, 2005; Tsuboi and Shults, 2002). In these paradigms, the neuroprotective effect of Shh on mesencephalic DA neurons is comparable to that observed with striatal delivery of GDNF (Dass et al., 2002). In vitro, Shh synergizes with neural growth factor (NGF) in providing trophic support to basal forebrain-derived, postnatal ACh neurons (Reilly et al., 2002). Despite these observations, it

is not clear whether there is a functionally relevant source of Shh that could act in the mature mesostriatal system and if so, which cell types would communicate by Shh signaling. Here, we present evidence for reciprocal, trophic factor signaling between mesencephalic DA, striatal ACh, and FS neurons. We show that DA neurons

utilize Shh to signal to ACh and FS interneurons INCB024360 datasheet in the striatum where it regulates extracellular ACh tone, expression of GDNF, and maintenance of these neurons. Conversely, Shh expression by DA neurons is repressed by signals that originate from ACh neurons and engage the canonical selleck kinase inhibitor GDNF receptor Ret on DA neurons. The conditional ablation of Shh from DA neurons results in a progressive model of PD with face, construct and predictive validity. Thus, our results shed light onto aspects of the chemical neuroanatomy of the basal ganglia and may have far-reaching implications for the understanding of the physiopathology and the treatment of movement disorders such as PD. To examine whether Shh-mediated signaling occurs among neurons of the mesostriatal circuit, we first visualized expression of Shh in the adult brain using mice heterozygous for a conditional, gene expression tracer allele of Shh (Shh-nLZC/+) ( Figure S1A available online). We observed Shh expression by all tyrosine hydroxylase-positive (Th+) neurons in the substantia nigra pars compacta (SNpc) (

Figures 2A and 2B), the ventral tegmental area (VTA) ( Figure 2A), and the retrorubral field (RRF, data not shown) along the entire rostro-caudal axis of these nuclei at 3 months of age Org 27569 (100 ± 0%, 683 cells, n = 2). We did not observe Shh expression by Th+ neurons of the diencephalon or olfactory bulb or by cells in the striatum (data not shown). To determine whether Shh signaling within the mesostriatal circuit is of physiological relevance, we selectively ablated Shh expression from DA neurons mediated by Cre activity expressed from the DA transporter locus (Dat-Cre; all mouse strains used in this study are referenced in Supplemental Experimental Procedures). Shh-nLZC/C/Dat-Cre mutant animals were born alive and mobile with expected Mendelian frequency and no overt structural or motor signs at the end of postnatal development compared to Shh-nLZC/+/Dat-Cre control littermates ( Figures S1 and S2; Table S1; Supplemental Results A and B; for all comparative analyses herein littermates double heterozygous for Shh-nLZC/+and Dat-Cre served as controls).

The SSD was tuned for each rat so that it would erroneously continue its movement (STOP-Failure) or successfully stop on approximately equal numbers of trials. During each test session, SSD was held constant to facilitate analysis of the electrophysiological data triggered on the GO and STOP cues. Rats received implants containing 21 individually drivable tetrodes (Gage et al., 2010). For the Immediate- and Deferred-GO tasks, tetrodes were targeted to right M1, STR, and GP. For rats trained on the Go/NoGo and Stop-signal tasks, the left

BG (STR, GP, STN, and SNr) were targeted. Ipsilateral prefrontal ECoGs were recorded with skull screws in contact with the brain (AP 4.5 mm, ML 1.5 mm relative to bregma). All signals were referenced to Gefitinib solubility dmso a skull screw on the midline 1 mm posterior to lambda (between cerebral cortex and cerebellum). We have found previously that this reference location is not itself associated with substantial beta power, that would produce artificially elevated beta coherence estimates between all pairs of forebrain locations (Berke, 2009). Analyses were performed using Matlab (Mathworks, Inc., Natick, MA). Gabor power spectrograms

were computed by convolving LFPs with Gaussian-tapered (50 ms standard deviation) complex sinusoids of integer frequencies from 1 to 100 Hz, and taking the logarithm of the squared magnitude of the resulting time-series. To generate Figures 1C and Figures

4C, the spectrograms for each recording session were averaged. To generate power comodulograms (Figures 2D and Figures S3B), Pearson’s correlation coefficient IWR-1 nmr was calculated between these same time series for each pair of recording sites. This resulted in a not 100 × 100 grid with each point having a value ranging from −1 (perfect anticorrelation of power at two frequencies) to +1 (perfect correlation of power at two frequencies). Only epochs during which the rat was engaged in the task (from initial nose poke to trial completion) were included. Power spectra (Figure 2C) were calculated for each trial, averaged across trials to give a mean spectrum at each recording site for each session, and smoothed with a three-point rectangular sliding window. To calculate coherence spectra, for each trial we calculated the cross-spectrum between each pair of recording sites. Session-wide coherence was then calculated as the squared magnitude of the averaged trial-by-trial cross spectra normalized by the product of the average autospectra (Figure 2E). See Figures 1D, Figures 3B, 3E; Figures 4D; Figures S2, S5. LFPs were zero-phase filtered between 15–25 Hz and the analytic signal was calculated using the Hilbert transform. The squared magnitude of the analytic signal is a continuous measure of beta power, and continuous beta phase was extracted as the argument of the analytic signal.

This trial showed that participants who undertook four months of treadmill training improved significantly learn more more than a no-intervention

control group on several outcomes: increased comfortable walking speed by 0.12 m/s, increased fast walking speed by 0.15 m/s and increased walking distance by 38 m. Although the participants all walked slower than normal at baseline (< 1.1 m/s), ambulatory levels were heterogeneous (mean walking speed 0.50 m/s, SD 0.26). This raises the possibility that the effect of treadmill training in this group of ambulatory stroke survivors may differ, based on their baseline walking speed. Walking speed has been shown to be associated with community ambulation and participation following stroke.7 and 8 There is evidence that people who walk very slowly (ie, gait speed ≤ 0.4 m/s) rarely venture outside their homes, while those who walk faster (ie, gait speed > 0.4 m/s) CHIR-99021 mw have some ability to ambulate around their community. Those who walk even faster (ie, gait speed > 0.8 m/s) are able to ambulate fully around their community.7 As the current study is a secondary analysis of the AMBULATE trial, investigating whether baseline walking speed in people with chronic stroke

has a differential effect on mobility outcomes following treadmill training, a cut-off of 0.4 m/s was used to subdivide participants from the AMBULATE trial

into faster versus slower walkers. Therefore, the specific research question for this study was: After stroke, does treadmill training to improve walking speed and distance have Dichloromethane dehalogenase a greater effect on community-dwelling people who walk faster than 0.4 m/s than those who walk more slowly? Data collected in the AMBULATE trial6 were used in this study. The AMBULATE trial was a three-arm randomised trial with concealed allocation, assessor blinding, and intention-to-treat analysis involving 102 people with stroke who could walk slowly, lived in the community and had ceased all formal rehabilitation. An experimental group undertook 30 minutes of treadmill and overground walking thrice per week for four months, a second experimental group undertook training for two months, while the control group had no intervention. At four months, the experimental group that had trained for four months walked further, faster and reported better health than those who received no training. However, this effect had disappeared by 12 months. The present study is a subgroup analysis of slow and fast walkers in the experimental group that trained for four months, and in the control group. Any differential effects of walking speed on the outcomes that demonstrated improvement in the primary analysis, ie, walking distance, walking speed (comfortable and fast) and health status were examined.

Trem2 recycling was impaired in beclin 1-deficient BV2 cells as was observed with CD36 ( Figure 3H). Taken together, these data suggest that beclin 1 has a function in phagocytic receptor recycling. While receptor recycling is regulated by numerous mechanisms, a recent study in C. elegans showed that clearance of apoptotic cell corpses by the phagocytic receptor CED-1 was dependent on receptor recycling via DAPT the retromer complex ( Chen et al., 2010). This conserved structure is responsible

for endosome-to-Golgi retrograde transport of membrane proteins and in mammals consists of the subunits Vps26, Vps29, Vps35, and sorting nexins ( Bonifacino and Hurley, 2008). To determine whether beclin 1 might regulate the retromer complex, we knocked down beclin 1 expression using a lentivirus encoding beclin 1 shRNA and subsequently investigated whether the retromer complex was affected by beclin 1 knockdown. Remarkably, beclin 1 knockdown resulted in a prominent reduction of the retromer complex ( Figures 4A and 4B). To test whether diminished retromer expression impairs CD36 recycling, we knocked down Vps35 levels in BV2 cells using lentivirus encoding

Vps35 shRNA ( Figure 4C) and analyzed CD36 recycling. Reducing Vps35 significantly impaired CD36 recycling ( Figure 4D). Furthermore, reducing Vps35 also resulted in a concomitant reduction in phagocytic efficiency ( Figure 4E). Because previous studies have demonstrated that retromer dysfunction can be reversed by enhancing Vps35 levels ( MacLeod et al., 2013), we rescued Vps35 levels in beclin 1-deficient CB-839 in vitro BV2 cells. Rescuing Vps35 resulted in enhanced CD36 recycling ADAMTS5 ( Figure 4G) and phagocytosis ( Figure 4H). Together these data suggest that beclin 1 regulates the retromer complex, which is required to maintain

phagocytic receptor recycling and phagocytosis. To further explore this connection, we first tested whether beclin 1 might regulate retromer via a direct interaction. This proved unlikely, as we were unable to coimmunoprecipitate beclin 1 and Vps35 (data not shown). Moreover, a recent study using mass spectrometry screens to identify putative binding partners of mammalian beclin 1 and other known autophagy proteins that complex with beclin 1 did not reveal any binding partners exclusive to the retromer complex (Behrends et al., 2010). Therefore, we next tested whether beclin 1 may have a role in the recruitment of retromer. Retromer is typically recruited to vesicles via its sorting nexin subunits. These subunits contain a Phox homology domain capable of binding to phosphatidylinositol 3-phosphate (PI3P) present on target membranes (Burda et al., 2002). Interestingly, phosphatidylinositol is converted to PI3P primarily by the PI3K, Vps34, which is known to form a complex with beclin 1 and regulate autophagy and apoptosis (Funderburk et al., 2010).

We found that the vmPFC was modulated by signals related to the subject’s own reward probability in the Other task. Whole-brain analysis during the Other task identified BOLD signals in several brain regions, including the vmPFC (p < 0.05, corrected; Figure 4A), that were significantly modulated by the subject's reward probability (for the stimulus chosen by the subject) at the time of decision (Table 1). The subject's reward probability is the decision variable closest to their choices, as it is the farthest downstream in the hypothesized computational processes for

generating their choices, but it is also based on simulating the other’s decision-making processes, in particular, the simulated-other’s reward probability (Figure S1A). To determine whether the activation of the vmPFC that was significantly modulated by the subject’s reward

probability was compounded by, or possibly rather due to, the simulated-other’s PDGFR inhibitor reward probability, we conducted two additional whole-brain analyses: when the simulated-other’s reward probability (for the stimulus chosen by the subject) was added to the regression analysis as a potential confounder and when the regressor variable of the subject’s Selleckchem VE 822 probability was first orthogonalized to the simulated-other’s reward probability and then included in the regression analysis together with the simulated-other’s reward probability. In both cases, vmPFC activation remained significantly modulated by the subject’s reward probability (p < 0.05, corrected). These results indicate that at the time of decision during the Other task, vmPFC activation was significantly modulated by the subject's reward probability. For comparison, the significant vmPFC signals related to the sRPE are also shown in Figure 4A. Here, we emphasize that the sRPE was not the subject's own reward prediction error (the difference

between the subject’s own outcome and his/her own reward probability) during the Other task. Indeed, no region was significantly activated by the subject’s own reward prediction error during the Other task. This observation was confirmed by an additional whole-brain analysis that was conducted in the same way as the original analysis, except that we added the regressor variable for the subject’s own reward prediction error and removed the regressors until for the sRPE and sAPE. Whole-brain analysis during the Control task revealed significant modulation of vmPFC activity (p < 0.05, corrected) by the reward probability (for the stimulus chosen by the subject) at the time of the decision and the reward prediction error at the time of the outcome (Figure 4B; Table 2). These activities remained significant (p < 0.05, corrected) when the following potential confounders were added to the analysis: the reward magnitude of the chosen stimulus with the reward probability and the value and reward probabilities of the chosen stimulus with the reward prediction error.

The vast majority of these patients were included in phase II trials. Based on these analyses, the authors were able to map the orchestration of immune cells in blood and tumor at baseline and during IL-2 based immunotherapy [8]. An understanding of IL-2 based immunotherapy as a “targeted therapy” requiring lymphocyte subsets for tumor rejection emerged from these analyses. In addition, an important understanding of the powerful negative impact of neutrophils also appeared. The analyses revealed that no blood lymphocyte subset was correlated with survival whereas high numbers of baseline blood neutrophils, on-treatment blood neutrophils and on-treatment

blood monocytes were correlated with short survival [8]. Low numbers of on-treatment ATM inhibitor blood neutrophils were correlated with response [8]. Evaluating intra-tumoral immune cells, high numbers of baseline CD57+ natural killer (NK) cells, baseline CD4+ T-cells, and on-treatment CD3+ T-cells were significantly correlated with favorable survival

[9] whereas baseline presence of intratumoral neutrophils was correlated with short survival [10]. Thus, neutrophils and monocytes/macrophages were “bad guys” and T cells and NK-cells were “good guys” for the outcome of IL-2 based immunotherapy [7]. However, it appeared selleck chemical that the “bad guys” had stronger prognostic impact than the “good guys”. Strikingly, in a randomized phase II trial of IL-2 alone versus IL-2 plus histamine [11], patients with high numbers of neutrophils

in peripheral blood at baseline (>6 × 109/L) and after 8 weeks of treatments (>4.57 × 109/L) had very poor survival, with apparently no impact of either IL-2-alone or IL-2 plus histamine treatment, as almost all patients with high blood neutrophils were dead within 2 years from commencement of already therapy [12]. Only patients with low numbers of blood neutrophils at baseline and during treatment achieved long-term survival. In another cohort of patients treated with low-dose IL-2 based immunotherapy, even lower level of blood neutrophils (≥2.19 × 109/L) at week 5 after commencement of therapy was associated with poor survival. Thus, patients with blood neutrophils ≥2.19 × 109/L had a median survival of 10.9 months whereas patients with blood neutrophils < 2.19 had a median survival of 25.1 months [8]. Based on a final multivariate analyses including baseline factors only, the authors pointed on five clinical features (performance status, bone metastases, lymph node metastases, low hemoglobin and high lactate dehydrogenase) and three supplemental immunological features (presence of intratumoral CD66+ neutrophils > 0, high blood neutrophils > 6.0 × 109/L and low intratumoral CD57+ NK cells < 50 cells/mm2) as independent prognostic factors of survival in patients with mRCC receiving IL-2 [10].

These findings yielded several new insights regarding the functional implications of the unique connectivity pattern of dendritic inhibition. Importantly, although these insights are based on the analytical

solution for the steady-state case and for passive dendrites ( Figures 1, 2, and S1–S3), they nevertheless explain simulated results ABT-888 chemical structure obtained for corresponding nonlinear and transient cases. In particular, we analyzed in detail the case of an MC-to-PC inhibitory connection in layer 5 of the neocortex ( Figures 5 and 6), whereby the MC’s inhibitory synapses contact the distal apical dendrites of the PC. Near the main apical branch of the PC, a powerful Ca2+ spike could be evoked; this spike interacts reciprocally with the soma to generate a burst of Na+ spikes Dabrafenib manufacturer at the soma (BAC firing; Larkum et al., 1999). Although the MC’s synapses are more distal than the Ca2+ spike initiation region, we showed that they do effectively dampen the Ca2+ spike (see Figure S12) and also electrically decouple the apical dendrite from the soma, as expected from our analysis of the corresponding passive

case. The effective spread of SL into the dendritic region surrounded by multiple inhibitory synapses ( Figures 4 and 5) leads to a spatially extended shunted dendritic domain beyond the anatomical domain demarcated by these synapses. This spatial spread of inhibitory shunt implies that in order to dampen excitatory and/or excitable dendritic currents, it is not necessary to match each excitatory synapse with a corresponding adjacent inhibitory synapse. Rather, by surrounding a dendritic region with a few inhibitory contacts,

it is possible to effectively dampen the excitatory and/or excitable current that would be generated in this region ( Figures 5 and 6) and thereby effectively control the neuron’s output. This may explain why in the neocortex and the hippocampus, only ∼20% of the synapses are inhibitory ( DeFelipe Adenosine and Fariñas, 1992; Megías et al., 2001; Merchán-Pérez et al., 2009). Due to the extended centripetal spread of the inhibitory shunt, different functional dendritic domains may interact with each other and be formed dynamically by recruiting and/or omitting various combinations of inhibitory synapses at strategic loci. For example, when each of the group of five inhibitory synapses in Figure 4A is individually active, then the functional dendritic subdomain corresponding to each inhibitory subgroup is spatially restricted. However, when all three inhibitory groups of synapses are active together, as in Figure 4A, then the functional dendritic domain that is shunted by the 15 inhibitory synapses expands dramatically, effectively controlling the excitatory and/or excitable charge (output) from a large portion of the postsynaptic dendritic tree.

Since cattle are its principal intermediate host ( Dubey et al., 1996), the parasite causes important economic losses in the cattle industry ( Anderson et al., 2000). Serological methods are commonly used in epidemiological

studies of N. caninum in animals. Methods include the indirect fluorescent antibody test (IFAT) ( Dubey et al., 1988), direct agglutination, Western blotting, and enzyme-linked immunosorbent assays (ELISAs) ( Dubey and Schares, 2006). In IFAT, which is considered the reference technique for N. caninum serology ( Dubey and Schares, 2006), intact tachyzoites are used as antigens. The test detects antibodies directed against antigens present on the cell surface of the parasite ( Bjorkman and Uggla, 1999), although reactivity with other coccidian parasites might be possible i.e. Toxoplasma CH5424802 in vivo gondii ( Dubey et al., 2003). Also, the assay involves subjectiveness in the scoring of results ( Pare et al., 1995), is labor-intensive, and does not lend itself

to large-scale investigations. ELISAs, in contrast, are feasible at larger scales and do not involve subjective interpretation. Single antigens, particularly the parasite’s surface proteins, as an alternative for tachyzoite extracts, Bcl-2 inhibitor are suitable candidates for the development of more specifics tests. A number of dense-granule antigens of N. caninum have been identified to date, including NcGRA1, NcGRA2, NcGRA6, NcGRA7 (NCDG1/Nc-p33), NcPI-S, and NTPase ( Howe and Sibley, 1999 and Morris et al., 2004), some of which have been tested for neosporosis diagnosis. NcGRA2, NcGRA6, and NcGRA7 have been prepared as recombinant antigens to be used in ELISA for diagnosing neosporosis in cattle and canine population

( Lally et al., 1996 and Liu et al., 2007). The surface protein NcSRS2 is common to both tachyzoite and bradyzoite stages and is of potential use for the serological during diagnosis of N. caninum infection ( Ahn et al., 2003, Gaturaga et al., 2005, Ghalmi et al., 2009, Hemphill and Gottstein, 1996 and Nishikawa et al., 2001). The available ELISAs lack in validation data, and some of these tests are only partially published ( Dubey and Schares, 2006). The purpose of the present study was to standardize an indirect ELISA which uses the C-terminal domain of protein NcSRS2 to diagnose N. caninum in cattle, and compare its performance with the IFAT. N. caninum, strain NC-1 ( Dubey et al., 1988) was used to prepare an antigen formulation for IFAT. The parasites were propagated in Vero cells maintained in Dulbecco’s modified essential medium (DMEM) supplemented with 10% fetal calf serum (FCS), at 37 °C with 5% CO2. When 80% of the Vero cells that had been infected with N. caninum tachyzoites shows CPE (citopatic effect), the cell monolayers were removed by scraping, twice washed with phosphate-buffered saline (PBS) solution, and then centrifuged at 1000 × g for 10 min.

2 s, followed by a cross-hair for 3 s. Ten such trials were presented in each block and a single run consisted of two blocks each of the Motion and Static stimuli. Pediatric participants underwent a training session in a mock scanner prior to the experiment to familiarize them with the MRI environment and all subjects practiced the tasks prior to the scan. Data were acquired using a 3T Siemens Trio scanner located in the Center for Functional and Molecular Imaging at the Georgetown University Medical Center,

Washington, DC. For each run, 89 functional images consisting find more of 50 contiguous whole-brain axial slices were acquired using an echo-planar imaging (EPI) sequence and the following parameters: TR = 3 s, TE = 30 ms, flip angle = 90°, FOV = 192 mm, slice thickness = 2.8 mm (0.2 mm interslice gap), in-plane resolution = 64 × 64, and voxel size = 3 mm isotropic. SPM8 was

used in analysis of functional MRI data sets. The first five scans of each run were discarded to account for T1 saturation effects. Resulting data sets were realigned to the mean of the remaining images, normalized to the Montreal Neurological Institute EPI template, resampled to an isotropic voxel size of 2 mm3, and smoothed with a Gaussian kernel of 8 mm full-width at half-maximum. Statistical analysis was performed based on the general linear model. Functional data sets were high-pass filtered with a cut-off of 128 s to account for signal drift and corrected AZD8055 for autocorrelations using an AR(1)

model. Stimulus onsets were modeled using the SPM canonical hemodynamic response function, and within-subject parametric maps were created for the motion-specific contrast (Motion > Static). Area V5/MT was functionally identified via its responsivity to the visual motion stimulus. In Experiment 1, V5/MT was identified individually in each subject via the contrast of Motion versus Static. For this single-subject analysis, Casein kinase 1 we searched for clusters within Talairach coordinates bounded by previously defined anatomical volumes: x = lateral to ±35; y = posterior to −60; z = −9 to +13 (Dumoulin et al., 2000; Tootell et al., 1995; Watson et al., 1993). To avoid circularity, we performed this identification of V5/MT using half the data acquired, while the other half was utilized in percent signal change calculation. Allocation of task blocks for this split between the two halves of the run was randomized across subjects. Data from Experiment 1 were also used to determine the ROI used in Experiments 2 and 3, however, this time using a different analysis, since Experiment 1 involved a different group of subjects than those participating in Experiments 2 and 3. An independent ROI was identified via a second level random-effects whole-brain analysis (no anatomical boundaries or masks were used here) performed using a one-sample t test to combine activation for the motion specific contrast over all the subjects in Experiment 1.

Behavioral experiments were performed in a custom-built, fully automated apparatus (Claridge-Chang et al., 2009) at 32°C unless stated otherwise

(see the Supplemental Experimental Procedures). Data were analyzed in MATLAB 2009b (MathWorks), SigmaPlot 12.5 (Systat Software), and Prism 6 (GraphPad). ePN or iPN projections to the LH were imaged by two-photon laser scanning microscopy (Ng et al., 2002 and Wang et al., 2003). Cuticle and trachea in a window overlying the LH were removed, and the exposed brain was superfused with carbogenated solution (95% O2 and 5% CO2) containing 103 mM NaCl, 3 mM KCl, 5 mM trehalose, 10 mM glucose, 26 mM NaHCO3, 1 mM NaH2PO4, 3 mM CaCl2, 4 mM MgCl2, and 5 mM N-Tris (TES) (pH 7.3). Odors at 10−2 dilution were delivered by switching mass-flow-controlled carrier and stimulus streams (CMOSens performance line, Sensirion) via software-controlled solenoid valves (the Lee Company). AZD5363 in vitro Flow rates at the exit port of the odor tube were 0.5 l per min. Basal plasma membrane fluorescence of ePNs expressing spH was used

to target a suction electrode to the mALT. Spikes were elicited with 1 ms pulses of current (10–30 μA) with a DS3 stimulus isolator (Digitimer). For thermal stimulation of iPNs expressing dTRPA1, the superfusion solution was heated with a closed-loop TC-10 temperature controller (NPI) with a HPT-2 in-line heater (ALA). Temperature shifts from 25°C to 32°C were complete in <1 min. Fixed samples expressing fluorescent proteins and/or stained with

fluorescently labeled antibodies were imaged on a Leica TCS SP5 confocal microscope (see the Supplemental Experimental Procedures). Pfizer Licensed Compound Library We thank Alexei Bygrave and Ruth Brain for generating QUAS-spH flies; Liqun Luo for communicating unpublished results; Bassem Hassan, Kei Ito, Toshi Kitamoto, Tzumin Lee, David Owald, Joachim Urban, Jing Wang, the Bloomington Stock Center, the Vienna Drosophila RNAi Center, and the Kyoto Drosophila Genetic Resource Center for fly strains; and Loren Looger for GCaMP3. This work was supported by grants (to G.M.) from the Wellcome Trust, the Gatsby Charitable Foundation, the Medical Research Council, the National Institutes of Health, and the Oxford Martin 3-mercaptopyruvate sulfurtransferase School. M.P. received postdoctoral fellowships from the European Molecular Biology Organization and the Edmond and Lily Safra Center for Brain Sciences. A.C.L. was a Sir Henry Wellcome Postdoctoral Fellow. M.P. and G.M. conceived and designed the study; M.P. performed and analyzed all experiments; and M.P., A.C.L., and G.M. interpreted the results and wrote the paper. A.C.L. provided fly strains and image analysis scripts. W.H. performed structural imaging. “
“Understanding how nervous systems represent sensory cues, store memories, and support decision making and appropriate action selection is of major interest. Olfactory learning in Drosophila is ideally suited to address these questions.