of computational molecular cell biology
2018; 25 (11): 1231-1246
Viral transneuronal tracing methods effectively label synaptically connected neurons in a time-dependent manner. However, the modeling of viral vectors has been largely absent. An objective of this article is to motivate and initiate a basis for computational modeling of viral labeling and the questions that can be investigated through modeling of pseudorabies virus (PRV) virion progression in a neural circuit. In particular, a mathematical model is developed for quantitative analysis of PRV infection. Probability expressions are presented to evaluate the progression of viral labeling along the neural circuit. The analysis brings forth various parameters, the numerical values of which must be attained through future experiments. This is the first computational model for PRV viral labeling of a neural circuit.
View details for DOI 10.1089/cmb.2017.0131
View details for PubMedID 30133311
A Communication-Theoretic Formulation of a Continuous Linear-Nonlinear Model of Retinal Ganglion Cells
View details for Web of Science ID 000434867200065
Deconstruction of the Beaten Path-Sidestep interaction network provides insights into neuromuscular system development.
An "interactome" screen of all Drosophila cell-surface and secreted proteins containing immunoglobulin superfamily (IgSF) domains discovered a network formed by paralogs of Beaten Path (Beat) and Sidestep (Side), a ligand-receptor pair that is central to motor axon guidance. Here we describe a new method for interactome screening, the Bio-Plex Interactome Assay (BPIA), which allows identification of many interactions in a single sample. Using the BPIA, we "deorphanized" four more members of the Beat-Side network. We confirmed interactions using surface plasmon resonance. The expression patterns of beat and side genes suggest that Beats are neuronal receptors for Sides expressed on peripheral tissues. side-VI is expressed in muscle fibers targeted by the ISNb nerve, as well as at growth cone choice points and synaptic targets for the ISN and TN nerves. beat-V genes, encoding Side-VI receptors, are expressed in ISNb and ISN motor neurons.
View details for PubMedID 28829740
An Autoregressive Approach to Inference in Populations of Correlated Stochastic Neurons
IEEE COMPUTER SOC. 2017: 2020-2024
View details for Web of Science ID 000442659900358
A Framework for Quantitative Modeling of Neural Circuits Involved in Sleep-to-Wake Transition.
Frontiers in neurology
2015; 6: 32-?
Identifying the neuronal circuits and dynamics of sleep-to-wake transition is essential to understanding brain regulation of behavioral states, including sleep-wake cycles, arousal, and hyperarousal. Recent work by different laboratories has used optogenetics to determine the role of individual neuromodulators in state transitions. The optogenetically driven data do not yet provide a multi-dimensional schematic of the mechanisms underlying changes in vigilance states. This work presents a modeling framework to interpret, assist, and drive research on the sleep-regulatory network. We identify feedback, redundancy, and gating hierarchy as three fundamental aspects of this model. The presented model is expected to expand as additional data on the contribution of each transmitter to a vigilance state becomes available. Incorporation of conductance-based models of neuronal ensembles into this model and existing models of cortical excitability will provide more comprehensive insight into sleep dynamics as well as sleep and arousal-related disorders.
View details for DOI 10.3389/fneur.2015.00032
View details for PubMedID 25767461
View details for PubMedCentralID PMC4341569
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