Chemogenetic ligands for translational neurotheranostics, bioRxiv, 2018-12-08
AbstractDesigner Receptors Exclusively Activated by Designer Drugs (DREADDs) are a popular chemogenetic technology for manipulation of neuronal activity in uninstrumented awake animals with potential for precision medicine-based clinical theranostics. DREADD ligands developed to date are not appropriate for such translational applications. The prototypical DREADD agonist clozapine N-oxide (CNO) lacks brain entry and converts to clozapine. The second-generation DREADD agonist, Compound 21 (C21), was developed to overcome these limitations. We found that C21 has low brain penetrance, weak affinity, and low in vivo DREADD occupancy. To address these drawbacks, we developed two new DREADD agonists, JHU37152 and JHU37160, and the first dedicated positron emission tomography (PET) DREADD radiotracer, [18F]JHU37107. JHU37152 and JHU37160 exhibit high in vivo DREADD potency. [18F]JHU37107 combined with PET allows for DREADD detection in locally-targeted neurons and at their long-range projections, enabling for the first time, noninvasive and longitudinal neuronal projection mapping and potential for neurotheranostic applications.
biorxiv neuroscience 0-100-users 2018Intelligible speech synthesis from neural decoding of spoken sentences, bioRxiv, 2018-11-30
The ability to read out, or decode, mental content from brain activity has significant practical and scientific implications. For example, technology that translates cortical activity into speech would be transformative for people unable to communicate as a result of neurological impairment. Decoding speech from neural activity is challenging because speaking requires extremely precise and dynamic control of multiple vocal tract articulators on the order of milliseconds. Here, we designed a neural decoder that explicitly leverages the continuous kinematic and sound representations encoded in cortical activity to generate fluent and intelligible speech. A recurrent neural network first decoded vocal tract physiological signals from direct cortical recordings, and then transformed them to acoustic speech output. Robust decoding performance was achieved with as little as 25 minutes of training data. Naive listeners were able to accurately identify these decoded sentences. Additionally, speech decoding was not only effective for audibly produced speech, but also when participants silently mimed speech. These results advance the development of speech neuroprosthetic technology to restore spoken communication in patients with disabling neurological disorders.
biorxiv neuroscience 200-500-users 2018Using DeepLabCut for 3D markerless pose estimation across species and behaviors, bioRxiv, 2018-11-26
Noninvasive behavioral tracking of animals during experiments is crucial to many scientific pursuits. Extracting the poses of animals without using markers is often essential for measuring behavioral effects in biomechanics, genetics, ethology & neuroscience. Yet, extracting detailed poses without markers in dynamically changing backgrounds has been challenging. We recently introduced an open source toolbox called DeepLabCut that builds on a state-of-the-art human pose estimation algorithm to allow a user to train a deep neural network using limited training data to precisely track user-defined features that matches human labeling accuracy. Here, with this paper we provide an updated toolbox that is self contained within a Python package that includes new features such as graphical user interfaces and active-learning based network refinement. Lastly, we provide a step-by-step guide for using DeepLabCut.
biorxiv neuroscience 200-500-users 2018Hunger for Knowledge How the Irresistible Lure of Curiosity is Generated in the Brain, bioRxiv, 2018-11-22
Introductory ParagraphCuriosity is often portrayed as a desirable feature of human faculty. For example, a meta-analysis revealed that curiosity predicts academic performance above and beyond intelligence1, corroborating findings that curiosity supported long-term consolidation of learning2,3. However, curiosity may come at a cost of strong seductive power that sometimes puts people in a harmful situation. Here, with a set of three behavioural and two neuroimaging experiments including novel stimuli that strongly trigger curiosity (i.e. magic tricks), we examined the psychological and neural mechanisms underlying the irresistible lure of curiosity. We consistently demonstrated that across different samples people were indeed willing to gamble to expose themselves to physical risks (i.e. electric shocks) in order to satisfy their curiosity for trivial knowledge that carries no apparent instrumental values. Also, underlying this seductive power of curiosity is its incentive salience properties, which share common neural mechanisms with extrinsic incentives (i.e. hunger for foods). In particular, the two independent fMRI experiments using different kinds of curiosity-stimulating stimuli found replicable results that acceptance (compared to rejection) of curiosityincentive-driven gambles was accompanied with an enhanced activity in the striatum.
biorxiv neuroscience 100-200-users 2018Deep Neural Networks and Kernel Regression Achieve Comparable Accuracies for Functional Connectivity Prediction of Behavior and Demographics, bioRxiv, 2018-11-20
AbstractThere is significant interest in the development and application of deep neural networks (DNNs) to neuroimaging data. A growing literature suggests that DNNs outperform their classical counterparts in a variety of neuroimaging applications, yet there are few direct comparisons of relative utility. Here, we compared the performance of three DNN architectures and a classical machine learning algorithm (kernel regression) in predicting individual phenotypes from whole-brain resting-state functional connectivity (RSFC) patterns. One of the DNNs was a generic fully-connected feedforward neural network, while the other two DNNs were recently published approaches specifically designed to exploit the structure of connectome data. By using a combined sample of almost 10,000 participants from the Human Connectome Project (HCP) and UK Biobank, we showed that the three DNNs and kernel regression achieved similar performance across a wide range of behavioral and demographic measures. Furthermore, the generic feedforward neural network exhibited similar performance to the two state-of-the-art connectome-specific DNNs. When predicting fluid intelligence in the UK Biobank, performance of all algorithms dramatically improved when sample size increased from 100 to 1000 subjects. Improvement was smaller, but still significant, when sample size increased from 1000 to 5000 subjects. Importantly, kernel regression was competitive across all sample sizes. Overall, our study suggests that kernel regression is as effective as DNNs for RSFC-based behavioral prediction, while incurring significantly lower computational costs. Therefore, kernel regression might serve as a useful baseline algorithm for future studies.
biorxiv neuroscience 100-200-users 2018Distributed correlates of visually-guided behavior across the mouse brain, bioRxiv, 2018-11-20
Behavior arises from neuronal activity, but it is not known how the active neurons are distributed across brain regions and how their activity unfolds in time. Here, we used high-density Neuropixels probes to record from ~30,000 neurons in mice performing a visual contrast discrimination task. The task activated 60% of the neurons, involving nearly all 42 recorded brain regions, well beyond the regions activated by passive visual stimulation. However, neurons selective for choice (left vs. right) were rare, and found mostly in midbrain, striatum, and frontal cortex. Those in midbrain were typically activated prior to contralateral choices and suppressed prior to ipsilateral choices, consistent with a competitive midbrain circuit for adjudicating the subject’s choice. A brain-wide state shift distinguished trials in which visual stimuli led to movement. These results reveal concurrent representations of movement and choice in neurons widely distributed across the brain.
biorxiv neuroscience 100-200-users 2018