, 2007). We found that NDR1-CA M166A used Benzyl-ATP-γ-S; however, the ATP analog usage was reduced (Figure S4B). To rescue NDR1 kinase activity we, mutated two residues known to be suppressor

mutations that can rescue kinase activity when the gatekeeper residue is mutated (Zhang et al., 2005) and obtained NDR1-as-CA with increased kinase activity (NDR1-CA with M166A, M152L, and S229A mutations; Figures 1D, 1E, and 5B). We used this kinase (NDR1-as-CA) in subsequent substrate identification experiments. To perform labeling reactions in which NDR1-as-CA would thiophosphorylate substrates with selleck chemical Benzyl-ATP-γ-S, we reacted 10 μg of purified kinase with 1 mg brain lysate protein. Labeled lysate was treated by covalent capture for substrate identification (Blethrow et al., 2008 and Hertz et al., 2010). Briefly, labeled protein lysate is digested by trypsin and then thiol-containing peptides (including thiophosphorylated substrates and cysteine-containing peptides) are captured by thiol reactive resin, whereas non-thiol-containing

peptides are washed away. In the third step, beads are treated with Oxone to oxidize sulfur and elute phosphopeptides by spontaneous hydrolysis of thiophosphate linkage, whereas cysteine-containing CFTR modulator peptides remain attached to the beads by thioether bonds. Finally, the eluted peptides are analyzed by liquid chromatography/tandem mass spectrometry to identify not only the substrates but also the phosphorylation sites, which is a major advantage of the method (Figure 5D). In each experiment, we included two negative controls (lysate Thymidine kinase alone and lysate reacted with NDR1-KD) in parallel; with these controls we could disregard abundant proteins that are detected nonspecifically. We have carried out substrate labeling from brain lysates eight times, using

P3 (2X), P8 (5X), and P13 (1X) brains, to identify potential NDR1 targets. We identified five phospho-proteins that are specific to NDR1-as-CA and are detected in more than one experiment (Table 1). Strikingly, four of these contained the consensus sequence of HXRXXS/T, which is highly similar to the one reported for the NDR1 homolog Cbk1p (HXRRXS/T; Mazanka et al., 2008; Table 1). The remaining candidate was not included in the table, because the phosphorylation site was preceded by acidic amino acids, rendering it an unlikely NDR1 substrate. In addition, we cultured dissociated cortical neurons on transwell insert culture dishes in order to harvest neuronal processes but not cell bodies to simplify total protein content. We identified one additional candidate with the same consensus site: Rab11fip5 (Rab11 family interacting protein 5; Table 1). Proteins without the consensus sequence were not included in the table for this experiment.

In the presence of TTX, the first Ca2+ spike was intact, but rhythmic Ca2+ spikes were markedly suppressed in CaV2.3−/− neurons

(1.2 ± 0.20, n = 5) compared with wild-type neurons (5.17 ± 0.79, n = 6; p = 0.002; Figures 4B and 4F). Application of SNX-482 (500 nM) to wild-type neurons similarly suppressed rhythmic Ca2+ spikes (5.17 ± 0.79 in control [n = 6] versus 1 ± 0.00 for SNX [n = 4]; p = 0.003; Figures 4B and 4F), leaving only the first spike intact. The time to the LTS peak was significantly increased in SNX-482 treated CaV2.3+/+ (282.25 ± 38.78 ms; p = 0.004) and CaV2.3−/− Apoptosis Compound Library order neurons (275.40 ± 20.53 ms; p = 0.001) compared with CaV2.3+/+ (142.50 ± 12.17 ms). The amplitude of LTS measured from the first inflection to the peak was reduced in SNX-482 treated CaV2.3+/+ (22.83 ± 1.37 mV; p = 0.02) and CaV2.3−/− neurons (21.84 ± 1.13 mV; p = 0.006) compared with CaV2.3+/+

(27.97 ± 1.22 mV), suggesting the role of CaV2.3 in generating depolarization following the activation of T-currents. The width of LTS, measured between the points of inflection to deflection, was prolonged in CaV2.3−/− neurons (219.75 ± 35.69 ms; p = 0.013) as well as SNX-482 treated CaV2.3+/+ (185.6 ± 21.78 ms; p = 0.037) compared with the wild-type (135.01 ± 6.92 ms), suggesting an inefficient activation of Ca2+-dependent slow AHP. These results support the idea that CaV2.3 channels contribute to the strength of the Ca2+ spike that is critical selleck inhibitor for the recruitment of slow AHP. Slow AHP is induced by selective coupling of voltage-insensitive SK2 channels with distinct sets of Ca2+ channels. To examine the involvement of SK2 channels in slow AHP in this system ( Debarbieux et al., 1998), we isolated SK2 currents by utilizing the SK2-specific blocker, apamin, a bee-venom toxin ( Sah, 1996 and Sah and McLachlan, 1991). Sample traces are shown in Figure 5A. Before adding apamin, currents evoked by depolarizing steps (50 ms) from −60 mV to −30, −20, or −10 mV were 0.32 ± 0.14,

0.48 ± 0.12, aminophylline and 0.69 ± 0.13 pApF−1 in CaV2.3−/− neurons (n = 9), respectively, compared to the corresponding values of 1.11 ± 0.14, 1.64 ± 0.15, and 1.96 ± 0.13 pApF−1 (n =  11) in wild-type RT neurons (p < 0.001; Figure 5B). These results suggest that SK2 currents were significantly reduced in CaV2.3−/− neurons compared to the wild-type. Next, to examine the amplitude of SK2 currents under the conditions close to that of LT bursting, the currents were activated by repeated voltage gating of Ca2+ channels ( Cueni et al., 2008) at −20 mV. Compared with the wild-type control (2.61 ± 0.11, 1.71 ± 0.13, 1.3 ± 0.07, and 0.97 ± 0.05 pApF−1), SK2 currents were smaller in SNX-482 treated CaV2.3+/+ neurons (1.19 ± 0.13, 0.87 ± 0.04, 0.79 ± 0.03, and 0.69 ± 0.04 pApF−1) but were comparable to those of CaV2.3−/− neurons (1.08 ± 0.13, 0.64 ± 0.04, 0.57 ± 0.02, and 0.51 ± 0.

Viewed from a perspective of temporal dynamics, the high similarity of node relationships within SSM and visual systems and the default mode system might indicate that these systems in particular are relatively stationary, whereas other subgraphs selleck such as task control systems might have more dynamic sets of relationships. It should also be noted

that several studies (Buckner et al., 2009 and Cole et al., 2010) have implicated the default mode system as the seat of the most prominent “hubs” in rs-fcMRI brain graphs. Although default mode nodes may indeed have many ties, the isolated nature of the default mode subgraph recasts the meaning of these nodes as hubs in the context of brain-wide rs-fcMRI networks. One of the more striking features of the modified voxelwise analysis is that subgraphs appear to be

arranged in spatial motifs throughout the cortex. Figure 7 demonstrates the presence of motifs at a single threshold of the modified voxelwise analysis. For each subgraph, the distribution of its spatial interfaces (defined as en face voxels) with other subgraphs is plotted, and then these neighboring subgraphs are examined to see whether they are themselves unlikely to interface LY294002 manufacturer (implying a 3-step motif). For example, the light blue subgraph interfaces predominantly with red and yellow subgraphs, which are themselves miniscule portions

of each others’ borders (red is 3.5% of yellow’s border, and yellow is 2.6% of red’s border), implying a yellow-light blue-red motif. Plots of relevant subgraphs on brain surfaces visually confirm the presence of motifs. Three instances of this motif are demonstrated, for the light blue, black (salience), and green (dorsal attention) subgraphs. Other 3-step motifs are present but not shown (e.g., red-teal-purple), and these motifs can be found up and down subgraph hierarchies (i.e., thresholds). A principal concern about such spatial motifs is that they are artifactual—that they arise as intermediate mixtures of adjacent signals, particularly when averaging over subjects. Mephenoxalone Although these concerns cannot be entirely excluded, several interposed subgraphs (e.g., the green dorsal attention system or the teal ventral attention system) have firm and extensive experimental bases. If these are not considered artifactual, then other subgraphs deserve similar consideration. At the onset of functional neuroimaging some 25 years ago, investigators made educated guesses about the types of operations that the human brain must perform, and designed experimental paradigms to elicit such operations (Lueck et al., 1989, Pardo et al., 1991, Petersen et al., 1988 and Posner et al., 1988).

For instance, when the potassium channel Kv4.2 is exogenously expressed in neurons in culture or slices, it localizes diffusely to the somatodendritic region (Chu et al., 2006; Rivera et al., 2003), whereas endogenous Kv4.2 localizes in a conspicuously punctate manner (Burkhalter et al., 2006; Jinno et al., 2005). These problems may be circumvented by introducing tagged proteins into a knockout background

(Lu et al., 2010) or by knocking GFP into the locus of the endogenous gene (Chiu et al., 2002). However, the former method may fail if the expression of the introduced transgene is not regulated at precisely the same level and with the same temporal pattern as the endogenous protein and the latter method is time consuming and costly. Moreover, both methods have three serious limitations that restrict their applicability: (1) they do not readily allow labeling Selleck INCB018424 of two or more proteins in the same cell, (2) it is difficult to confine the expression of the tagged proteins to a genetically defined subset of cells, and (3) they do not allow any analysis of either posttranslational modifications or specific protein conformations. Recently, a novel strategy was used to label endogenous proteins in a manner that avoids the drawbacks associated with traditional approaches (Nizak et al., 2003). Recombinant antibody-like proteins (termed intrabodies) that bind to endogenous target proteins were selected from

a library of single-chain antibodies, scFvs (Huston et al., 1988), using phage display. The Selleck Paclitaxel genes encoding intrabodies were then fused to GFP genes and transfected into cells in culture allowing an activated form of Rab6 to be visualized in real time. Phage display selection of scFv libraries has also been used to generate intrabodies against neuronal proteins such as Gephyrin and Huntingtin (Southwell et al., 2008; Varley et al., 2011). Nonetheless, this method has a serious drawback: the scFv scaffold requires disulfide bonds for stable folding, but the reducing environment of the cell precludes the formation

of disulfide bonds. Thus, the scFv scaffold is prone to misfolding and/or aggregation (Goto and Hamaguchi, 1979; Goto et al., 1987; Proba et al., 1998). This problem was subsequently solved by using the 10th PDK4 fibronectin type III domain from human fibronectin (10FnIII) as a scaffold (Koide et al., 1998). This domain has an overall beta-sandwich topology and loop structure similar to the VH domain of IgG but folds stably with no disulfide bonds (Dickinson et al., 1994; Koide et al., 1998; Main et al., 1992). Libraries composed of 10FnIII domains have been combined with phage display selection to create binders to targets, such as one against the Src SH3 domain (Karatan et al., 2004), that work in reducing environments. Another innovation has been the use of mRNA display, an entirely in vitro selection method that uses libraries with > 1012 sequences, 103- to 104-times higher diversity than phage display.

NLG1 knockout (KO) or transgenic mice showed synaptic dysfunctions and ASD-like behaviors (Varoqueaux et al., 2006; Chubykin et al., 2007; Blundell et al., 2010; Dahlhaus et al., 2010). Thus, the levels of NLGs within the synaptic membranes are presumed to directly modulate the synaptic functions in vivo. Although several reports indicated that the surface Bortezomib supplier levels of NLG1 are regulated by synaptic activities through membrane trafficking (Schapitz et al., 2010; Thyagarajan and Ting, 2010), the regulatory mechanisms to control protein levels of NLG remains unclear. Here, we show that NLG1 is sequentially cleaved by ADAM10 and γ-secretase to release its extra- and intracellular domain fragments,

respectively. Proteolytic processing of NLG1 resulted in the elimination of NLG1 on the cell surface, thereby causing a decrease in the synaptogenic activity of NLG1. We further show that ADAM10-mediated shedding is regulated in an activity-dependent manner through NMDA receptor (NMDAR) activation or by binding to secreted forms of NRXs. Our present results suggest that neuronal activity and interaction with NRXs regulate the levels of NLG1 via proteolytic processing to modulate the adhesion

system as well as the functions of synapses. NLGs are synaptogenic type 1 transmembrane proteins that harbor selleck chemical large extracellular domains (Ichtchenko et al., 1995). While the levels of NLGs are presumed to be correlated with their physiological and pathological functions (Varoqueaux et al., 2006; Chubykin et al., 2007; Glessner et al., Ketanserin 2009; Blundell et al., 2010; Dahlhaus et al., 2010), little information is available on the proteolytic mechanism of NLGs. Several lines of evidence have indicated that a subset of type 1 transmembrane proteins are processed by

sequential cleavages by ectodomain shedding and intramembrane cleavage, the latter being executed by γ-secretase (Beel and Sanders, 2008; Bai and Pfaff, 2011). To test whether the levels of NLGs are regulated by proteolytic processing, we analyzed endogenous NLG polypeptides in adult rat brains (Figure 1A). Immunoblot analysis using antibodies that specifically recognize the cytoplasmic region of NLG1 and NLG2 (see Figure S1 available online) revealed immunopositive bands at ∼20–25 kDa, in addition to full-length (FL) protein that migrated at ∼120 kDa. Because the predicted sizes of the cytoplasmic domain of NLGs were within the range of 120–165 amino acid (aa) lengths (NLG1, 125 aa; NLG2, 137 aa), we reasoned that the ∼20–25 kDa polypeptides represent the membrane-tethered C-terminal fragment (CTF) of endogenous NLGs. Multiple bands corresponding to CTFs may represent different posttranslational modifications (e.g., glycosylation, see below). To examine whether these CTFs are processed by the γ-secretase activity, we incubated the membrane fractions of rat brains at 37°C and detected the appearance of additional bands that migrate faster than the CTFs with each NLG.

, 2003, Wilson et al., 2005 and Wilson et al., 2006). Consistent with those findings, behavioral data obtained in three-choice recognition memory tasks from elderly humans show a shift in performance toward pattern completion, as reflected during lure trials by more incorrect “old” responses and fewer correct “similar” responses when compared this website to young adults (Toner et al., 2009 and Stark et al., 2010). This error

profile on lure trials is further worsened in aMCI patients compared to normal aging (Yassa et al., 2010). Functional neuroimaging data was first subjected to a one-way analysis of variance (ANOVA) of trial type to select voxels that showed task related activity (see Experimental Procedures). To avoid selection bias while acknowledging the dependence of observations in the two treatment conditions Dasatinib mw for aMCI subjects, this analysis included data from all control subjects and aMCI patients randomly selected from either their placebo or their drug condition such that both treatment conditions were equally represented (approximately half from placebo and half from the drug condition). This analysis maintains independence of observations by including each aMCI patient only once. Unrelated foil items correctly identified as “new” were used as an implicit baseline against

which all other conditions were compared. This analysis resulted in an area of task-related activity localized within the left DG/CA3 subregion of the hippocampus (Figure 2C). To assess whether increased hippocampal activation was observed in the current study of patients with aMCI, we first compared functional activity during fMRI memory task performance between healthy control subjects and patients with aMCI on placebo within the DG/CA3 region as shown in Figure 2C.

The aMCI patients on placebo showed significantly increased blood oxygenation level-dependent (BOLD) activation during lure trials correctly identified as similar when compared to control subjects Bay 11-7085 (t = 2.056, p = 0.048) (Figure 2E; see also Figure S1). This finding replicates earlier findings in Yassa et al. (2010). Behavioral performance of the aMCI subjects on placebo compared to healthy controls during the scanning task was assessed by the rates of each response option (old, similar, or new) on the critical lure trials. A between-groups ANOVA using only the response categories “old” and “similar” to maintain response independence revealed a significant effect of response type (F(1,32) = 5.357, p = 0.027) and, importantly, a significant group (control versus aMCI placebo) × response interaction (F(1,32) = 7.687, p = 0.009) showing that aMCI patients on placebo incorrectly identified lure items as “old” more often and gave relatively fewer correct responses of “similar” compared to control subjects (Figure 3A). That profile is consistent with reduced pattern separation and a shift to pattern completion in aMCI.

, 2013). While these studies represent a major milestone, they also point to the need for further advances. This includes tracer injections placed into even more areas and within different subregions of areas having connection heterogeneity, the use of finer-grained cortical parcellations (see above, Figure 2B), and quantifying connection strengths across the entire cortical sheet, irrespective of any particular parcellation. When considered as a binary interareal connectivity matrix, the macaque parcellated connectome is a dense (highly interconnected) graph (67% of all possible connections exist in the 29 × 91 matrix), which is incompatible

with the small-world network architecture previously hypothesized. However, viewed in a different way, macaque cortex contains ∼1.4 billion BMS-777607 cortical neurons (Collins et al., 2010) and approximately104 synapses/neuron (Beaulieu and Colonnier, 1985 and Braitenberg and Schuz, 1991). This suggests a sparsity of ∼10−5 for individual neurons.

At an intermediate level of ∼1 mm3 (i.e., the approximate voxel size for whole-brain neuroimaging) Selleckchem ON1910 corticocortical connectivity, each patch of cortical neurons may be directly linked to a domain that may be roughly 10%–20% of the cortical sheet (based on supplemental figures in Markov et al., 2012), but it would be valuable to refine such estimates. Systematic studies of corticocortical connectivity in rodents languished until recently, despite its simpler cortical organization. Major progress has come from a recent study that quantified projection pattern from anterograde tracer injections

into ten visual areas in a 40-area parcellation of mouse cortex, that includes transitional and archicortical subdivisions (Wang et al., 2012). Virtually all of the ten visual areas are interconnected reciprocally with one another, and the overall binary graph density in the 10 × 40 connectivity matrix they studied (Figure 3F) exceeds that noted above for the macaque. Connection strengths span at least three orders of magnitude (at a minimum, as estimates were limited by the sensitivity of the method), and they follow a lognormal distribution similar to that reported in the macaque. Thus, those important principles apply to rodents and primates despite major differences in the total number and arrangement of cortical areas. The Allen Brain Institute has taken the systematic analysis of long-distance connectivity in the mouse to a new level through a publicly accessible connectivity atlas that currently includes 1,010 anterograde tracer injections (http://connectivity.brain-map.org). By using sensitive viral tracers, whole-brain data acquisition via serial two-photon microscopy, and standardized experimental and analysis protocols that enable quantification of projection strengths, this project will serve as an invaluable resource that greatly enhances our understanding of the mouse mesoconnectome.

Thus, the level of interference

was much lower, and so we predicted that patients with damage to conjunctive representations should be able to perform better on the Low Interference conditions. Based on the findings from experiment 3, we expected the MTL cases to perform well up to approximately 36 trials (when deficits had emerged in the High Ambiguity condition). Critically, in each Low Interference condition, there were only 30 trials involving Selleck Talazoparib the comparison High Ambiguity Object stimuli. Thus, even though the number of intervening stimuli was controlled, there was much less build-up of repeated single features in this condition compared to the High Interference condition. As such, we did not expect impairments on this condition (consistent with their intact performance on the first 36 trials of the High Ambiguity condition in experiment 3). To counter the claim that any deficits in the High Interference condition were due to participant

fatigue, the conditions were always administered in the following order: Low Interference 1, High Interference, Low Interference 2. All other parameters were identical to that described in experiment 3. Due to a response box malfunction, the data from the first Low Interference condition for patient HC5 were lost. We calculated a discriminability measure (d′) using the method developed for same-different judgments (Macmillan and Creelman, 1991). In this analysis, correct responses of “different” to images that were different were designated as Regorafenib concentration hits, and incorrect responses of “different” to images that were in fact the same were designated as false alarms. Scores

of 1.0 or 0.0 for hits and false alarms were subjected to a standard correction whereby half a trial was either subtracted or added to the actual score. Data from each individual patient were compared to his or her respective control group using Crawford’s Modified t tests (Crawford et al., 2009). Given our directional hypotheses, all t tests were one-tailed and unless stated otherwise. In addition to d′ (Figures 5 and 6) we also report reaction times and percent correct for each experiment (Table S7), as well as percent correct split according to High Ambiguity Object nonmatch trial type (fill, inner shape, outer shape; Table S8). We would like to thank all participants for their time, Joan K.W. Ngo and Lily Hung (University of Toronto) for help with control data collection and Georgina Browne (Addenbrookes Hospital, Cambridge), Tina Bingham, and Sharon Davies (MRC CBU) for help with patient testing. This research was funded by the Canadian Institutes of Health Research (MOP-115148 to M.D.B.), the UK Medical Research Council (MC_A060_5PR10 to R.N.A.H.), the Wellcome Trust (grant #082315 to A.C.H.L.), and a European Erasmus grant for studying abroad (IIAG). “
“We often reflect on our past to understand current experience or predict future events.

In addition, in Sprague Dawley rats antepartum maternal behavior, BMS-754807 in vitro which was decreased as a result of PNS, was decreased in the granddaughters of the prenatally stress rats as well ( Ward et al., 2013). In guinea pigs transgenerational

effects on the HPA-axis function of PNS were shown; F2 offspring of PNS guinea pigs were shown to have higher fecal cortisol metabolites than F2 control offspring ( Schopper et al., 2012). Overall these studies suggest that prenatal stress may not only affect the exposed offspring, but may alter the phenotype of the following generations. This, in turn, suggests that prenatal stress may affect the disease risk in multiple generations. More research is needed to understand the mechanism underlying these trans-generational effects. From

a gene-environment mismatch theory perspective these trans-generational effects pose an interesting question. It seems that exposure to standard environmental conditions do not normalize the now selleck kinase inhibitor mal-adaptive alterations in the F1 or F2 offspring. From an evolutionary standpoint, one may argue the absence of an environmental stressor in the current generation that was present in the previous generations may indicate variable environmental conditions, and since most of these mis-match pathologies develop after reproductive age, and thus will not diminish the population fitness, reversal of the phenotype has no priority. However, the “original” phenotype has to have some fitness advances otherwise this phenotype would have been lost during evolution. Thus one may wonder which environmental cues would lead to “normalization” of the

phenotype, and whether we can mimic these environmental cues as a preventative strategy. Prenatal stress exposure alters the phenotype of the offspring, and when the postnatal environment does not match the prenatal environmental conditions these alterations may have pathological consequences. The studies discussed in this manuscript clearly indicate that there are some innate differences in Terminal deoxynucleotidyl transferase stress vulnerability, like the stress-coping style, that may impact an individuals’ risk of developing metabolic and other pathologies. Furthermore, this innate risk seems to be modulated by the prenatal environment, dependent on the genotype of the fetus, prenatal stress exposure may have adverse or protective properties. Additionally, to make risk prediction even more complex, the postnatal environment also interacts with both the genotype, and the prenatal environment. Using the stress-coping style model as an example, rats genetically selected for a passive stress-coping style have an increased risk to develop obesity.

However, unlike in the locust brain, the orientation of the monarch CC and the remaining central brain were rotated by roughly 90° (around the sagittal axis), with respect to the fixed orientation of the optic lobes. Therefore, the monarch FK228 PB is located on the posterior side of the central body close to the posterior surface of the brain (Figures 2E–2G). The distinctly noncontinuous layout of two hemispheres of the monarch PB again resembled the situation in the hawkmoth. The central body and the PB were separated by a midline-spanning neuropil that left

two gaps on either side for passage of the W-, X-, Y-, and Z-bundles. These massive tracts provide the only connection between the PB and central body and contain the axons of all compass-related columnar neurons of the CC. Within the central body, the bean-shaped CBL was located anterior to the larger CBU, while the noduli were located ventrally (Figure 2E). We reconstructed individual neurons of the monarch CC to further define the butterfly sun compass network and substantiate that the layout of this brain area is highly conserved between locusts MAPK inhibitor and monarchs. Of the five groups of neuron (TL, CL1, TB, CPU1, CP) known to constitute the polarization vision

network of the locust central complex (Heinze et al., 2009), we identified four groups in the monarch CC, covering all processing stages from the proposed input to the proposed output neurons of the CC (Figure 3; Table 1). Homologies were established based on detailed information regarding the location of input/output

arborizations, the structure of terminals, and the heterolateral connectivity patterns (Figure S1 available online, Figure 3, Table 1). Consequently, we classified the identified neurons as monarch butterfly versions of TL2 (n = 6), TL3 (n = 5), CL1 (n = 4), TB1 (n = 4), CPU1a (n = these 6), and CPU1b neurons (n = 1). Although subtypes of CL1 neurons could not be identified unambiguously in the monarch, CPU1 subtypes (CPU1a/b) could be clearly distinguished in monarchs because all arborizations of CPU1b neurons were located contralateral to the soma (Figure 3E), which is the defining feature of locust CPU1b cells (Heinze and Homberg, 2008; Figure S1E). In addition to neurons of the core polarization-sensitive network, conditionally polarization-sensitive neurons of the locust provide another level of complexity to the CC-network, as they are thought to be recruited only in a context-dependent manner (Heinze and Homberg, 2009). Likewise, homologs of all three conditionally polarization-sensitive cell types were also identified in the monarch CC (CL2, CPU2, and CPU4) (Table 1). The only major types of neuron that were not found in the monarch were homologs of locust cells directly connecting the PB with the lateral triangle/medial olive (CP1, CP2).