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Open Access Publications from the University of California

Scholarly Works (4 results)

  • Article
  • Peer Reviewed
UC San Diego Previously Published Works (2018)
Two-photon calcium imaging has been extensively used to record neural activity in the brain. It has been long used solely with post-hoc analysis, but the recent efforts began to include closed-loop experiments. Closed-loop experiments pose new challenges because they require fast, real-time image processing without iterative parameter tuning. When imaging awake animals, one of the crucial steps of post hoc image analysis is correction of lateral motion artifacts. In most of the closed-loop experiments, this step has not been implemented and ignored due to technical difficulties. We recently reported the first experiments with real-time processing of calcium imaging that included lateral motion correction. Here, we report the details of the implementation of fast motion correction and present performance analysis across several algorithms with different parameters. Additionally, we introduce a novel method to estimate baseline calcium signal using kernel density estimate, which reduces the number of parameters to be tuned. Combined, we propose a novel software pipeline of real-time image processing suited for closed-loop experiments. The pipeline is also useful for rapid post hoc image processing.
    Cover page: Real-Time Processing of Two-Photon Calcium Imaging Data Including Lateral Motion Artifact CorrectionCreative Commons 'BY' version 4.0 license
    • Thesis
    • Peer Reviewed
    UC San Diego Electronic Theses and Dissertations (2018)

    Direct communication with the brain by brain-computer interfaces (BCI) has been one of the goals of neuroscience due to their potential therapeutic applications. It has been shown that animals, including humans, can learn to control the movement of prosthetic devices with BCI. The control, however, is far from smooth and precise control of the limbs of healthy individuals, and there is a need to further improve the performance of BCI.

    One of the limitations of the previous studies is that they have been mainly targeting somatic activity of excitatory neurons, while different cell types possesses different functions in cortical computations and likely different capacities to control BCI. Here, we made a first step in addressing this issue by tracking the plastic changes of three major types of cortical inhibitory neurons (INs) during a BCI task using two-photon calcium imaging. Mice were rewarded when the activity of the positive target neuron (N+) exceeded that of the negative

    target neuron (N-) beyond a set threshold. Mice improved the performance with all subtypes using subtype-specific strategies. When parvalbumin (PV)-expressing INs were targeted, the activity of N- decreased. However, targeting of somatostatin (SOM)- and vasoactive intestinal peptide (VIP)-expressing INs led to an increase of the N+ activity. These results demonstrate that INs can be individually modulated in a subtype-specific manner, and highlight the versatility of neural circuits in adapting to new demands by using cell-type specific strategies. Another potential limitation is that the mechanisms underlying BCI learning, and how the plasticity of the neurons contributes to learning has not been elucidated yet. In a motor learning task, it has been shown that dendritic spine dynamics and clustering are associated with learning, and nonlinear summation of excitatory inputs among nearby dendritic spines determine how the neuron responds. Does the spatial distribution of spines affect BCI learning? Does BCI training induce further spine formation and elimination? Answering those questions will help understanding the mechanisms of BCI learning. As a first step, here, I applied a modified neural feedback system to spine imaging and examined whether mice can learn to modulate spine-specific activity.

    These are the first BCI tasks with lateral motion artifact correction. The implemented algorithms were presented with performance analysis.

      Cover page: Learning and plasticity for a brain-computer interface task with two-photon calcium imaging
      • Article
      • Peer Reviewed
      UC San Diego Previously Published Works (2019)
      Motor learning involves reorganization of the primary motor cortex (M1). However, it remains unclear how the involvement of M1 in movement control changes during long-term learning. To address this, we trained mice in a forelimb-based motor task over months and performed optogenetic inactivation and two-photon calcium imaging in M1 during the long-term training. We found that M1 inactivation impaired the forelimb movements in the early and middle stages, but not in the late stage, indicating that the movements that initially required M1 became independent of M1. As previously shown, M1 population activity became more consistent across trials from the early to middle stage while task performance rapidly improved. However, from the middle to late stage, M1 population activity became again variable despite consistent expert behaviors. This later decline in activity consistency suggests dissociation between M1 and movements. These findings suggest that long-term motor learning can disengage M1 from movement control.
        Cover page: Disengagement of motor cortex from movement control during long-term learningCreative Commons 'BY-NC' version 4.0 license
        • Article
        • Peer Reviewed
        UC San Francisco Previously Published Works (2023)
        BACKGROUND: Androgen deprivation therapy (ADT) with radiotherapy can benefit patients with localized prostate cancer. However, ADT can negatively impact quality of life, and there remain no validated predictive models to guide its use. METHODS: We used digital pathology images from pretreatment prostate tissue and clinical data from 5727 patients enrolled in five phase 3 randomized trials, in which treatment was radiotherapy with or without ADT, as our data source to develop and validate an artificial intelligence (AI)–derived predictive patient-specific model that would determine which patients would develop the primary end point of distant metastasis. The model used baseline data to provide a binary output that a given patient will likely benefit from ADT or not. After the model was locked, validation was performed using data from NRG Oncology/Radiation Therapy Oncology Group (RTOG) 9408 (n=1594), a trial that randomly assigned men to radiotherapy plus or minus 4 months of ADT. Fine–Gray regression and restricted mean survival times were used to assess the interaction between treatment and the predictive model and within predictive model–positive, i.e., benefited from ADT, and –negative subgroup treatment effects. RESULTS: Overall, in the NRG/RTOG 9408 validation cohort (14.9 years of median follow-up), ADT significantly improved time to distant metastasis. Of these enrolled patients, 543 (34%) were model positive, and ADT significantly reduced the risk of distant metastasis compared with radiotherapy alone. Of 1051 patients who were model negative, ADT did not provide benefit. CONCLUSIONS: Our AI-based predictive model was able to identify patients with a predominantly intermediate risk for prostate cancer likely to benefit from short-term ADT. (Supported by a grant [U10CA180822] from NRG Oncology Statistical and Data Management Center, a grant [UG1CA189867] from NCI Community Oncology Research Program, a grant [U10CA180868] from NRG Oncology Operations, and a grant [U24CA196067] from NRG Specimen Bank from the National Cancer Institute and by Artera, Inc. ClinicalTrials.gov numbers NCT00767286, NCT00002597, NCT00769548, NCT00005044, and NCT00033631.)
          Cover page: Artificial Intelligence Predictive Model for Hormone Therapy Use in Prostate Cancer.
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