@article {69709, title = {In vivo multiplex imaging of dynamic neurochemical networks with designed far-red dopamine sensors}, journal = {Science}, year = {2025}, month = {2025 Jun 05}, abstract = {

Neurochemical signals like dopamine (DA) play a crucial role in a variety of brain functions through intricate interactions with other neuromodulators and intracellular signaling pathways. However, studying these complex networks has been hindered by the challenge of detecting multiple neurochemicals in vivo simultaneously. To overcome this limitation, we developed a single-protein chemigenetic DA sensor, HaloDA1.0, which combines a cpHaloTag-chemical dye approach with the G protein-coupled receptor activation-based (GRAB) strategy, providing high sensitivity for DA, sub-second response kinetics, and an extensive spectral range from far-red to near-infrared. When used together with existing green and red fluorescent neuromodulator sensors, Ca2+ indicators, cAMP sensors, and optogenetic tools, HaloDA1.0 provides high versatility for multiplex imaging in cultured neurons, brain slices, and behaving animals, facilitating in-depth studies of dynamic neurochemical networks.

}, doi = {10.1126/science.adt7705}, url = {https://doi.org/10.1126/science.adt7705}, author = {Zheng, Yu and Cai, Ruyi and Wang, Kui and Zhang, Junwei and Zhuo, Yizhou and Dong, Hui and Zhang, Yuqi and Wang, Yifan and Deng, Fei and Ji, En and Cui, Yiwen and Fang, Shilin and Zhang, Xinxin and Zhang, Kecheng and Wang, Jinxu and Li, Guochuan and Miao, Xiaolei and Wang, Zhenghua and Yang, Yuqing and Li, Shaochuang and Grimm, Jonathan and Johnsson, Kai and Schreiter, Eric and Lavis, Luke and Chen, Zhixing and Mu, Yu and Li, Yulong} } @article {68674, title = {Time-resolved correlation of distributed brain activity tracks E-I balance and accounts for diverse scale-free phenomena.}, journal = {Cell Reports}, volume = {42}, year = {2023}, month = {2023 Mar 24}, pages = {112254}, abstract = {

Much of systems neuroscience posits the functional importance of brain activity patterns that lack natural scales of sizes, durations, or frequencies. The field has developed prominent, and sometimes competing, explanations for the nature of this scale-free activity. Here, we reconcile these explanations across species and modalities. First, we link estimates of excitation-inhibition (E-I) balance with time-resolved correlation of distributed brain activity. Second, we develop an unbiased method for sampling time series constrained by this time-resolved correlation. Third, we use this method to show that estimates of E-I balance account for diverse scale-free phenomena without need to attribute additional function or importance to these phenomena. Collectively, our results simplify existing explanations of scale-free brain activity and provide stringent tests on future theories that seek to transcend these explanations.

}, issn = {2211-1247}, doi = {10.1016/j.celrep.2023.112254}, author = {Nanda, Aditya and Johnson, Graham W and Mu, Yu and Ahrens, Misha B and Chang, Catie and Englot, Dario J and Breakspear, Michael and Rubinov, Mikail} } @article {65797, title = {Brain-wide, scale-wide physiology underlying behavioral flexibility in zebrafish.}, journal = {Current Opinion in Neurobiology}, volume = {64}, year = {2020}, month = {2020 Oct 19}, pages = {151-160}, abstract = {

The brain is tasked with choosing actions that maximize an animal{\textquoteright}s chances of survival and reproduction. These choices must be flexible and informed by the current state of the environment, the needs of the body, and the outcomes of past actions. This information is physiologically encoded and processed across different brain regions on a wide range of spatial scales, from molecules in single synapses to networks of brain areas. Uncovering these spatially distributed neural interactions underlying behavior requires investigations that span a similar range of spatial scales. Larval zebrafish, given their small size, transparency, and ease of genetic access, are a good model organism for such investigations, allowing the use of modern microscopy, molecular biology, and computational techniques. These approaches are yielding new insights into the mechanistic basis of behavioral states, which we review here and compare to related studies in mammalian species.

}, issn = {1873-6882}, doi = {10.1016/j.conb.2020.08.013}, author = {Mu, Yu and Narayan, Sujatha and Mensh, Brett D and Ahrens, Misha B} } @article {65023, title = {Glia accumulate evidence that actions are futile and suppress unsuccessful behavior.}, journal = {Cell}, volume = {178}, year = {2019}, month = {2019 Jun 27}, pages = {27-43}, abstract = {

When a behavior repeatedly fails to achieve its goal, animals often give up and become passive, which can be strategic for preserving energy or regrouping between attempts. It is unknown how the brain identifies behavioral failures and mediates this behavioral-state switch. In larval zebrafish swimming in virtual reality, visual feedback can be withheld so that swim attempts fail to trigger expected visual flow. After tens of seconds of such motor futility, animals became passive for similar durations. Whole-brain calcium imaging revealed noradrenergic neurons that responded specifically to failed swim attempts and radial astrocytes whose calcium levels accumulated with increasing numbers of failed attempts. Using cell ablation and optogenetic or chemogenetic activation, we found that noradrenergic neurons progressively activated brainstem radial astrocytes, which then suppressed swimming. Thus, radial astrocytes perform a computation critical for behavior: they accumulate evidence that current actions are ineffective and consequently drive changes in behavioral states.

}, doi = {10.1016/j.cell.2019.05.050}, author = {Mu, Yu and Bennett, Davis V and Rubinov, Mikail and Narayan, Sujatha and Yang, Chao-Tsung and Tanimoto, Masashi and Mensh, Brett D and Looger, Loren L and Ahrens, Misha B} } @article {49535, title = {Brain-wide circuit interrogation at the cellular level guided by online analysis of neuronal function.}, journal = {Nature Methods}, volume = {15}, year = {2018}, month = {2018 Nov 30}, pages = {1117-1125}, abstract = {

Whole-brain imaging allows for comprehensive functional mapping of distributed neural pathways, but neuronal perturbation experiments are usually limited to targeting predefined regions or genetically identifiable cell types. To complement whole-brain measures of activity with brain-wide manipulations for testing causal interactions, we introduce a system that uses measuredactivity patterns to guide optical perturbations of any subset of neurons in the same fictively behaving larval zebrafish. First, a light-sheet microscope collects whole-brain data that are rapidly analyzed by a distributed computing system to generate functional brain maps. On the basis of these maps, the experimenter can then optically ablate neurons and image activity changes across the brain. We applied this method to characterize contributions of behaviorally tuned populations to the optomotor response. We extended the system to optogenetically stimulate arbitrary subsets of neurons during whole-brain imaging. These open-source methods enable delineating the contributions of neurons to brain-wide circuit dynamics and behavior in individual animals.

}, doi = {10.1038/s41592-018-0221-x}, author = {Vladimirov, Nikita and Wang, Chen and H{\"o}ckendorf, Burkhard and Pujala, Avinash and Tanimoto, Masashi and Mu, Yu and Yang, Chao-Tsung and Wittenbach, Jason and Freeman, Jeremy and Preibisch, Stephan and Koyama, Minoru and Keller, Philipp J and Ahrens, Misha B} } @article {49520, title = {Brain-wide organization of neuronal activity and convergent sensorimotor transformations in larval zebrafish.}, journal = {Neuron}, volume = {100}, year = {2018}, month = {2018 Nov 21}, pages = {876-890.e5}, abstract = {

Simultaneous recordings of large populations of neurons in behaving animals allow detailed observation of high-dimensional, complex brain activity. However, experimental approaches often focus on singular behavioral paradigms or brain areas. Here, we recorded whole-brain neuronal activity of larval zebrafish presented with a battery of visual stimuli while recording fictive motor output. We identified neurons tuned to each stimulus type and motor output and discovered groups of neurons in the anterior hindbrain that respond to different stimuli eliciting similar behavioral responses. These convergent sensorimotor representations were only weakly correlated to instantaneous motor activity, suggesting that they critically inform, but do not directly generate, behavioral choices. To catalog brain-wide activity beyond explicit sensorimotor processing, we developed an unsupervised clustering technique that organizes neurons into functional groups. These analyses enabled a broad overview of the functional organization of the brain and revealed numerous brain nuclei whose neurons exhibit concerted activity patterns.

}, issn = {1097-4199}, doi = {10.1016/j.neuron.2018.09.042}, author = {Chen, Xiuye and Mu, Yu and Hu, Yu and Kuan, Aaron T and Nikitchenko, Maxim and Randlett, Owen and Chen, Alex B and Gavornik, Jeffery P and Sompolinsky, Haim and Engert, Florian and Ahrens, Misha B} } @article {48716, title = {Multi-scale approaches for high-speed imaging and analysis of large neural populations.}, journal = {PLoS Computational Biology}, volume = {13}, year = {2017}, month = {2017 Aug 03}, pages = {e1005685}, abstract = {

Progress in modern neuroscience critically depends on our ability to observe the activity of large neuronal populations with cellular spatial and high temporal resolution. However, two bottlenecks constrain efforts towards fast imaging of large populations. First, the resulting large video data is challenging to analyze. Second, there is an explicit tradeoff between imaging speed, signal-to-noise, and field of view: with current recording technology we cannot image very large neuronal populations with simultaneously high spatial and temporal resolution. Here we describe multi-scale approaches for alleviating both of these bottlenecks. First, we show that spatial and temporal decimation techniques based on simple local averaging provide order-of-magnitude speedups in spatiotemporally demixing calcium video data into estimates of single-cell neural activity. Second, once the shapes of individual neurons have been identified at fine scale (e.g., after an initial phase of conventional imaging with standard temporal and spatial resolution), we find that the spatial/temporal resolution tradeoff shifts dramatically: after demixing we can accurately recover denoised fluorescence traces and deconvolved neural activity of each individual neuron from coarse scale data that has been spatially decimated by an order of magnitude. This offers a cheap method for compressing this large video data, and also implies that it is possible to either speed up imaging significantly, or to "zoom out" by a corresponding factor to image order-of-magnitude larger neuronal populations with minimal loss in accuracy or temporal resolution.

}, issn = {1553-7358}, doi = {10.1371/journal.pcbi.1005685}, author = {Friedrich, Johannes and Yang, Weijian and Soudry, Daniel and Mu, Yu and Ahrens, Misha B and Yuste, Rafael and Peterka, Darcy S and Paninski, Liam} } @article {47380, title = {Brain-wide mapping of neural activity controlling zebrafish exploratory locomotion.}, journal = {eLife}, year = {2016}, month = {2016 Mar 22}, abstract = {

In the absence of salient sensory cues to guide behavior, animals must still execute sequences of motor actions in order to forage and explore. How such successive motor actions are coordinated to form global locomotion trajectories is unknown. We mapped the structure of larval zebrafish swim trajectories in homogeneous environments and found that trajectories were characterized by alternating sequences of repeated turns to the left and to the right. Using whole-brain light-sheet imaging, we identified activity relating to the behavior in specific neural populations that we termed the anterior rhombencephalic turning region (ARTR). ARTR perturbations biased swim direction and reduced the dependence of turn direction on turn history, indicating that the ARTR is part of a network generating the temporal correlations in turn direction. We also find suggestive evidence for ARTR mutual inhibition and ARTR projections to premotor neurons. Finally, simulations suggest the observed turn sequences may underlie efficient exploration of local environments.

}, doi = {10.7554/eLife.12741}, url = {http://dx.doi.org/10.7554/eLife.12741}, author = {Dunn, Timothy W. and Mu, Yu and Narayan, Sujatha and Randlett, Owen and Naumann, Eva A. and Yang, Chao-Tsung and Schier, Alexander F. and Freeman, Jeremy and Engert, Florian and Ahrens, Misha B.} } @article {47262, title = {Simultaneous denoising, deconvolution, and demixing of calcium imaging data.}, journal = {Neuron}, volume = {89}, year = {2016}, month = {2016 Jan 20}, pages = {285-99}, abstract = {

We present a modular approach for analyzing calcium imaging recordings of large neuronal ensembles. Our goal is to simultaneously identify the locations of the neurons, demix spatially overlapping components, and denoise and deconvolve the spiking activity from the slow dynamics of the calcium indicator. Our approach relies on a constrained nonnegative matrix factorization that expresses the spatiotemporal fluorescence activity as the product of a spatial matrix that encodes the spatial footprint of each neuron in the optical field and a temporal matrix that characterizes the calcium concentration of each neuron over time. This framework is combined with a novel constrained deconvolution approach that extracts estimates of neural activity from fluorescence traces, to create a spatiotemporal processing algorithm that requires minimal parameter tuning. We demonstrate the general applicability of our method by applying it to in\ vitro and in\ vivo multi-neuronal imaging data, whole-brain light-sheet imaging data, and dendritic imaging data.

}, issn = {1097-4199}, doi = {10.1016/j.neuron.2015.11.037}, author = {Pnevmatikakis, Eftychios A and Soudry, Daniel and Gao, Yuanjun and Machado, Timothy A and Merel, Josh and Pfau, David and Reardon, Thomas and Mu, Yu and Lacefield, Clay and Yang, Weijian and Ahrens, Misha and Bruno, Randy and Jessell, Thomas M and Peterka, Darcy S and Yuste, Rafael and Paninski, Liam} } @article {45177, title = {Light-sheet functional imaging in fictively behaving zebrafish.}, journal = {Nature Methods}, volume = {11}, year = {2014}, month = {2014 Jul 27}, pages = {883-4}, abstract = {

The processing of sensory input and the generation of behavior involves large networks of neurons, which necessitates new technology for recording from many neurons in behaving animals. In the larval zebrafish, light-sheet microscopy can be used to record the activity of almost all neurons in the brain simultaneously at single-cell resolution. Existing implementations, however, cannot be combined with visually driven behavior because the light sheet scans over the eye, interfering with presentation of controlled visual stimuli. Here we describe a system that overcomes the confounding eye stimulation through the use of two light sheets and combines whole-brain light-sheet imaging with virtual reality for fictively behaving larval zebrafish.

}, issn = {1548-7105}, doi = {10.1038/nmeth.3040}, author = {Vladimirov, Nikita and Mu, Yu and Kawashima, Takashi and Bennett, Davis V and Yang, Chao-Tsung and Looger, Loren L and Keller, Philipp J and Freeman, Jeremy and Ahrens, Misha B} } @article {45065, title = {Mapping brain activity at scale with cluster computing.}, journal = {Nature Methods}, volume = {11}, year = {2014}, month = {2014 Jul 27}, pages = {941-950}, abstract = {

Understanding brain function requires monitoring and interpreting the activity of large networks of neurons during behavior. Advances in recording technology are greatly increasing the size and complexity of neural data. Analyzing such data will pose a fundamental bottleneck for neuroscience. We present a library of analytical tools called Thunder built on the open-source Apache Spark platform for large-scale distributed computing. The library implements a variety of univariate and multivariate analyses with a modular, extendable structure well-suited to interactive exploration and analysis development. We demonstrate how these analyses find structure in large-scale neural data, including whole-brain light-sheet imaging data from fictively behaving larval zebrafish, and two-photon imaging data from behaving mouse. The analyses relate neuronal responses to sensory input and behavior, run in minutes or less and can be used on a private cluster or in the cloud. Our open-source framework thus holds promise for turning brain activity mapping efforts into biological insights.

}, issn = {1548-7091}, doi = {10.1038/nmeth.3041}, author = {Freeman, Jeremy and Vladimirov, Nikita and Kawashima, Takashi and Mu, Yu and Sofroniew, Nicholas J. and Bennett, Davis V. and Rosen, Joshua and Yang, Chao-Tsung and Looger, Loren L. and Ahrens, Misha B.} }