On the other hand, gain-of-function mutations in NCA-1, referred

On the other hand, gain-of-function mutations in NCA-1, referred to as nca(gf) henceforth, lead to exaggerated body bending termed coiling ( Yeh et al., 2008). The in vivo physiological properties of these invertebrate CHIR-99021 manufacturer channels remain to be determined. However, genetic studies of the behavioral phenotypes of C. elegans ( Humphrey et al., 2007; Jospin et al., 2007; Yeh et al., 2008) and Drosophila ( Humphrey et al., 2007)

have led to the identification of UNC-79 and UNC-80, two conserved auxiliary subunits of this new channel. Multiple auxiliary subunits of sequence-related cation channels, such as the voltage-gated calcium channels (VGCCs), promote the stabilization and membrane localization of the channel, and/or modulate channel gating and kinetics ( Catterall, 2000b; Simms and Zamponi, 2012). Despite bearing no sequence similarity to known cation channel auxiliary subunits, UNC-79 and UNC-80 exert similar effects on the expression and localization of the NCA channel ( Jospin et al., 2007; Yeh et al.,

2008), and mUNC-80 couples the NALCN channel conductivity with an intracellular signaling cascade ( Lu et al., 2010). In C. elegans, the loss of either UNC-79 or UNC-80 suppresses and reverts the coiler phenotype exhibited by nca(gf) PD0332991 cell line to that of fainters ( Yeh et al., 2008). unc-79 and unc-80 mutants exhibit a fainter phenotype identical to that of nca(lf) mutants. The loss of either UNC-79 or UNC-80 causes a reduced localization of NCAs along the axon. UNC-79 and UNC-80 also localize along the axon, but only in Adenylyl cyclase the presence of NCAs, implicating their copresence in a channel complex ( Jospin et al., 2007; Yeh et al., 2008). Indeed, mouse mUNC-79 and mUNC-80 coimmunoprecipitated with NALCN (

Lu et al., 2010). Identifying genetic suppressors of nca(gf) therefore effectively reveals subunits or effectors of this new channel. Through genetic suppressor screens for nca(gf), we identified another recessive, loss-of-function suppressor, nlf-1, that rescues the coiler phenotype exhibited by nca(gf) animals. Below, we present molecular, biochemical, electrophysiological, calcium imaging and behavioral analyses on nlf-1 and nca that demonstrate (1) NCA contributes to a Na+ leak current in C. elegans neurons; (2) NLF-1 is an ER resident protein that specifically promotes axon delivery of the NCA Na+ leak channel; (3) NCA/NLF-1-mediated Na+ leak current maintains the RMP and potentiates the activity of premotor interneurons to sustain C. elegans’ rhythmic locomotion; (4) a mouse homolog mNLF-1 is functionally conserved with NLF-1 in vivo, and physically interacts with the mammalian Na+ leak channel NALCN in vitro. We isolated a recessive, loss-of-function mutation allele (hp428) of the nlf-1 gene that suppresses the behavioral phenotypes of nca(gf) mutants.

One such model (Clopath et al , 2010, built on earlier work by Pf

One such model (Clopath et al., 2010, built on earlier work by Pfister and Gerstner, 2006) is based on interaction of presynaptic spikes with instantaneous and time-filtered postsynaptic membrane potential. At the synapse level, the model predicts the timing, rate and voltage-dependence of plasticity. On the network level, this learning rule stores information about both slow input correlations and rapid spatiotemporal sequences, depending on the structure of spike train input, thus capturing functional aspects of rate-dependent plasticity and STDP (Clopath et al., 2010). Hebbian STDP at glutamatergic synapses is mediated http://www.selleckchem.com/products/ulixertinib-bvd-523-vrt752271.html by

the same three signaling pathways that mediate most classical, correlation-dependent LTP and LTD. These are as follows: (1) NMDA receptor (NMDAR)-dependent LTP and (2) NMDAR-dependent LTD, in which correlated presynaptic release and postsynaptic depolarization trigger calcium influx through postsynaptic NMDARs (and voltage-sensitive calcium channels, VSCCs). LTP versus LTD induction is determined by the magnitude and time course of calcium flux, with brief, high calcium-generating LTP, sustained moderate calcium-generating LTD, and low calcium-inducing

no plasticity (Lisman, 1989; Yang et al., 1999). The primary expression mechanisms are postsynaptic, via addition or removal of postsynaptic AMPA receptors (AMPARs) and changes in single-channel conductance Carfilzomib mw (Malinow and Malenka, 2002), though presynaptic expression can also occur. (3) Metabotropic glutamate receptor (mGluR)-dependent and/or cannabinoid type 1 receptor (CB1R)-dependent LTD, in which postsynaptic NMDARs are not involved, and LTD is expressed via a decrease in presynaptic transmitter release probability. This form is heterogeneous. In CB1R-dependent LTD, which is linked most strongly

to STDP, postsynaptic calcium and mGluR activation trigger dendritic synthesis of endocannabinoids, which diffuse retrogradely to activate CB1Rs on the presynaptic terminal and drive a long-lasting decrease Electron transport chain in release probability (Chevaleyre et al., 2006). Other forms of mGluR-LTD are CB1R-independent and postsynaptically expressed but are less linked to STDP. STDP is mediated by these three mechanisms, with postsynaptic spikes providing a critical component of postsynaptic depolarization for plasticity. There are two major, biochemically distinct forms of Hebbian STDP. One is composed of NMDAR-dependent LTP and NMDAR-dependent LTD (Figure 4A, left). This occurs at CA3-CA1 hippocampal synapses and some synapses on neocortical L2/3 pyramidal cells (Nishiyama et al., 2000; Froemke et al., 2005). Here, the magnitude of the NMDAR calcium signal determines the sign of plasticity (along with calcium from VSCCs) (Lisman, 1989).

At the time of choice, the AI might signal cue negative value (i

At the time of choice, the AI might signal cue negative value (i.e., punishment prediction),

which could drive avoidance behavior. This is in line with theories proposing that brain areas involved in somatic affective representations are causally responsible for making a choice (Jones et al., 2010; Naqvi and Bechara, 2009; Craig, 2003). The flattened punishment-learning curves following DS preferential atrophy in presymptomatic HD patients was specifically captured by a higher choice randomness. Contrary to reinforcement magnitude and learning rate, this parameter impacts the choice, not the learning process. This is consistent with our fMRI finding that the DS was active at punishment cue display (during choice period), but not at outcome display (during learning period). It accords well with the idea that the DS is the “actor” SCH727965 cost part of the striatum, the “critic” part being more ventral (O’Doherty et al., 2004; Atallah et al., 2007). Indeed, the transition from presymptomatic to symptomatic HD, which was characterized by degeneration extending to the VS, was captured by a lower reinforcement magnitude in the gain condition. Thus the VS, which is closely linked to the VMPFC, would play a role similar to that of the insula, but for learning positive instead of negative values. This is in line with studies implicating the VS and VMPFC in encoding both reward predictions

at cue display and reward prediction errors enough at outcome display (Rutledge U0126 et al., 2010; Palminteri et al., 2009a; Hare et al., 2008). However, interpreting the specific role of the DS in choosing between aversive cues remains speculative. The link with choice randomness might suggest that the DS is involved in comparing

negative value estimates or in integrating the precision of these estimates, or in adjusting the balance between exploration and exploitation. Another possibility is that the DS is specifically involved in avoidance behavior, i.e., in inhibiting the selection of the worst option and facilitating the selection of alternatives. This interpretation is endorsed by the observation that input connections to the caudate head come from dorsal prefrontal structures, which have been implicated in inhibitory and executive processes (Draganski et al., 2008; Haber, 2003; Postuma and Dagher, 2006). In conclusion, we found evidence that the AI and DS are causally implicated in punishment-based avoidance learning, but for different reasons. The AI might participate by signaling punishment magnitude, in accordance with its involvement in negative affective reactions, whereas the DS might participate by implementing avoidance choices, in accordance with its involvement in executive processes. These findings suggest the existence of a distinct punishment system underpinning avoidance learning, just as the reward system underpins approach learning.

In this study, we show that dopamine depletion causes a target-sp

In this study, we show that dopamine depletion causes a target-specific reorganization of the feedforward inhibitory circuit through selective enhancement of FS connections to D2 MSNs. A simple model of the striatal microcircuit suggests

that this pathway-specific increase in connectivity is sufficient to augment firing synchrony between indirect-pathway projection neurons, thus implicating reorganization of FS microcircuits in striatal Enzalutamide dysfunction in PD. In the striatum of 6-OHDA-injected mice, we find that dopamine depletion causes an increase in FS innervation of D2 MSNs, driven by sprouting of FS axons. This was confirmed anatomically by reconstructions of FS interneurons and immunohistological analysis of presynaptic puncta, and functionally by paired recordings showing increased FS-D2 MSN connectivity and increased mIPSC frequency selectively in D2 MSNs. These results demonstrate that dopamine depletion can induce a target-specific remodeling

of FS innervation, which is both rapid (observed within 3 days) and persistent (observed at 4 weeks). This target-specific plasticity may represent a homeostatic response to D2 MSN hyperactivity after dopamine depletion. Within hours to days after dopamine depletion, D2 MSNs this website show increased excitability (Fino et al., 2007, Mallet et al., 2006 and Nicola et al., 2000), accompanied by reduced spine density (Day et al., 2006) and collaterals between both MSN subtypes (Taverna et al., 2008). The hyperactivity of MSNs in the indirect pathway could trigger

compensatory upregulation of inhibition from FS 3-mercaptopyruvate sulfurtransferase interneurons, reminiscent of compensatory sprouting observed by some types of GABAergic interneurons in epilepsy (Bausch, 2005, Davenport et al., 1990, Klaassen et al., 2006 and Palop et al., 2007). Indeed, previous studies have demonstrated that structural plasticity of GABAergic interneurons can occur within hours or days (Chen et al., 2011 and Marik et al., 2010). The mechanisms of compensatory sprouting of inhibitory axons have long remained enigmatic (Valdes et al., 1982). In the hippocampus a subset of inhibitory inputs is selectively strengthened by reductions in endocannabinoid (eCB) signaling (Kim and Alger, 2010), and in the striatum, reduced eCB-dependent LTD in D2 MSNs is thought to contribute to increased drive on the indirect pathway following dopamine depletion (Kreitzer and Malenka, 2007). However, it does not appear that eCBs in the striatum contribute to compensatory sprouting of FS interneurons because we did not observe changes in amplitude and short-term plasticity of IPSCs as described by Kim and Alger, 2010. Alternatively, BDNF signaling through TrkB receptors has also been shown to regulate sprouting of inhibitory axons (Huang et al., 1999, Peng et al., 2010, Rutherford et al., 1997, Seil and Drake-Baumann, 2000 and Swanwick et al.

β2m protein, the light

chain that is coexpressed with MHC

β2m protein, the light

chain that is coexpressed with MHCI molecules (Zijlstra et al., 1990), is also elevated after MCAO, implying that there is an increase in stable cell-surface expression of MHCI protein. Because PirB expression, phosphorylation, and its interaction Alectinib with SHP-2 are also increased, these observations argue mechanistically for an increase in signaling cascades downstream of the PirB receptor. Together, these experiments identify a set of molecules that, when present, exacerbate damage caused by stroke and, when removed, permit more extensive recovery. The greater recovery in PirB versus KbDb KO mice fits well with a model in which PirB binds not only Kb and Db, but also other ligands. In addition to classical MHCIs, PirB is also thought to bind Nogo (Atwal et al., 2008) and to collaborate with the Nogo receptor (NgR), which itself cannot signal (Fournier et al., 2002). Mice lacking Nogo or NgR, like PirB mice, have enhanced synaptic plasticity (McGee et al., 2005), and blocking NgR function also enhances recovery after MCAO (Lee et al., 2004). Thus, deletion of PirB would be expected to have a larger effect than deleting only a subset

of ligands. It will be worthwhile to explore PirB interaction with other ligands as well as receptors in the context of neuroprotection from stroke. An important implication of the findings reported here is that new avenues of therapy after stroke may be available, because PirB in humans has only a limited number of homologs, members of the LILRB

Protease Inhibitor Library screening family (Takai, 2005). As a key step, it will be necessary to explore whether acute blockade of PirB or LILRBs can also lead to neuroprotection. After stroke, neurons in undamaged cortical regions extend their axons into damaged regions and become responsive to motor or sensory functions perturbed by injury (Lee et al., 2004 and Netz et al., 1997). In PirB KO mice, an increased number of midline crossing fibers from the undamaged corticospinal tract were seen extending into the denervated red nucleus 28 days post-MCAO. These observations support previous studies showing that PirB and MHCI ligands limit axonal outgrowth in development and regeneration after injury in vitro and in vivo (Atwal et al., 2008, Fujita et al., 2011, Washburn Adenosine et al., 2011 and Wu et al., 2011). In vivo, PirB downstream signaling inhibits Trk receptors that function to promote axonal outgrowth; KO of PirB increases TrkB signaling and neurite outgrowth after optic nerve injury (Fujita et al., 2011). However, our results contrast with recent studies that report no difference in PirB KO CST axonal projections using a traumatic brain or spinal cord injury model (Nakamura et al., 2011 and Omoto et al., 2010). Note that these studies used entirely different injury paradigms as well as a different PirB KO mouse.

This was largely due to the availability of samples within the su

This was largely due to the availability of samples within the survey which had sufficient germinative energy to malt and Everolimus nmr which showed interesting variations with regard

to their measured concentrations of fungal DNA and mycotoxins. In general the malts prepared were of acceptable specification (although precise requirements depend on the end user). If anything, the majority of malts were rather well modified (friability > 90% and with high α-amylase activities), which was a result of the generous 50 h steep cycle, designed to ensure that barley samples of differing provenance would all hydrate and modify sufficiently. Water sensitivity is defined as the difference between the GE (4 ml) and GE (8 ml) counts. The number (expressed as a percentage) indicates whether a malt sample has lower germinative energy in the presence selleckchem of excess water. In the present study, both M. nivale and F. poae were significant factors which correlated positively with water sensitivity. Crop year was also a significant factor in determining water sensitivity, with 2011 samples having on average, greater water sensitivity than those from 2010. Water sensitivity is of commercial significance because the maltster will need to adjust the steeping process (e.g. the duration of air rests) when malting water sensitive grain.

Water sensitivity has been linked to malt microflora ( Woonton et al., 2005) although other factors seem to be involved, as treatment of grains with anti-microbial agents does not consistently overcome water sensitivity ( Kelly and Briggs, 1992). The fact that water sensitivity was also affected by crop year could be caused by differences in climatic/agronomic influences during the respective years. It could also reflect the fact that on average more fungal DNA was found in the 2011 samples for the two species identified as being significant in the model for water sensitivity (0.027 pg/ng as compared with 0.015 pg/ng for F. poae and 0.37 pg/ng versus 0.19 pg/ng for Oxalosuccinic acid M. nivale). There was

a positive correlation of F. poae with wort FAN suggesting that F. poae contributes to proteolytic activity through the malting and mashing processes, thus increasing FAN production, particularly during the low temperature stand at 45 °C during the congress mash schedule. The model for wort FAN also included F. langsethiae and an interaction term between the two species. The interaction indicated that at low concentrations of F. langsethiae, F. poae dominated with regard to increasing wort FAN, whereas at high F. langsethiae concentrations and low F. poae, the contribution to FAN from F. langsethiae was significant. The trends found in the interactions of F. poae, F. langsethiae and wort FAN may reflect competitive aspects between the growth habits of these two species. These results are consistent with prior reports of protease secretion by F. poae ( Pekkarinen et al., 2000 and Schwarz et al., 2002). Pekkarinen et al. (2000) reported that F.

The second argument is concerned with amplifications of the first

The second argument is concerned with amplifications of the first argument that can occur when systems are not modeled at their inherent levels of organization, such as when brains (cortically organized at levels of columns,

areas, and systems [Churchland and Sejnowski, 1988 and Felleman and Van Essen, 1991]) are modeled as voxels (an arbitrary volumetric element). Since some classic methods of hub identification are confounded in correlation networks, we develop two alternative methods for identifying Nintedanib cost hubs that are more suited to RSFC correlation networks. Both methods aim to identify regions of the brain that are well-situated to support and/or integrate multiple types of information. Both methods leverage the correspondence between functional brain systems (e.g., dorsal attention system) and graph subnetworks

observed in recently described RSFC graphs (Power et al., 2011; see also Yeo et al., 2011). First, using a model of the brain at the level of functional areas, we identify nodes that participate in many subnetworks of the brain (e.g., a node that has relationships with members of multiple brain systems, such as visual, default mode, or frontoparietal control systems). These nodes www.selleckchem.com/products/Cisplatin.html are candidate brain hubs. We identify these candidate hubs using the established measure of participation coefficients (Guimerà and Nunes Amaral, 2005). Second, we examine a high-resolution brain network to identify spatial locations where many subnetworks are present within a small volume (e.g., finding, within a small sphere, voxels representing the dorsal attention, visual, frontoparietal control, and default mode systems). We call these locations articulation points—they are not hubs in the traditional graph theoretic sense, but they are locations where such hubs might be situated. Both methods identify similar sets of brain regions in the anterior insula, anterior, middle and superior frontal cortex, medial either superior frontal

cortex, medial parietal cortex, inferior parietal, and temporo-occipital cortex. Notably, these regions do not emphasize the default mode system. Several influential reports have identified brain hubs in RSFC networks using (variations of) a measure called degree (or degree centrality), which is the number of edges on a node (Buckner et al., 2009, Cole et al., 2010, Fransson et al., 2011, Tomasi and Volkow, 2010, Tomasi and Volkow, 2011 and van den Heuvel et al., 2008). Hubs, when identified by high degree, are nodes with many edges. In weighted networks, the analogous measure, strength, is defined as the sum of the weights of the edges on a node. Degree (or strength) is usually an appropriate measure for identifying hubs (e.g.

This then raises the distinct possibility that presynpatic releas

This then raises the distinct possibility that presynpatic release of glutamate, which then engages postsynaptic NMDARs on AgRP neurons, somehow

initiates the spinogenesis. Neratinib purchase Consistent with this, previous studies have found that dendritic spinogenesis occurs in response to evoked synaptic glutamate release (Engert and Bonhoeffer, 1999 and Maletic-Savatic et al., 1999) and also, very rapidly, following focal glutamate uncaging onto dendritc shafts (Kwon and Sabatini, 2011). As was true with fasting, these studies similarly found a requirement for NMDARs. Combined, these observations suggest that glutamatergic afferents to AgRP neurons are very likely to play important roles in activating AgRP neurons—in promoting spinogenesis via release of glutamate that then engages NMDARs on AgRP neurons, in providing presynaptic partners for the new dendritic spines and, finally, in providing sustained activation of the nascent synapses. The marked effects caused by removing NMDARs from AgRP

neurons reviewed above, in combination with the presence of dendritic spines on AgRP neurons but not on nearby POMC neurons, strongly suggests that glutamatergic neurotransmission, and its regulation via NMDARs, along with signaling events that are confined within dendritic spines, play key roles in regulating the activity of AgRP neurons. Dendritic spines, and the signaling within, serve Anti-diabetic Compound Library research buy three major functions, and each of these has important Levetiracetam implications for mechanisms regulating AgRP neurons and feeding behavior. First, spines are the sites where excitatory afferents are received. Given that AgRP neurons drive feeding behavior (Aponte et al., 2011 and Krashes et al., 2011), these excitatory afferents must, by extension, be key, but presently unknown, drivers of food intake. Identifying these

excitatory afferents should shed new light on neural circuits regulating feeding. Second, NMDARs/spines are mediators of plasticity (i.e., changes in strength of glutamatergic transmission). Given this, it is likely that mechanisms of plasticity, including long term potentiation, long term depression, and dendritic spinogenesis (Collingridge et al., 2010, Engert and Bonhoeffer, 1999, Kessels and Malinow, 2009, Kwon and Sabatini, 2011, Malenka and Nicoll, 1999 and Maletic-Savatic et al., 1999), play important roles in controlling feeding behavior. Third, and of special relevance to hypothalamic neurons, dendritic spines serve as communication hubs where other “inputs” are integrated for the purpose of acutely and chronically modulating glutamatergic transmission. Notable examples of this include dopaminergic and cholinergic modulation, respectively, of glutamatergic transmission in the striatum (Kreitzer and Malenka, 2008) and hippocampus (Buchanan et al., 2010 and Giessel and Sabatini, 2010).

Such information will not only provide fundamental insights into

Such information will not only provide fundamental insights into how the AIS affects AP generation and information processing in neurons, but may also open new avenues for targeted therapies to treat neurological disorders. “
“Neuropeptides are expressed and secreted throughout the mammalian brain, typically in combination with a fast neurotransmitter such as glutamate or GABA (Hökfelt et al., 2000). Neuropeptides are packaged in vesicles and several are known to be released this website in an activity-dependent manner (Ludwig and Leng, 2006). Neuropeptide expression is often regulated by neuronal

activity and many neurons are classified by their selective expression of different neuropeptides and neuropeptide receptors (Hökfelt et al., 2000). Such regulated and heterogeneous expression of neuropeptides suggests a precise function in neuron-to-neuron signaling. Indeed, many aspects of synapse and cell function are modulated XAV-939 concentration by neuropeptide-dependent activation of G protein-coupled receptors (GPCRs) (Strand, 1999 and Tallent, 2008). At the behavioral level, neuropeptides have profound and complex neuromodulatory effects on brain function: they regulate social bonding (Insel, 2010), feeding (Morton et al., 2006), sleep (Adamantidis et al., 2010), aversion (Knoll and Carlezon, 2010),

and reward (Le Merrer et al., 2009). Studies into neuropeptide systems have been limited by a paucity of experimental tools. The conditions that trigger neuropeptide release from neurons are largely unknown and currently available Parvulin methods of activating neuropeptide receptors in brain tissue prevent quantitative studies of their function. Although small-molecule agonists for many neuropeptide receptors are available, many GPCRs exhibit functional selectivity such that they are incompletely or unnaturally activated by synthetic ligands (Urban et al., 2007). Furthermore, neuropeptides can bind and activate multiple receptor subtypes present on the same cell with similar affinities (Lupica et al., 1992 and Svoboda et al.,

1999). Thus, exogenous application of peptide ligands, rather than synthetic agonists, more accurately mimics endogenous peptidergic signaling. However, compared to traditional pharmacological agents, peptides are large, hydrophobic molecules and thus diffuse slowly within the brain. Direct peptide application in vivo and in brain slices by perfusion, pressure injection (Williams et al., 1982), or iontophoresis (Travagli et al., 1995) produces a slowly rising, prolonged, and spatially imprecise presentation of the peptide. These methods offer poor control over the concentration of peptide delivered, largely limiting quantitative analysis to the effects of saturating doses for consistency (Duggan and North, 1983).

In addition, in all six monkeys several regions were reproducibly

In addition, in all six monkeys several regions were reproducibly more active to Shapes (both Learned symbols and Untrained shapes) than to Faces (conjunction of L > F AND U > F contrast maps) (Figure 4, Figure 5 and Figure 6, green RO4929097 mw patches). Three Shape-selective regions (s1, s2, s3, posterior to anterior) were consistent between the two hemispheres for each monkey, so we again

averaged the two hemispheres together to project each monkey’s Shape selectivity maps onto a common hemisphere (Figure 6, green patches). Again, by inspection of Figure 6, several regions are commonly Shape selective. The maximally selective voxels in each of the three largest Shape selective regions for each monkey are listed in Table S1. The posterior-most Shape patch (s1) was consistently localized ventral and slightly posterior Fasudil research buy to Face patch f1 in posterior area TEO or in anterior V4, at the anterior tip of IOS, with maximal overlap at A2. The middle Shape patch (s2) extended from the bank of the STS near the anterior tip of PMTS out onto the inferotermporal gyrus, maximal overlap at A4 mostly within area TEpd or area TEO. The anterior most Shape patch (s3) was less consistent between monkeys; it was located in TEa/TEm, varying in position from A12 to A16. Shape selective regions that are distinct

from Face selective patches have also been previously described (Denys et al., 2004 and Sawamura et al., 2005). In all six monkeys, the relative category-selective regions formed three pairs of regions more responsive to Faces than to Symbols (Learned and Untrained) or the reverse, distributed along the inferotemporal gyrus (Figure 6A). The locations of the two posterior pairs of patches roughly correspond to the borders between the major subdivisions of the ventral temporal lobe (Boussaoud et al.,

1991, Desimone and Ungerleider, 1989 and Saleem and Logothetis, 2007)—V4/TEO and TEO/TE (Figure 6A). The anterior patches may be located at the TE/TG border, but their position was too variable to really say. Because already our stimuli covered only the central visual field, the patches may correspond to foveal confluences between areas (Kolster et al., 2009). Alternating face, body, and object selective regions have been described previously in macaque temporal lobe (Bell et al., 2009, Denys et al., 2004 and Op de Beeck et al., 2008) and have been proposed to represent alternating regions selective for animate versus inanimate categories (Bell et al., 2009 and Op de Beeck et al., 2008). Our results are consistent with this hypothesis, and in one of our monkeys we confirmed that the regions activated by Shapes > Faces were also selectively activated by images of inanimate objects (data not shown).