@article {69542, title = {Neural circuit basis of placebo pain relief.}, journal = {Nature}, year = {2024}, month = {2024 Jul 24}, abstract = {

Placebo effects are striking demonstrations of mind-body interactions . During pain perception, in the absence of any treatment, an expectation of pain relief can reduce the experience of pain, a phenomenon known as placebo analgesia . However, despite the strength of placebo effects and their impact on everyday human experience and failure of clinical trials for new therapeutics , the neural circuit basis of placebo effects has remained elusive. Here, we show that analgesia from the expectation of pain relief is mediated by rostral anterior cingulate cortex (rACC) neurons that project to the pontine nucleus (rACC{\textrightarrow}Pn), a pre-cerebellar nucleus with no established function in pain. We created a behavioral assay that generates placebo-like anticipatory pain relief in mice. In vivo calcium imaging of neural activity and electrophysiological recordings in brain slices showed that expectations of pain relief boost the activity of rACC{\textrightarrow}Pn neurons and potentiate neurotransmission in this pathway. Transcriptomic studies of Pn neurons revealed an abundance of opioid receptors, further suggesting a role in pain modulation. Inhibition of the rACC{\textrightarrow}Pn pathway disrupted placebo analgesia and decreased pain thresholds, whereas activation elicited analgesia in the absence of placebo conditioning. Finally, Purkinje cells exhibited activity patterns resembling those of rACC{\textrightarrow}Pn neurons during pain relief expectation, providing cellular-level evidence of a role for the cerebellum in cognitive pain modulation. These findings open the possibility of targeting this prefrontal cortico-ponto-cerebellar pathway with drugs or neurostimulation to treat pain.

}, issn = {1476-4687}, doi = {10.1038/s41586-024-07816-z}, author = {Chen, Chong and Niehaus, Jesse K and Dinc, Fatih and Huang, Karen L and Barnette, Alexander L and Tassou, Adrien and Shuster, S Andrew and Wang, Lihua and Lemire, Andrew and Menon, Vilas and Ritola, Kimberly and Hantman, Adam and Zeng, Hongkui and Schnitzer, Mark J and Scherrer, Gr{\'e}gory} } @article {49473, title = {Long-Term Consolidation of Ensemble Neural Plasticity Patterns in Hippocampal Area CA1.}, journal = {Cell reports}, volume = {25}, year = {2018}, month = {2018 Oct 16}, pages = {640-650.e2}, abstract = {

Neural network remodeling underpins the ability to remember life experiences, but little is known about the long-term plasticity of neural populations. To study how the brain encodes episodic events, we used time-lapse two-photon microscopy and a fluorescent reporter of neural plasticity based on an enhanced form of the synaptic activity-responsive element (E-SARE) within the Arc promoter to track thousands of CA1 hippocampal pyramidal cells over weeks in mice that repeatedly encountered different environments. Each environment evokes characteristic patterns of ensemble neural plasticity, but with each encounter, the set of activated cells gradually evolves. After repeated exposures, the plasticity patterns evoked by an individual environment progressively stabilize. Compared with young adults, plasticity patterns in aged mice are less specific to individual environments and less stable across repeat experiences. Long-term consolidation of hippocampal plasticity patterns may support long-term memory formation, whereas weaker consolidation in aged subjects might reflect declining memory function.

}, issn = {2211-1247}, doi = {10.1016/j.celrep.2018.09.064}, author = {Attardo, Alessio and Lu, Ju and Kawashima, Takashi and Okuno, Hiroyuki and Fitzgerald, James E and Bito, Haruhiko and Schnitzer, Mark J} } @article {48389, title = {Impermanence of dendritic spines in live adult CA1 hippocampus.}, journal = {Nature}, volume = {523}, year = {2015}, month = {2015 Jul 30}, pages = {592-6}, abstract = {

The mammalian hippocampus is crucial for episodic memory formation and transiently retains information for about 3-4 weeks in adult mice and longer in humans. Although neuroscientists widely believe that neural synapses are elemental sites of information storage, there has been no direct evidence that hippocampal synapses persist for time intervals commensurate with the duration of hippocampal-dependent memory. Here we tested the prediction that the lifetimes of hippocampal synapses match the longevity of hippocampal memory. By using time-lapse two-photon microendoscopy in the CA1 hippocampal area of live mice, we monitored the turnover dynamics of the pyramidal neurons{\textquoteright} basal dendritic spines, postsynaptic structures whose turnover dynamics are thought to reflect those of excitatory synaptic connections. Strikingly, CA1 spine turnover dynamics differed sharply from those seen previously in the neocortex. Mathematical modelling revealed that the data best matched kinetic models with a single population of spines with a mean lifetime of approximately 1-2 weeks. This implies \~{}100\% turnover in \~{}2-3 times this interval, a near full erasure of the synaptic connectivity pattern. Although N-methyl-d-aspartate (NMDA) receptor blockade stabilizes spines in the neocortex, in CA1 it transiently increased the rate of spine loss and thus lowered spine density. These results reveal that adult neocortical and hippocampal pyramidal neurons have divergent patterns of spine regulation and quantitatively support the idea that the transience of hippocampal-dependent memory directly reflects the turnover dynamics of hippocampal synapses.

}, keywords = {Animals, CA1 Region, Hippocampal, Dendritic Spines, Endoscopy, Kinetics, Male, Memory, Episodic, Mice, Neocortex, Neuronal Plasticity, Photons, Pyramidal Cells, Receptors, N-Methyl-D-Aspartate, Synapses, Time Factors}, issn = {1476-4687}, doi = {10.1038/nature14467}, author = {Attardo, Alessio and Fitzgerald, James E and Schnitzer, Mark J} } @article {46189, title = {The neural representation of taste quality at the periphery.}, journal = {Nature}, volume = {517}, year = {2015}, month = {2015 Jan 15}, pages = {373-6}, abstract = {

The mammalian taste system is responsible for sensing and responding to the five basic taste qualities: sweet, sour, bitter, salty and umami. Previously, we showed that each taste is detected by dedicated taste receptor cells (TRCs) on the tongue and palate epithelium. To understand how TRCs transmit information to higher neural centres, we examined the tuning properties of large ensembles of neurons in the first neural station of the gustatory system. Here, we generated and characterized a collection of transgenic mice expressing a genetically encoded calcium indicator in central and peripheral neurons, and used a gradient refractive index microendoscope combined with high-resolution two-photon microscopy to image taste responses from ganglion neurons buried deep at the base of the brain. Our results reveal fine selectivity in the taste preference of ganglion neurons; demonstrate a strong match between TRCs in the tongue and the principal neural afferents relaying taste information to the brain; and expose the highly specific transfer of taste information between taste cells and the central nervous system.

}, keywords = {Animals, Calcium, Geniculate Ganglion, Mice, Mice, Transgenic, Neurons, Taste, Taste Buds, Taste Perception, Tongue}, issn = {1476-4687}, doi = {10.1038/nature13873}, author = {Barretto, Robert P J and Gillis-Smith, Sarah and Chandrashekar, Jayaram and Yarmolinsky, David A and Schnitzer, Mark J and Ryba, Nicholas J P and Zuker, Charles S} } @article {47017, title = {Optogenetics: 10 years after ChR2 in neurons-views from the community.}, journal = {Nature Neuroscience}, volume = {18}, year = {2015}, month = {2015 Aug 26}, pages = {1202-12}, issn = {1546-1726}, doi = {10.1038/nn.4106}, author = {Adamantidis, Antoine and Arber, Silvia and Bains, Jaideep S and Bamberg, Ernst and Bonci, Antonello and Buzs{\'a}ki, Gy{\"o}rgy and Cardin, Jessica A and Costa, Rui M and Dan, Yang and Goda, Yukiko and Graybiel, Ann M and H{\"a}usser, Michael and Hegemann, Peter and Huguenard, John R and Insel, Thomas R and Janak, Patricia H and Johnston, Daniel and Josselyn, Sheena A and Koch, Christof and Kreitzer, Anatol C and L{\"u}scher, Christian and Malenka, Robert C and Miesenb{\"o}ck, Gero and Nagel, Georg and Roska, Botond and Schnitzer, Mark J and Shenoy, Krishna V and Soltesz, Ivan and Sternson, Scott M and Tsien, Richard W and Tsien, Roger Y and Turrigiano, Gina G and Tye, Kay M and Wilson, Rachel I} } @article {48391, title = {Photon shot noise limits on optical detection of neuronal spikes and estimation of spike timing.}, journal = {Biophysical journal}, volume = {104}, year = {2013}, month = {2013 Jan 08}, pages = {51-62}, abstract = {

Optical approaches for tracking neural dynamics are of widespread interest, but a theoretical framework quantifying the physical limits of these techniques has been lacking. We formulate such a framework by using signal detection and estimation theory to obtain physical bounds on the detection of neural spikes and the estimation of their occurrence times as set by photon counting statistics (shot noise). These bounds are succinctly expressed via a discriminability index that depends on the kinetics of the optical indicator and the relative fluxes of signal and background photons. This approach facilitates quantitative evaluations of different indicators, detector technologies, and data analyses. Our treatment also provides optimal filtering techniques for optical detection of spikes. We compare various types of Ca(2+) indicators and show that background photons are a chief impediment to voltage sensing. Thus, voltage indicators that change color in response to membrane depolarization may offer a key advantage over those that change intensity. We also examine fluorescence resonance energy transfer indicators and identify the regimes in which the widely used ratiometric analysis of signals is substantially suboptimal. Overall, by showing how different optical factors interact to affect signal quality, our treatment offers a valuable guide to experimental design and provides measures of confidence to assess optically extracted traces of neural activity.

}, keywords = {Action Potentials, Animals, Computer Simulation, Fluorescence Resonance Energy Transfer, Neurons, Optical Imaging, Photons, Signal Processing, Computer-Assisted, Time Factors}, issn = {1542-0086}, doi = {10.1016/j.bpj.2012.07.058}, author = {Wilt, Brian A and Fitzgerald, James E and Schnitzer, Mark J} } @article {48392, title = {Estimation theoretic measure of resolution for stochastic localization microscopy.}, journal = {Physical review letters}, volume = {109}, year = {2012}, month = {2012 Jul 27}, pages = {048102}, abstract = {

One approach to super-resolution fluorescence microscopy, termed stochastic localization microscopy, relies on the nanometer scale spatial localization of individual fluorescent emitters that stochastically label specific features of the specimen. The precision of emitter localization is an important determinant of the resulting image resolution but is insufficient to specify how well the derived images capture the structure of the specimen. We address this deficiency by considering the inference of specimen structure based on the estimated emitter locations. By using estimation theory, we develop a measure of spatial resolution that jointly depends on the density of the emitter labels, the precision of emitter localization, and prior information regarding the spatial frequency content of the labeled object. The Nyquist criterion does not set the scaling of this measure with emitter number. Given prior information and a fixed emitter labeling density, our resolution measure asymptotes to a finite value as the precision of emitter localization improves. By considering the present experimental capabilities, this asymptotic behavior implies that further resolution improvements require increases in labeling density above typical current values. Our treatment also yields algorithms to enhance reliable image features. Overall, our formalism facilitates the rigorous statistical interpretation of the data produced by stochastic localization imaging techniques.

}, keywords = {Microscopy, Fluorescence, Models, Theoretical, Stochastic Processes}, issn = {1079-7114}, doi = {10.1103/PhysRevLett.109.048102}, author = {Fitzgerald, James E and Lu, Ju and Schnitzer, Mark J} } @article {48393, title = {Symmetries in stimulus statistics shape the form of visual motion estimators.}, journal = {Proceedings of the National Academy of Sciences of the United States of America}, volume = {108}, year = {2011}, month = {2011 Aug 02}, pages = {12909-14}, abstract = {

The estimation of visual motion has long been studied as a paradigmatic neural computation, and multiple models have been advanced to explain behavioral and neural responses to motion signals. A broad class of models, originating with the Reichardt correlator model, proposes that animals estimate motion by computing a temporal cross-correlation of light intensities from two neighboring points in visual space. These models provide a good description of experimental data in specific contexts but cannot explain motion percepts in stimuli lacking pairwise correlations. Here, we develop a theoretical formalism that can accommodate diverse stimuli and behavioral goals. To achieve this, we treat motion estimation as a problem of Bayesian inference. Pairwise models emerge as one component of the generalized strategy for motion estimation. However, correlation functions beyond second order enable more accurate motion estimation. Prior expectations that are asymmetric with respect to bright and dark contrast use correlations of both even and odd orders, and we show that psychophysical experiments using visual stimuli with symmetric probability distributions for contrast cannot reveal whether the subject uses odd-order correlators for motion estimation. This result highlights a gap in previous experiments, which have largely relied on symmetric contrast distributions. Our theoretical treatment provides a natural interpretation of many visual motion percepts, indicates that motion estimation should be revisited using a broader class of stimuli, demonstrates how correlation-based motion estimation is related to stimulus statistics, and provides multiple experimentally testable predictions.

}, keywords = {Algorithms, Animals, Computer Simulation, Models, Neurological, Motion, Motion Perception, Photic Stimulation, Time Factors}, issn = {1091-6490}, doi = {10.1073/pnas.1015680108}, author = {Fitzgerald, James E and Katsov, Alexander Y and Clandinin, Thomas R and Schnitzer, Mark J} }