For interactions in the beta band, these are located in dorsolateral prefrontal, lateral parietal, and temporal cortex (Figure 2E). In contrast, theta-band interactions involve major hubs in the medial temporal lobe, and gamma-band hubs can be observed in sensorimotor cortex (Hipp et al., 2012). An important finding is that coupling, as revealed by envelope correlations, can dissociate from the spatial distribution of local signal power. Another MEG study employing

a related approach has provided similar results (Brookes et al., 2012). A recent study of phase ICMs employing the phase lag index has revealed somewhat different patterns of highly connected regions that differ across frequency bands (Hillebrand et al., www.selleckchem.com/products/VX-770.html 2012). In the alpha band, the most strongly connected regions were visual and posterior cingulate cortex. In the beta band, this involved sensorimotor and parietal cortex, and in the gamma band, temporal and parietal areas showed high functional connectivity. Phase ICMs have also been mapped in a recent study that focused on coupling in the dorsal attention network (Marzetti et al., 2013). Significant delta- and alpha-band interactions were observed between homologous regions of the attention network in the left and right hemisphere. Moreover, this network showed coupling in the alpha band to visual regions, as well as beta-band interactions with sensorimotor regions. Taken Epigenetics inhibitor together, these

studies seem to provide evidence that phase ICMs can dissociate from also envelope ICMs, but further studies will be required to elaborate this in greater

detail. An important question is to what extent the neurophysiological signatures of ICMs match their MRI-based characterization. The relation between LFP and BOLD signals has been the subject of a number of studies. BOLD fluctuations seem to correlate best with the slow power envelope fluctuations observed for LFPs and MEG or EEG signals (Logothetis et al., 2001, Leopold et al., 2003, Nir et al., 2007 and He et al., 2008). In particular, this holds for the gamma band, but lower frequencies have also been found to be related to the BOLD signal (He et al., 2008, Magri et al., 2012 and Keller et al., 2013). This is supported by studies that have employed direct coregistration of ongoing EEG or LFPs with BOLD activity (Shmuel and Leopold, 2008, Schölvinck et al., 2010 and Tagliazucchi et al., 2012a). It has been suggested that slow changes in both BOLD signal and power envelopes of oscillatory signals, may reflect endogenous fluctuations of neuronal excitability, which occur in a coupled manner across different cortical and subcortical regions (Leopold et al., 2003 and Deco and Corbetta, 2011). Taken together, these studies provide evidence that BOLD coupling analyses primarily reveal envelope ICMs, thus converging with neurophysiological analyses of envelope correlations.

Finally, we examined the function of Sema-2a and Sema-2b in PNs that normally target dendrites to the ventromedial antennal lobe. We focused on VM2 PNs, the only ventromedial-targeting PN classes we can label with a specific GAL4 driver (NP5103) ( Komiyama et al., 2007). In sema-2a−/− GDC 0449 or sema-2b−/− single mutants, VM2 PN targeting appeared normal compared to controls ( Figures 7A–7C). However, in sema-2a−/− sema-2b−/− double mutant flies, VM2 PNs exhibited significant dorsolateral mistargeting ( Figures 7D, quantified in Figure 7E and Figure S6A). These experiments indicate that Sema-2a and Sema-2b also act redundantly to direct

ventromedial-targeting PN dendrites to their normal positions. Next, we attempted to determine the cellular source for this additional function of Sema-2a and Sema-2b. Because we needed to use the GAL4/UAS system to label VM2 PNs, we could not use GAL4/UAS again to ablate larval ORNs or perform tissue specific knockdown and rescue as we did for MZ19+ PNs. However, since PNs themselves made a significant contribution to Sema-2a expression (Figures 4E and 4F), and because ventromedial-targeting PNs should express high levels

of Sema-2a and Sema-2b given their distribution patterns (Figure 2), Raf activation we tested whether PN-derived Sema-2a and Sema-2b contribute to VM2 dendrite targeting. We used NP5103-GAL4 based MARCM to label anterodorsal neuroblast clones from which VM2 PNs are derived ( Jefferis et al., 2001).

When we induced MARCM neuroblast clones in early larvae such that Sema-2a and Sema-2b were eliminated from all larval-born PNs in the anterodorsal lineage, including VM2 PNs ( Figure 7F, left), VM2 PN dendrites exhibited significant dorsolateral mistargeting ( Figure 7H, compared with Figure 7G; quantified in Figure 7J and Figure S6B). In contrast, dorsolateral-targeting DL1 PN dendrites were unaffected by removal of Sema-2a/2b from this same neuroblast lineage ( Figure S7). These experiments indicate that Sema-2a/2b derived from PNs are essential for VM2 dendrite targeting but not for DL1 dendrite targeting. PN-derived Sema-2a and Sema-2b can affect VM2 dendrite targeting through two mechanisms. First, they could act cell-autonomously, for example ADP ribosylation factor by modifying the cell surface presentation of a targeting receptor. Second, they could act cell-nonautonomously as ligands to mediate dendrite-dendrite interactions among PNs. To distinguish between these two possibilities, we took advantage of the fact that VM2 PNs are produced late in the anterodorsal lineage, and induced smaller MARCM neuroblast clones that contained VM2 PNs but few other PNs within the same lineage (Figure 7F, right). We found that VM2 dendrite targeting in these smaller sema-2a−/− sema-2b−/− neuroblast clones was largely normal ( Figure 7I, quantified in Figure 7J). Thus, Sema-2a/2b act nonautonomously for VM2 dendrite targeting.

, 2011, Joesch et al., 2010 and Rister et al., 2007). Silencing L4 neurons also decreased full-field optomotor responses at low contrasts and very fast stimulus speeds and impaired the ability of flies to track rapidly oscillating patterns (Figure S7A). In contrast to L2 and L4, we found that the columnar, centrifugal neurons C2 and C3 play an important role in shaping behavioral responses to regressive motion stimuli. C2 and C3 are GABAergic neurons (Fei et al., 2010 and Kolodziejczyk et al., 2008) that arborize in multiple layers of the proximal and distal

medulla and send axons into the lamina, where they are primarily presynaptic on several neuron types, including L1, L2, and

Lai neurons (Meinertzhagen and O’Neil, INK1197 clinical trial 1991 and Rivera-Alba et al., 2011). In the distal medulla, C2 and C3 both receive presynaptic input from L1 and form synapses on L2; C2 is also presynaptic to L1 (Takemura et al., 2008). In addition to the distal medulla, C3 neurons arborize in the proximal medulla, primarily in layer M9 (Figures 1C and 2H). Examination of the C3 terminals in the medulla revealed that putative dendritic arbors in layer M9 showed a stereotyped orientation, with processes extending posteriorly from the branch Cisplatin cell line point off the main axon (Figures 5C and 5D). This directionality was highly stereotyped (33/33 neurons

from 3 brains). Closer examination revealed that these arbors extend into neighboring columns (Figure 5E), reminiscent of the the multicolumnar projections of L4 in lamina (Figure 5B; Strausfeld and Campos-Ortega, 1973) and medulla (Takemura et al., 2011). This organization suggests that C3 neurons receive synaptic input from posterior medulla columns and provide output to more anterior lamina and medulla columns. Such an asymmetric circuit could enhance the detection of regressive motion by amplifying signals translating from posterior to anterior across the eye. Consistent with this hypothesis, we found that silencing C3 neurons abolished steering responses to regressive motion stimuli moving at high speeds (Figure 5K, bottom row) but did not affect responses to progressive motion (Figure 5K, top row) or basic optomotor stimuli (Figure S7C). C2 neurons also had multicolumnar, presumably dendritic, arborizations in the medulla (Figures 5F–5H). Most of the C2 arbors in layer M10, while variable in their detailed shapes, were strongly asymmetric (18/20 neurons from 19 brains), extending preferentially in a dorsal direction relative to the main neurite (Figures 5G, 5H, and S3D). This multicolumnar profile of C2 neurons suggests that they may also be involved in integrating signals from neighboring columns.

49 and 0.50 for the two noncontour trials. This means no response

amplitude difference between the circle and background pixels in the noncontour condition. Based on this, we defined figure-ground measure for single trials (FG trials): Pc-Pb, i.e., subtracting the population response of the background (Pb) from the population response of the circle (Pc) in each contour and noncontour single trial. Figure 4E shows the distribution histograms of the FG trials for all contour and noncontour trials in a typical recording session. The distribution histogram shows a significant difference between the contour and noncontour trials (p < 0.001; Mann-Whitney Ribociclib manufacturer U test). Figure 4F shows the ROC analysis and the AUC is 0.92, indicating a high separation between single trials belonging to the contour and noncontour condition based on FG trials. This AUC value was much higher than the shuffled AUC that was calculated from 100 iterations of randomly shuffled contour and noncontour trials (AUC, 0.5 ± 0.11, mean ± 3 × SD; Figure 4F, dashed gray lines). We then performed an ROC analysis on the FG trials, for each recording

session and found the AUC to be 0.92 ± 0.014 (mean ± SEM; n = 15 recording sessions; significantly different from 0.5, p < 0.001) and 0.81 ± 0.023 (mean ± SEM; n = 10 recording sessions; until significantly different from 0.5, p < 0.01) for monkeys L and S, respectively. In contrast to the late phase, the Alisertib AUC in the early phase was much smaller: 0.63 ± 0.035 and 0.63 ± 0.017 for monkeys L and S, respectively. Our results indicate that the response difference between the circle and background area, only in the late phase, can be useful for making a behavioral decision at the single-trial level. Finally, we wanted to study

the relation between the population response, contour saliency, and the perceptual report. For this purpose, the monkeys performed a contour-detection task when presented with contours at various saliency levels. We varied the contour saliency by increasing the orientation jitter of the contour elements (see Experimental Procedures; Figure 5A). For each orientation jitter, we measured the behavioral and neuronal responses, i.e., the contour-detection probability and the population response (see Experimental Procedures). Next, the psychometric curve was computed (the contour-detection probability for each orientation jitter) and the results were fitted with a Weibull function (Figures 5B and S4A). Both monkeys showed similar normalized psychometric curves where, as expected, increasing the orientation jitter (decreasing the saliency of the contour) decreased the probability of contour detection.

While XPORT is required for both TRP and Rh1, TRP and Rh1 expression are not dependent upon one another. TRP protein levels were wild-type in the ninaEI17 (Rh1) null mutant ( Figure 1B) and Rh1 protein levels were wild-type in the trp343 null mutant ( Figure 1C). Therefore, XPORT provides a biosynthetic link between TRP C59 wnt and its GPCR, Rh1. The xport

locus is comprised of 2 exons and 1 intron ( Figure S2A). The 953 base pair transcript encodes a 116 amino acid protein ( Figures 2A and S2A) that was detected as a 14kD band in wild-type flies ( Figure 2B). Consistent with the presence of a premature stop codon, XPORT protein was reduced in xport1 heterozygotes and completely absent in xport1 homozygotes as well as in flies harboring the xport1 allele in trans to Df(3R)BSC636 ( Figure 2B). XPORT expression

was completely restored in the rescue line ( Figure 2B). Although TRP and Rh1 require XPORT protein for their expression, XPORT is expressed normally in both the trp and ninaE (Rh1) null mutants ( Figure 2B). The XPORT protein is predicted to be a Type II transmembrane protein with a single C-terminal transmembrane domain click here and a cytosolic N-terminal globular domain (Figures 2A, S2A, and S2B). Consistent with this prediction, following centrifugation of a total cell homogenate from wild-type heads, XPORT was absent from the soluble fraction and exclusively present in the membrane pellet (Figure 2C). XPORT was solubilized by suspension of the membrane pellet in SDS. Following subsequent centrifugation, XPORT was detected entirely in the

supernatant and was absent from the pellet, confirming that XPORT is an integral membrane protein. XPORT is highly conserved Amisulpride among 12 Drosophila species as well as among other Diptera, including two mosquito genera, Anopheles and Culex. Drosophila XPORT is also conserved in the Jerdon’s jumping ant and honeybee (Hymenoptera) as well as in the red flour beetle (Coleoptera) ( Figure S2C). While XPORT is highly conserved among insect species, there are currently no vertebrate counterparts in the NCBI database. Although XPORT lacks an obvious vertebrate homolog, it has a small recognizable motif that displays 46% amino acid identity and 62% similarity with the KH domain of a DnaJ-like protein from Chlamydomonas reinhardtii ( Figure 2A). DnaJ proteins, also known as Hsp40s, are members of a large family of highly diverse cochaperones that bind Hsp70 via a 70 amino acid J-domain, and assist in the folding and quality control of a vast array of client proteins ( Kampinga and Craig, 2010). While DnaJ and DnaJ-like chaperones are defined by the presence of the J-domain, XPORT lacks this domain. The KH domain is a nucleic acid recognition motif that binds single-stranded RNA or DNA with low affinity. KH domains contain a “GXXG” loop that is key to nucleotide binding and this motif is also present in XPORT (Figure 2A).

, 2010; Hamdan et al., 2010; Horn et al., 2010; O’Roak et al., 2011). We further examined FOXP2 targets in human neuronal cell lines previously shown to exhibit patterns of gene expression similar to those of forebrain

neurons (Konopka et al., 2012). We manipulated FOXP2 expression during the normal 4 week period of differentiation of these human cells by either forcing expression of FOXP2 or knocking down expression of FOXP2 using RNA interference (Supplemental Experimental Procedures). Using Selleck Dasatinib Illumina microarrays, we identified over 600 target genes with expression going in the opposite direction with FOXP2 forced expression compared to FOXP2 knockdown (Figure S4). Upon comparing this list of experimentally identified FOXP2 targets in human neural progenitors using microarrays with the genes in the olivedrab2 module identified by DGE, we found a significant overlap (13 overlapping genes, p = 4.0 × 10−4; Figure 6D). Interestingly, nine FOXP2 target genes overlap with hDE genes in

this module (Figure 6D). Strikingly, the FOXP2 learn more targets in the olivedrab2 module are enriched for genes involved in neuron projections, synapse, and axonogenesis. These data fit with work showing modulation of neurite outgrowth in mouse models of Foxp2 ( Enard et al., 2009; Vernes et al., 2011). Thus, while regulation of neurite outgrowth by FOXP2 may be a conserved mammalian function of FOXP2, the contribution of human FOXP2 to modulation of this critical neuronal process may be enhanced as evidenced by increased neurite length in humanized

Foxp2 mice ( Enard et al., 2009). Together, these data identify a human-specific FP gene coexpression network that is enriched in both genes involved in neurite outgrowth, binding sites for a differentially expressed splicing factor on the human lineage, and genes regulated by FOXP2. Since the sequencing of the human genome, a major goal of evolutionary neuroscience has been to identify human-specific patterns of gene expression and regulation in the brain. While several studies isothipendyl have addressed gene expression in primate brain (Babbitt et al., 2010; Brawand et al., 2011; Cáceres et al., 2003; Enard et al., 2002a; Khaitovich et al., 2004a, 2005; Liu et al., 2011; Marvanová et al., 2003; Somel et al., 2009, 2011; Uddin et al., 2004; Xu et al., 2010a), our study ascertains human-specific patterns using multiple platforms, multiple brain regions, and sufficient sample sizes in multiple species. Moreover, our study identifies human-specific gene coexpression networks with the inclusion of an outgroup. By including these data, we find that gene coexpression or connectivity has rapidly evolved in the neocortex of the human brain. In addition, the genes with changing patterns of connectivity are important for neuronal process formation, the structures that underlie neuronal functional activity and plasticity.

To investigate the contribution of MeCP2 S421 phosphorylation in cortical circuit formation, the authors examined dendritic morphology of cortical neurons both in vitro and

in vivo from MeCP2 S421A mutant animals. Mutant cortical neurons exhibited significantly more dendritic branches and notably, this increase in dendritic complexity was found only in the apical dendritic tufts of pyramidal neurons. This finding however, differs from the reduced spine number and dendritic complexity reported in studies of MeCP2 KO null and RTT patients (Na and Monteggia, 2011). Previous work in MeCP2 null mice showed reduced cortical activity due to a shift in the balance between excitation and inhibition in layer 5 pyramidal neurons. Specifically, reduced circuit excitability learn more was accompanied by both reduced spontaneous excitatory synaptic input and increased inhibition,

however the molecular mechanisms which underlie this shift remain largely unknown (Dani et al., 2005). What then, are the neurophysiological consequences of activity-dependent MeCP2 S421 phosphorylation and does this modification influence normal synaptic function and behavior? To address this issue, Cohen et al. (2011) analyzed spontaneous miniature inhibitory postsynaptic currents (mIPSCs) and spontaneous miniature excitatory postsynaptic currents (mEPSCs) in whole-cell recordings from layer II/III pyramidal neurons from MeCP2 S421A mutant animals and control littermates. They observed a modest increase in the amplitude of mIPSCs but no difference in either the amplitude or frequency BMS 354825 of mEPSCs. Noting that a hallmark of the early stages of RTT is decreased social function, the authors next examined the behavioral responses of animals in which activity-dependent MeCP2 S421 phosphorylation was abolished. Unlike animals with complete loss of of function of MeCP2, MeCP2 S421A animals do not exhibit abnormalities in social interaction, motor coordination, spatial learning, or memory paradigms, but they are unable to distinguish

between novel and familiar stimuli. These findings demonstrate a role for activity-dependent phosphorylation of MeCP2 S421 in highly specific and subtle aspects of cortical neuronal morphology, synaptic function, and behaviors. Adrian Bird and colleagues have challenged the view that MeCP2 functions as a gene-specific transcriptional repressor (Skene et al., 2010). Using a newly developed biochemical fractionation technique, they reported that MeCP2 protein is almost as abundant as the number of histone octamers. They employed bisulfite sequencing and MeCP2 chromatin-immunoprecipitation assays followed by high throughput sequencing (ChIP-Seq) of mouse brain nuclei extract and discovered that MeCP2 is globally distributed across the entire mouse genome and this distribution tracks the density of methyl-CpGs.

We ask whether synchronized firing conveys information on odor

identity (“What is the odor?”), or alternatively, value (“Is it rewarded?”). In addition, noradrenergic (NA) modulation is known to play a role in new olfactory stimulus/reward association (Bouret and Sara, 2004 and Doucette et al., 2007), and we ask whether NA antagonist application in the OB affects synchronized spike odor responses of SMCs to rewarded and unrewarded odors in the go-no go behavioral task. We find that responses of synchronized SMC spikes to odors convey information on odor value (or Selleckchem Vismodegib a related reward signal), and that the differential synchronized spike response to rewarded and unrewarded odor is not as robust in the presence of inhibitors of NA modulation of the OB. Thus, the olfactory system stands out from other sensory systems in that information on stimulus value

is found in the MC that is one synapse away from the sensory neuron, in the same place in the circuit as would be a bipolar cell in the visual system or a spiral ganglion cell in the auditory system. Mice were implanted with two eight-microelectrode arrays targeted to the MC layer (Figure 1A). During each trial in the go-no go task, thirsty mice were asked to respond to a rewarded odor by licking a tube, and they received a water reward if they licked at least once in the last four 0.5 s periods of the trial (the Resminostat response area [RA]; see Figure 1Bi; no reward for the unrewarded odor). The sniffing behavior of animals during this task is illustrated Pazopanib in Figure 1Bii. Consistent with previous reports (Wesson et al., 2008), animals showed an increase in sniffing frequency in anticipation of odor presentation. Sniffing frequency started differing between successful rewarded and unrewarded odor trials at ∼1.7 s

in the middle of the decision-making period, when mice steadily reduced their breathing rates to a final frequency of 2–3 Hz after the water reward. Figure 1C shows an example of how a mouse learns to respond in a session wherein the animal is presented with a new pair of odors. Mice stop responding to the unrewarded odor because the licking entails considerable effort that is not rewarded with water. Mice learned to respond reliably (more than 80% correct) within 3–6 blocks of 20 trials (10 rewarded and 10 unrewarded) (Slotnick and Restrepo, 2005). We recorded from 345 single units and 820 multiunits in the MC layer of eight animals in 67 separate sessions (39 first day and 28 reversals). In recordings from mice performing odor discrimination, we find precise synchronization between a subset of spikes (Figure 2). Figure 2A shows precise spiking for three SMCs, and Figures 2B1 and 2B2 show the histograms of interspike lags.

In our experiments, the time to peak of the

CFCT was not significantly slowed by DHPG potentiation (increased delay to peak after DHPG: 0.94 ± 3.0 ms [±SD] in spines, 2.2 ± 4.4 ms [±SD] in spiny branchlets and AZD6244 nmr 2.1 ± 1.5 ms [±SD] in smooth dendrites, n = 5 cells, p > 0.05) (Figure 2D), contrary to what has been observed to date for the slow secondary release of calcium from IP3-sensitive calcium stores (Finch and Augustine, 1998, Sarkisov and Wang, 2008 and Takechi et al., 1998). Slices were preincubated with 25 μM cyclopiazonic acid (CPA), to empty the internal stores. In these conditions, DHPG strikingly potentiated the CFCTs by evoking unitary transients that were recruited in a voltage-dependent manner, as in control (n = 11 out of 11) (Figure 4E). Hence calcium stores, if recruited, act downstream of spike unlocking by mGluR1 activation. The mean amplitude

of the unitary transients was reduced to 0.08 ± 0.01 ΔG/R (65% of control, n = 11; p = 0.008) and the total amplitude of the CFCT was reduced to 0.19 ± 0.02 ΔG/R (73% of control, n = 11; p = 0.068). Participation of IP3-dependent calcium stores in submillisecond calcium learn more release (unitary transients) is unexpected, as all store release events described in Purkinje cells have an onset time course of several milliseconds (Finch and Augustine, 1998 and Takechi et al., 1998) even when paired with CF stimulation (Sarkisov and Wang, 2008). Alternatively, nonspecific effects, as attested by significant slice swelling during CPA application, may explain the reduction in spike-associated calcium influx. Overall, our data demonstrate that unitary transients mediated by dendritic P/Q spike are the primary contributors to voltage-dependent CFCT potentiation by mGluR1

activation. The onset of control CFCTs and of the first unitary transients in DHPG (both recorded during 40 Hz spontaneous Purkinje cells firing) were fitted by a logistic function (Figure 5A), yielding an exponential steepness factor. On average, unitary transients observed in the presence of DHPG rose faster (0.19 ± 0.01 ms, exponential steepness factor of the logistic fit, n = 17) than control CFCTs (0.45 ± 0.03 ms, n = 46) (p < 0.001). However, about 25% of the control CFCTs Astemizole rose as fast as unitary transients (gray circles, Figure 5B). Strikingly, the relationship between amplitude and rise kinetics (the exponential steepness factor) were opposite in control CFCTs and unitary transients (Figure 5C). The rise kinetics of control CFCTs were negatively correlated with their amplitude (slope = −0.098, r = −0.53, p < 0.0001, n = 45), as expected from the activation of T-type channels by increasingly temporally filtered electrotonic depolarizations due to cable effects. In contrast, the amplitude of unitary transients was proportional to their rise kinetics (slope = 0.56, r = 0.67, p = 0.003, n = 17), indicating that unitary transients result from regenerative events of similar peak calcium flux but variable duration.

Chronic two-photon imaging through a microprism combines the optical access of ex vivo brain slice preparations with in vivo behavioral context. This procedure involves insertion of a microprism attached to a cranial window (Figure 1A and Figure S1 available online). The hypotenuse of the microprism is coated with aluminum and thus serves as a right-angled mirror or “microperiscope,” with a vertical field-of-view parallel to the prism face. In different experiments, we implanted a microprism into Decitabine supplier either mouse somatosensory barrel cortex or visual cortex. As described in detail below (see Experimental Procedures and Figures S1A–S1D), a microprism (barrel cortex: 1.5 × 1.5 mm2 imaging face; visual

cortex: 1 × 1 mm2 imaging face) was glued to a coverslip. A craniotomy and durotomy were performed under sterile conditions, a small incision was made orthogonal to the cortical surface, and the microprism assembly was carefully inserted into Panobinostat in vivo cortex. Wide-field epifluorescence and two-photon images parallel to the cortical surface showed a vertical field-of-view across cortical layers 2–6 through the prism, revealing radial blood vessels and the expected laminar pattern of GCaMP3 expression (Figures 1B and 1C) or YFP expression (Figure 1D). The procedure for microprism insertion in the primary

visual cortex (V1) (Figures 1B and 1C) involved an ∼20% vertical compression of cortex (to ∼675 μm in area V1) to decrease brain motion and prevent dural regrowth at the cortical surface, as in previous studies (Andermann et al., 2011 and Dombeck et al., 2007). We first used microprisms for chronic two-photon structural imaging of genetically labeled cortical neurons across the depth of cortex. Somata and dendrites of layer 5 neurons in barrel cortex of anesthetized Thy1-YFP-H mice were imaged immediately following and for up to 2 months after prism insertion (n = 5; Figure 1D). Large field-of-view imaging with a 4× objective immediately

following prism insertion revealed labeled neurons in layers 2/3 and 5 (Figure 1D, left panel). Consistent with our earlier studies (Chia and Levene, 2009b), images included dendrites next of hundreds of neurons up to depths ∼900 μm below the pial surface. Imaging with a 40× objective, 29 and 68 days following prism insertion, yielded progressively clearer images, allowing visualization of fine structural details in proximal portions of layer 5 pyramidal neuron basal dendrites (Figure 1D, middle and right panels; Movie S1). The population of labeled neurons was stable over time, as demonstrated by tracking of over 40 neurons in one field-of-view across imaging sessions spaced 13 days apart (Figures S1E and S1F). We found that when the surface of the cortex around the prism was unobstructed, the fluorescence collection efficiency was improved and “shadow” effects of radial vessels located between the image plane and the prism face were reduced.