Online correction of licking‐induced brain motion during two‐photon imaging with a tunable lens

Chen, Jerry L.; Pfäffli, Oliver A.; Voigt, Fabian F.; Margolis, David J.; Helmchen, Fritjof

doi:10.1113/jphysiol.2013.259804

Cited by 51 publications

(42 citation statements)

References 42 publications

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“…An electrically-tunable lens (ETL; EL-C-10-30-VIS-LD, tuned to a range of +50-to +200-mm focal length, 10-mm clear aperture, Optotune AG) was installed at the rear aperture of the microscope objective, together with an offset lens (LC4232, −100-mm focal length fused silica lens, Thorlabs) 39 . The ETL was controlled by a custom current controller delivering a stable current output from 0 to 200 mA at a maximum of 5 V. The behavior data acquisition interface provided an analog voltage signal (0-10V) to the ETL controller for between-trial focusing and, in ~20% of training sessions, for online correction of lick-related movement artifacts 40 . For initial identification of YC-Nano140-expressing and tdTomato-positive S2P neurons, a volume stack was acquired using 800-nm excitation and yellow (542/50 nm) and red (610/75 nm) emission filters, respectively (AHF Analysentechnik).…”

Section: Cranial Window Implantation and Habituationmentioning

confidence: 99%

Pathway-specific reorganization of projection neurons in somatosensory cortex during learning

et al. 2015

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In the mammalian brain, sensory cortices exhibit plasticity during task learning, but how this alters information transferred between connected cortical areas remains unknown. We found that divergent subpopulations of cortico-cortical neurons in mouse whisker primary somatosensory cortex (S1) undergo functional changes reflecting learned behavior. We chronically imaged activity of S1 neurons projecting to secondary somatosensory (S2) or primary motor (M1) cortex in mice learning a texture discrimination task. Mice adopted an active whisking strategy that enhanced texture-related whisker kinematics, correlating with task performance. M1-projecting neurons reliably encoded basic kinematics features, and an additional subset of touch-related neurons was recruited that persisted past training. The number of S2-projecting touch neurons remained constant, but improved their discrimination of trial types through reorganization while developing activity patterns capable of discriminating the animal's decision. We propose that learning-related changes in S1 enhance sensory representations in a pathway-specific manner, providing downstream areas with task-relevant information for behavior.

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Section: Cranial Window Implantation and Habituationmentioning

confidence: 99%

Pathway-specific reorganization of projection neurons in somatosensory cortex during learning

et al. 2015

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“…In the Go/NoGo tactile decision-making task of Chen et al (2013a,b), mice learn to discriminate between two or more textures presented to the whisker by licking (Go) for water reward in response to one of the textures, and withholding licking (NoGo) in response to the distractor textures. We used this task to determine whether pupil dynamics are associated with specific aspects of task performance related to sensory cues, cue-driven behavioral responses (licking), cue-driven behavioral response inhibition (not licking), as well as behaviors such as whisking and licking performed outside of the task structure.…”

Section: Introductionmentioning

confidence: 99%

Pupil Dynamics Reflect Behavioral Choice and Learning in a Go/NoGo Tactile Decision-Making Task in Mice

Lee

Margolis

2016

Front. Behav. Neurosci.

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The eye’s pupil undergoes dynamic changes in diameter associated with cognitive effort, motor activity and emotional state, and can be used to index brain state across mammalian species. Recent studies in head-fixed mice have linked arousal-related pupil dynamics with global neural activity as well as the activity of specific neuronal populations. However, it has remained unclear how pupil dynamics in mice report trial-by-trial performance of behavioral tasks, and change on a longer time scale with learning. We measured pupil dynamics longitudinally as mice learned to perform a Go/NoGo tactile decision-making task. Mice learned to discriminate between two textures presented to the whiskers by licking in response to the Go texture (Hit trial) or withholding licking in response to the NoGo texture (Correct Reject trial, CR). Characteristic pupil dynamics were associated with behavioral choices: large-amplitude pupil dilation prior to and during licking accompanied Hit and False Alarm (FA) responses, while smaller amplitude dilation followed by constriction accompanied CR responses. With learning, the choice-dependent pupil dynamics became more pronounced, including larger amplitude dilations in both Hit and FA trials and earlier onset dilations in Hit and CR trials. A more pronounced constriction was also present in CR trials. Furthermore, pupil dynamics predicted behavioral choice increasingly with learning to greater than 80% accuracy. Our results indicate that pupil dynamics reflect behavioral choice and learning in head-fixed mice, and have implications for understanding decision- and learning-related neuronal activity in pupil-linked neural circuits.

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“…Finally, in vivo applications can experience motion-related fluorescence changes when there has been no actual change in neural activity. Genetically encoded ratiometric sensors (Thestrup et al, 2014) or online optical correction (Chen et al, 2013b) and motion modeling may help to avoid artifactual signals from corrupting feedback control inputs. In the case of actuation, particularly at the single-cell level as described below, motion can also lead to mistargeting of light patterns away from desired neurons without closed-loop adjustment of light patterns based on detected motion.…”

Section: Optogenetic Actuation and Optical Sensing Of Neural Activitymentioning

confidence: 99%

Closed-Loop and Activity-Guided Optogenetic Control

Grosenick

Marshel

Deisseroth

2015

Neuron

356

322

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Advances in optical manipulation and observation of neural activity have set the stage for widespread implementation of closed-loop and activity-guided optical control of neural circuit dynamics. Closing the loop optogenetically (i.e., basing optogenetic stimulation on simultaneously observed dynamics in a principled way) is a powerful strategy for causal investigation of neural circuitry. In particular, observing and feeding back the effects of circuit interventions on physiologically relevant timescales is valuable for directly testing whether inferred models of dynamics, connectivity, and causation are accurate in vivo. Here we highlight technical and theoretical foundations as well as recent advances and opportunities in this area, and we review in detail the known caveats and limitations of optogenetic experimentation in the context of addressing these challenges with closed-loop optogenetic control in behaving animals.

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Online correction of licking‐induced brain motion during two‐photon imaging with a tunable lens

Cited by 51 publications

References 42 publications

Pathway-specific reorganization of projection neurons in somatosensory cortex during learning

Pathway-specific reorganization of projection neurons in somatosensory cortex during learning

Pupil Dynamics Reflect Behavioral Choice and Learning in a Go/NoGo Tactile Decision-Making Task in Mice

Closed-Loop and Activity-Guided Optogenetic Control

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