, 2006a; Wang et al , 2008) Since then, additional transmembrane

, 2006a; Wang et al., 2008). Since then, additional transmembrane proteins have been implicated in AMPAR function ( Jackson and Nicoll, 2011; Kalashnikova et al., 2010;

Schwenk et al., 2009, 2012; von Engelhardt et al., 2010). Two outstanding questions are posed by these studies. First, are there additional auxiliary SB203580 in vitro proteins that contribute to receptor function? Second, how do the auxiliary proteins contribute to synaptic transmission and behavior? In C. elegans, the GLR-1 AMPAR mediates glutamate-gated current in a subset of interneurons that control movement and the avoidance of noxious stimuli ( Hart et al., 1995; Maricq et al., 1995). Genetic and reconstitution studies have demonstrated that GLR-1 is part of a multiprotein synaptic complex required

for GLR-1-mediated currents and behavior ( Wang et al., 2008; Zheng et al., 2004). In addition to GLR-1, this complex contains SOL-1 and at least one of the TARPs, i.e., STG-1 and STG-2. SOL-1 is an evolutionarily conserved type I transmembrane protein that contains protein-protein interaction motifs called CUB-domains (complement, Urchin learn more EGF, BMP). SOL-1 was shown to regulate the rate of GLR-1 desensitization as well as its rate of recovery from desensitization ( Walker et al., 2006a, 2006b; Zheng et al., 2006). More recently, the CUB-domain-containing transmembrane proteins Neto1 and Neto2 were identified in mice. These proteins contribute to signaling mediated by NMDA (N-methyl-D-aspartate) and kainate iGluRs, respectively ( Ng et al., 2009; Zhang et al., 2009). However, C. elegans SOL-1 and the vertebrate Neto proteins and belong to two different classes of CUB domain proteins. Whereas SOL-1 contains four predicted CUB domains, Neto1 and Neto2 contain two CUB domains and a LDLa domain (low-density lipoprotein receptor

class A). This raises the question of whether multiple classes of CUB-domain proteins contribute to the function of specific iGluRs. In an earlier study (Zheng et al., 2006), we found that a secreted form of SOL-1 (s-SOL-1) that lacked the transmembrane domain was sufficient to rescue the behavioral and synaptic signaling defects of sol-1 mutants. Here, we show that coexpression of s-SOL-1 with GLR-1 and STG-1 in heterologous cells is not sufficient to reconstitute glutamate-gated current. This result led us to the hypothesis that an additional protein, which was missing in heterologous cells, is expressed in neurons and is required for s-SOL-1 function. Presumably, this protein is part of the GLR-1 receptor complex and recruits s-SOL-1 to the complex, thus contributing to receptor function. This model also suggests that the protein itself might have a modulatory role in GLR-1 function. To identify the putative interacting protein, we used an unbiased forward genetic strategy and discovered SOL-2, a CUB-domain transmembrane protein that is the homolog of the vertebrate Neto proteins, with two CUB domains and a LDLa domain.

Similar to immature and mature animals, costimulation of the surr

Similar to immature and mature animals, costimulation of the surround suppressed neuronal responses and increased their selectivity in dark-reared mice (Figures 6A and 6B; firing rate change RF + natural surround, –60.7% ± 7.9%, p < 0.001; RF + phase-randomized surround, –52.3% ± 10.3%, p < 0.001, n = 15; t test), Ribociclib indicating that the capacity of visual circuits for surround modulation was maintained and not disrupted by rearing animals devoid of visual experience. Importantly, however, we observed no significant

differences between the effects of the natural and phase-randomized surrounds on responses to stimuli in the RF (Figure 6B) in terms of firing rate (RF + natural versus RF + phase-randomized surround, p = 0.33, paired t test), response selectivity (p = 0.23, paired t test), or information transmitted per spike (p = 0.88, paired t test). Differences in the level of spike suppression were not related to differences in absolute firing rates in any age

group (Figure S6). CP-673451 order The indifference of dark-reared V1 neurons to the statistical properties of surround stimuli was also reflected at the level of subthreshold membrane potential dynamics (note that the cellular sensitivity for spiking to membrane potential changes was comparable to the other age groups; Figure S5; Azouz and Gray 2003). The median Vm in dark-reared mice was not significantly altered by costimulation of RF and surround (Figures 6C and 6D, n = 19; p = 0.33; Friedman’s test). Similar to normally reared, mature mice, there was a strong dependence of ΔVm (Figure 6E) on the level of Vm depolarization during stimulation of the RF Rutecarpine alone (Figures 6G and 6H). However, the distribution of ΔVm was not different between natural and phase-randomized surround stimulation conditions (Figure 6F, p = 0.21, Wilcoxon rank sum test), and ΔVm at most depolarized Vm during RF stimulation was not significantly different

when costimulating the surround with natural and phase-randomized stimuli (Figures 6G and 6H) in dark-reared V1, similar to the observations in immature V1 (Figures 3F–3J). Accordingly, while the likelihood of spiking during large-amplitude depolarizing events (which were unaltered in frequency of occurrence across conditions; Figure 6J; p = 0.82, Kruskal-Wallis test) was reduced, it was not significantly different between the two surround conditions (Figure 6I; p = 0.18, Kruskal-Wallis test across all conditions). These findings are consistent with a similar level of firing rate suppression by phase-randomized and natural surround stimuli (Figure 6B) in these visually inexperienced but mature animals. Thus, the emergence of neuronal sensitivity for image features extending beyond the RF boundaries requires visual experience after eye opening. Our findings provide evidence for a progressive developmental refinement of visual processing to the global statistics of the natural environment, as hypothesized previously (Olshausen and Field, 1996, Berkes et al.

For example, we only enforced that firing rates above a value clo

For example, we only enforced that firing rates above a value close to r0 be stably maintained following the removal of recurrent inhibition through total unilateral inactivation ( Figure 3G, colored

portions). Finally, a regularization term ( Hastie et al., 2009) was added to the cost function to penalize exceptionally large connection strengths that lead to synaptic Screening Library ic50 response magnitudes inconsistent with intracellular measurements ( Aksay et al., 2001). This procedure succeeded in generating circuits that simultaneously reproduced all of the experimental data of Figure 2 (Figures 4 and 5). The circuits temporally integrated arbitrary patterns of saccadic inputs (Figures 4E and 4F, left, two example circuits) and precisely TGF-beta inhibitor reproduced the tuning curves of every experimentally recorded neuron in our database (Figures 4E and 4F, right, four example neurons). Furthermore, inactivations of these well-fit circuits reproduced the characteristic pattern of drifts following both contralateral and ipsilateral inactivations (Figure 5). Thus, the model recapitulated both the gross and neuron-specific properties

of an entire vertebrate neuronal circuit. Given that the complete circuit connectivity is defined by 5100 synaptic weight parameters, as well as the unknown form of the synaptic activations s(r), we expected that many different parameter value combinations could provide optimal or near-optimal fits to the available experimental data. To explore this large parameter space, we implemented a formal two-stage sensitivity analysis, first characterizing the dependence of the model fits on the form of synaptic activations, and then, for a given form of synaptic activations, the dependence on the pattern of connection strengths.

The sensitivity of the model fits to the form of excitatory and inhibitory synaptic activation was explored by systematically varying the two parameters describing the activation function: θ, which controlled the width, and Rf, which controlled the point of inflection (Figure 4A). Suplatast tosilate This allowed us to consider models in which the transformation at synapses was linear, saturating (e.g., resulting from synaptic depression or saturation of driving forces), or sigmoidal (e.g., resulting from synaptic facilitation or voltage-activated dendritic currents). Excitatory and inhibitory recurrent synapses were allowed to have different forms of nonlinearity. This analysis showed that the integrator network can utilize only a restricted set of synaptic activation functions to generate persistent firing. Figure 4B shows the space of synaptic activations permitted (blue) and prohibited (red) by the experimental constraints when inhibitory and excitatory synapses have identical (left) or different (middle, right) forms.

, 1997 and Seeburg et al , 1998) Functionally, the adenosine dea

, 1997 and Seeburg et al., 1998). Functionally, the adenosine deaminase enzyme ADAR2 is responsible for RNA editing that recodes a glutamine to arginine in the selectivity filter of GluR-B subunits (“Q/R editing” of GluR-B); consequently, ADAR2 knockout mice exhibit increased Ca2+ permeability, with concomitant epilepsy and death (Higuchi et al., 2000). More broadly, a generalized dysregulation of brain RNA editing in humans may contribute to epilepsy, depression, and suicidal tendencies (Gurevich et al., 2002, Schmauss, 2003 and Sergeeva et al., 2007). Indeed, it is this website likely that numerous other editing

substrates remain to be identified in the mammalian brain, given the high inosine content Pictilisib solubility dmso of mRNA in neural tissue (Paul and Bass, 1998). In particular, we wondered whether RNA editing might fine-tune the calmodulin (CaM) regulation of voltage-gated calcium channels (VGCCs). This Ca2+ feedback regulation would be an especially attractive target for editing, because structure-function analysis

reveals that even single amino acid substitutions at critical channel hotspots can markedly alter modulatory properties (Dick et al., 2008, Tadross et al., 2008 and Zühlke et al., 2000), and such regulation impacts functions as diverse as neurotransmitter release, neuronal pacemaking, neurite outgrowth, and gene expression (Dunlap, 2007). Figure 1A cartoons such regulatory hotspots, which are located

on the amino- and carboxyl-termini of the pore-forming α1 subunits of VGCCs. The best-studied locus is a CaM-binding domain approximating a consensus IQ element satisfying Thymidine kinase the amino acid pattern IQxxxRGxxxR (Jurado et al., 1999), with x denoting any residue. CaM binding at this IQ domain is critical for CaM/channel regulation (Liu et al., 2010), and mutations in the central isoleucine strongly attenuate Ca2+ regulation (Shen et al., 2006 and Yang et al., 2006). Here, we reveal the existence of ADAR2-mediated RNA editing of the IQ domain of CaV1.3 channels. This editing appears specific to the central nervous system, and proteomic analyses indeed confirm the presence of edited CaV1.3 channel proteins within native brain tissues. Adding to the theme of specificity, no RNA editing was found for CaV1.3 coding regions outside of the IQ domain, nor was IQ-domain editing present in any other members of the CaV1-2 channel family. All these features suggest that CaV1.3 editing may entail distinctive sequelae for the CaM-dependent inactivation (CDI) of these channels, particularly in relation to the availability of these low-voltage activated channels to support neurotransmission at ribbon synapses (Evans and Zamponi, 2006 and Yang et al., 2006) and repetitive firing within neurons throughout the brain (Chan et al., 2007). Accordingly, we demonstrate that RNA editing of the CaV1.

, 2008) Because these molecules play a dual role in the

, 2008). Because these molecules play a dual role in the

peripheral immune response and in neural plasticity in the CNS, they could be involved not only in the acute phases of stroke, but also in subsequent recovery. After stroke, these molecules might make a dual contribution to exacerbate LY294002 solubility dmso damage in the context of the inflammatory response and to restrict recovery by limiting plasticity. Here, we investigate these possibilities by examining response to in vivo and in vitro models of stroke in PirB KO mice and KbDb KO mice. To examine whether Kb and Db contribute to damage after stroke, we gave adult KbDb KO mice (Vugmeyster et al., 1998) transient middle cerebral artery occlusion (MCAO; Han et al., 2009). KbDb KO mice subjected to MCAO had no significant difference from wild-type (WT) in infarct area at 24 hr postinjury (37% versus 41%; p = 0.45; Figure 1A), and their initial neurological deficit was also similar (p = 0.4; see Figure S1A available online; Han et al.,

2009). However, by 7 days post-MCAO, infarct area in KbDb KO mice was modestly reduced (32%) compared to WT (44%; p = 0.03). Physiological parameters monitored during surgery were similar between WT and KO and fell within previously reported ranges (Table S1; Han et al., 2009). To examine motor recovery after MCAO, we tested KbDb KO and WT mice on two motor performance tasks, rotarod and foot fault. Prior to MCAO, KO and WT mice learned buy AZD8055 both tasks, improving performance over subsequent trials, as evidenced by the increased MTMR9 latency to fall from the rotarod (Figure S1B) and fewer missteps on foot fault (Figure S1C). KO mice learned both behaviors better than WT (p < 0.001), consistent with prior observations of enhanced motor learning (McConnell et al., 2009). After stroke, performance on rotarod and foot fault was significantly better in KO mice versus WT (p < 0.001 for both paradigms; Figures 1B and 1C). Overall, KbDb KO mice had smaller infarcts and recovered significantly faster and to a greater extent on motor performance (to 91% of prestroke rotarod time compared to 75% for WT at 28 days). The observations

that KbDb KO mice have smaller infarct areas and better behavioral recovery after MCAO suggest that Kb and Db may contribute to damage in WT mice. Moreover, because mice lacking Kb and Db have enhanced synaptic plasticity, it is conceivable that increased expression would contribute to diminished plasticity, thereby compromising recovery. To examine this idea further, we assessed MHCI levels after MCAO. Quantitative real-time PCR (qRT-PCR) revealed highly increased Kb and Db mRNA in the damaged hemisphere (ipsi) compared to sham control after MCAO (Figure 2A) both at 24 hr (Kb mRNA: 2.5-fold increase, p < 0.05; Db mRNA: 3.1-fold increase, p < 0.001) and at 7 days (Kb mRNA: 8.0-fold increase, p < 0.01; Db mRNA: 7.

For instance, rab4 acts downstream of rab5 in endosomal transport

For instance, rab4 acts downstream of rab5 in endosomal transport, and activated rab5 controls the spatial and temporal properties of rab4 activation by regulating its GTPase exchange factor (Miaczynska et al., 2004). Multiple rabs in a pathway associate and

dissociate from endosomes in a regulated, sequential manner, and not surprisingly, many of the regulatory families interact and regulate one another, thereby forming interconnected regulatory networks. The simultaneous action of these mechanisms contributes to compartment identity and ensures vectorial transport. Owing to the advancement in imaging techniques, especially high-resolution live-cell imaging, it is now clear that the endosomal system is very dynamic and more complex than previously anticipated (Kirchhausen, 2009, Lakadamyali et al., 2006, Mattheyses selleck chemical et al., 2011, Sönnichsen et al., 2000 and Zoncu et al., 2009). One long-standing question in the field of membrane trafficking is the stability of endosomal compartments in time and space. For the biosynthetic system, a “stable compartment” model has been favored in which enduring ER and Golgi compartments are connected via mobile small vesicular carriers Protein Tyrosine Kinase inhibitor that deliver and remove cargos

(Pfeffer, 2010 and Polishchuk et al., 2003). Membrane-associated regulators required for directed fusion (such as SNAREs) are then temporarily found in the “incorrect” compartment after fusion and need to be recycled back to their compartment of origin. In the endosomal system, evidence supporting a “maturation” model has emerged in which earlier compartments in the pathway recruit successively new regulators (such as rabs and lipid-modifying enzymes), which change the compartment identity over time (Poteryaev et al., 2010 and Zoncu et al., 2009). Live-cell imaging of

endosomal compartments labeled with L-NAME HCl different endosomal regulators showed that pre-early endosomal compartments (APPL-positive) convert to early endosomes (EEA1-positive) (Zoncu et al., 2009), by shedding APPL and recruiting EEA1 to the same pre-existing endosome. Little is known about how the conversion of one compartment to another is achieved. For the switch from the APPL-positive preEE to the EEA1-positive EE, rab5 activity and accumulation of a different phosphoinositide species, PI-3P, are required (Zoncu et al., 2009). Rab 5 is at some point rapidly removed from the early endosome and replaced by rab7 (Rink et al., 2005) in a coordinated “rab conversion” event. Similar rab conversions also occur on other compartments as they mature. Rab7-containing endosomes will mature toward the late endosomal fate by recruiting additional machinery, such as ESCRT complex (Henne et al., 2011). How rab5 converts to a rab7-positive LE is not known, but rab activity and phosphoinositides probably play a role here as well.

Voltage signals were band-pass filtered (0 3 Hz – 1 kHz) and digi

Voltage signals were band-pass filtered (0.3 Hz – 1 kHz) and digitized at 50 kHz before storage. Electrodes were independently lowered with the help of manual stereotaxic manipulators (Narishige). The electrode to target the dorsal MEC was lowered vertically

(0.2–0.5 mm anterior to the transverse sinus, 4.3–4.5 mm lateral to the midline), while the electrode to target a more ventral location was lowered at a 5°–10° angle caudally (1.5–2 mm anterior to the transverse sinus, 4.3–4.5 mm selleck screening library lateral to the midline). Recordings were targeted to L1, where gamma power is known to be highest (Quilichini et al., 2010). L1 was physiologically identified by the drop in spiking activity observed upon transition from L2 and by the prominent LFP gamma oscillations during theta epochs (as in Figure 7). We could assign 14 out of 16 recording locations relative to anatomically verified Venetoclax electrolytic lesions, performed either at the recording

site or at a defined distance from the site (as in Figure 7F); the remaining two recording locations were assigned at the end of the electrode tracks. All experimental procedures were performed in accordance with German guidelines on animal welfare under the supervision of local ethics committees. For the analysis, epochs of prominent theta oscillations (4–12 Hz) with nested gamma oscillations were included, which were visually identified from the raw traces and assisted by power spectral analysis of the theta band. Theta

oscillations either occurred spontaneously or were evoked by tail-pinch. In both the in vitro and in vivo gamma recordings, the gamma PSD integral for the ventral MEC locations was so strongly reduced that identifying a pronounced peak of gamma frequency at these locations consistently was often difficult. Therefore, we do not present any comparison data for the peak frequency. However, in both the in vitro and in vivo recordings, we observed the dorsal MEC gamma peak frequency in the expected range of PD184352 (CI-1040) 35–60 Hz. Statistical analysis was performed using the nonparametric Mann-Whitney test and paired t test. Numerical values are given as mean ± SEM. This study was supported by grants from the Deutsche Forschungsgemeinschaft (SFB 618, 665; Exc 257), the Bundesministerium für Bildung und Forschung (Bernstein Centers Berlin 01GQ0410, Bernstein Fokus 01GQ0981, 01GQ0972), and the Human Frontier Science Program (LTF to A.G.). The authors thank Susanne Rieckmann and Anke Schönherr for excellent technical assistance. The authors are indebted to Michael Bendels for help with the software, Friedrich Johenning for technical assistance with the optics, and Richard Kempter for advice regarding analysis and his helpful comments on the manuscript. P.S.B. and D.S. designed the study. P.S.B., A.G., A.B., S.S., and C.B. performed electrophysiological experiments. P.S.B., A.G., S.S., and M.T.K. analyzed the electrophysiological data. S.J. and I.V.

The histofluorescence method developed in the early 1960s allowed

The histofluorescence method developed in the early 1960s allowed the visualization of these nuclei and their projection pathways in the rat brain and revealed a remarkably similar organization in that the cell bodies are found in rather compact nuclei with widespread axonal projections to distant forebrain regions (Dahlstrom and Fuxe, 1964). Cholinergic nuclei in the brainstem (lateral dorsal tegmental nucleus and pedunculopontine nucleus [LDT/PPN]) and basal forebrain area (nucleus basalis of Meynert [NBM]) have similar anatomical organization and are also implicated in the regulation of vigilance and cognitive function (Jones, 2008). In

addition to their distal forebrain projections, the neuromodulatory nuclei have multiple reciprocal connections. The LC has strong SB203580 solubility dmso projections to all of the others and receives direct input from its neighbor LDT/PPN and

from DR. The DR also projects to VTA and NBM, thereby influencing both dopamine (DA) and cholinergic input to the cortex (Hervé et al., 1987). To add to the complexity of the situation, these systems interact at the level of axon terminals by reciprocal modulation of release of transmitters. For example, noradrenaline (NA), acting at alpha 2 adrenoceptors located on terminals of all four neuronal types, inhibits release of their transmitters. At the same time, there are mutual increases of release of DA and NA via alpha 1 and D1 receptors, respectively, in check details the prefrontal cortex (PFC) (Pan et al., 2004) and acetylcholine provokes a calcium-dependent release of both DA and NA via a muscarinic receptor (Rao et al., 2003). Ultimately, an understanding of the concerted action of neuromodulatory systems will reveal how behavioral states influence, promote, or even permit cognitive activity (Briand et al., 2007). Nevertheless, STK38 in our view, a great deal has yet to be understood about the relative contribution of each one of these systems in attention,

perception, reward and punishment, learning, and memory. Here, we address the specific role of the noradrenergic nucleus locus coeruleus in modulating forebrain networks mediating cognitive activity. In addition to strongly innervating all of the other neuromodulatory nuclei, the LC sends projections to all cortical regions, as well as to thalamic nuclei, septum, hippocampus, and basal lateral amygdala (Loughlin et al., 1986; Figure 1). Moreover, LC is the sole source of noradrenergic innervation to these structures (Jones and Moore, 1977; Moore and Bloom, 1979; Asan, 1998; Samuels and Szabadi, 2008; Figure 1). Multiple approaches using lesions, pharmacology, and transgenic technology combined with behavioral analysis and in vitro and in vivo electrophysiological recording in target regions have generated a large literature and contributed much to our knowledge of how NA acts in the brain. Noradrenergic action in thalamus and cortex strongly influences arousal and behavioral state (Berridge et al.

Further, relative

mRNA expression changes did not correla

Further, relative

mRNA expression changes did not correlate with changes Bortezomib in vitro in homologous recombination. Computational analyses of sequence similarity between siRNA reagents and non-targeted, mRNA transcripts can predict off-target effects but is imperfect in all situations. Genome-wide enrichment of seed sequences (GESS) analysis looks for enrichment of non-targeted 3′ UTR regions in siRNA sense and antisense sequences [14] and [16]. In theory, these 3′ UTR matches identify unintended target genes and subsequent modulation of these genes should recapitulate the phenotype erroneously assigned to the original siRNA. The method successfully identifies genes enriched in active siRNAs for multiple screens, and can filter AZD6244 manufacturer primary screening hits to decrease the false positive rate [14] and [17]. In the previously mentioned screen for homologous recombination mediators, GESS analysis identified a significant enrichment for RAD51 3′ UTR in the high-scoring, non-RAD51 siRNAs [12]. As expected, RAD51 mRNA was depleted in the presence of 4 of

the 7 siRNAs against HIRIP3 and RAD51 mRNA levels better correlated with changes in the homologous recombination phenotype than HIRIP3 mRNA levels. Yet, only 1 of the 7 HIRIP3 siRNAs actually contained the seed match for the RAD51 UTR demonstrating that additional cross-talk events may occur in the presence of the HIRIP3 siRNAs. While GESS successfully identified RAD51 mRNA levels as the true predictor for homologous recombination, it was unable to fully explain the observed changes in this gene’s transcription, as all HIRIP3 siRNAs did not reduce RAD51. A network framework enables researchers to consider contextual influences on how pathway components assimilate, integrate and propagate knowledge in a manner that is distinct from the list model [18] and [19]. More specifically, a network motif, consisting of a coherent group of functionally related genetic

regulators, may better explain an observed phenotype where statistically-ranked lists are insufficient [4] and [20]. Dipeptidyl peptidase Already, these network motifs for target discovery have lead to better understanding of the non-intuitive relationships between genotype and disease phenotype and identification of better therapeutic targets [4] and [8]. Networks can be useful for predicting drug targets and also for selecting drug combinations [19]. Their functional context provides rational selection of single targets as well as combinatorial targets that could synergistically affect a desired phenotype because they consider pathway membership [19]. Where toxicity had previously constrained the selection of combination therapies, researchers may now instead prioritize combinations based on specificity to controlling a particular phenotype.

Each map is overlaid with corresponding domain masks to show gene

Each map is overlaid with corresponding domain masks to show general correspondence (orange for orientation mask, pink for color mask, green for direction mask). Domain masks were calculated from p-maps at a fixed threshold level (see Experimental Procedures). Pixels from blood vessels were removed from these masks. These masks were www.selleckchem.com/products/LY294002.html then

overlaid in pairs in Figures 6D–6F. In Figure 6D, V4 direction-preferring domains (green) have some overlap with orientation-preferring domains (orange). We calculated the percentage of pixels in these overlapped regions in the total direction-preferring domains. We reasoned that, within a region, if the direction-preferring domains and orientation-preferring domains are truly independent (i.e., the direction-preferring domains have no particular Angiogenesis inhibitor spatial relationship with the orientation-preferring domains), the percentage of direction-preferring domain pixels overlapping with orientation-preferring domains would be at the chance level (i.e., would not be different from the percentage of orientation pixels in the whole

V4 area). If direction-preferring domains contain more orientation-selective pixels than the V4 average, then this would indicate an overlapping that is greater than chance between these two types of domains. Figure 6D shows that the percentage of orientation pixels in the direction-preferring domains is higher than the percentage of orientation pixels in V4. Averaging across all seven cases, 40.8% ± 7.1% of the pixels in the direction-preferring domains have a significant orientation response, compared to 24.6% ± 4.7% of all V4 pixels (two-tailed t test, p = 0.002).

This difference demonstrates a tendency for direction- and orientation-preferring domains to overlap. Furthermore, for pixels within the orientation-direction overlap Metalloexopeptidase regions, their preferred direction is always orthogonal to their preferred orientation (Figure S6), a common functional property for direction-preferring domains in different areas and different species (Malonek et al., 1994; Weliky et al., 1996; Shmuel and Grinvald, 1996; Lu et al., 2010). Similar to the direction-orientation overlap, Figure 6E shows that more pixels in direction-preferring domains (25.3% ± 7.6%) have a significant color response than the proportion of color response pixels in the whole V4 area (8.9% ± 2.7%, two-tailed t test, p = 0.023). In contrast, Figure 6F shows that, although areas that show orientation preference and color preference have some overlap, the degree of their overlap does not exceed a chance level (two-tailed t test, p = 0.2). To examine the representation of directional response in the macaque ventral visual pathway, we imaged large fields of view over the foveal and parafoveal regions of V4 and adjacent regions of V1 and V2.