We thank Heather Murray for expert technical assistance; Dr. Graham Knott (Center for Interdisciplinary Electron Microscopy; EPFL) for help and advice with EM; Dr. Daniel Keller for help with flat surface rendering of active zone profiles; Dr. Patrick Charnay and Dr. Hans Jörg Fehling for the gift of mouse lines; and Dr. Olexiy Kochubey, Dr. Erwin Neher, and Dr. David Perkel for comments on the manuscript. This research was supported by grants from the Swiss National Science Foundation (SNF; 31003A_122496) Palbociclib solubility dmso and the Synapsis foundation (both to R.S.). “
“Neurotransmission

is initiated when synaptic vesicles undergo exocytosis at the active zone, thereby releasing their neurotransmitter contents (Katz, 1969). Synaptic vesicle exocytosis is highly regulated, consistent with its role as the gatekeeper of neurotransmission (Stevens, 2003). Each event of exocytosis is induced by an action potential that induces Ca2+ influx via Ca2+ channels located in or near the active zone. The efficacy of action-potential-induced exocytosis depends on at least three parameters: the local activity of voltage-gated Ca2+ channels, the number of release-ready vesicles, and the Ca2+ sensitivity of these vesicles. Remarkably, none of the proteins that mediate these parameters (i.e., Ca2+ channels, the presynaptic

fusion machinery composed of SNARE and SM proteins, and the Ca2+ sensor synaptotagmin) is exclusively Metformin molecular weight localized to the active zone. Instead, their functions are organized at presynaptic release sites by the protein components of active zones (Südhof, 2004 and Wojcik and Brose, 2007). Among active

zone protein components, RIM proteins are arguably only the most central elements (Mittelstaedt et al., 2010). RIMs directly or indirectly interact with all other active zone proteins (Wang et al., 2000, Wang et al., 2002, Betz et al., 2001, Schoch et al., 2002, Ohtsuka et al., 2002 and Ko et al., 2003), Ca2+ channels (Hibino et al., 2002, Kiyonaka et al., 2007 and Kaeser et al., 2011), and the synaptic vesicle proteins Rab3 and synaptotagmin-1 (Wang et al., 1997, Coppola et al., 2001 and Schoch et al., 2002). Consistent with a central role for RIMs in active zones, RIM proteins are essential for presynaptic vesicle docking, priming, Ca2+ channel localization, and plasticity (Koushika et al., 2001, Schoch et al., 2002, Schoch et al., 2006, Castillo et al., 2002, Calakos et al., 2004, Weimer et al., 2006, Gracheva et al., 2008, Kaeser et al., 2008, Kaeser et al., 2011, Fourcaudot et al., 2008 and Han et al., 2011). However, apart from recent progress in understanding the role of RIMs in vesicle docking and in localizing Ca2+ channels to active zones (Gracheva et al., 2008, Schoch et al., 2006, Kaeser et al., 2008, Kaeser et al., 2011 and Han et al., 2011), it remains unclear how RIMs perform their functions.

Moreover, combining FGF-2 and 5-HT1A agonist synergistically enhanced both receptor signaling and cell differentiation, suggesting a trophic role in the serotonergic neurons (Borroto-Escuela et al., 2012b). Given the large literature on 5HT1A receptor signaling (Hannon and Hoyer, 2008), and its role in mediating the mode of action of antidepressants and in the regulation of emotional responsiveness (Blier and Abbott, 2001; Blier DNA Damage inhibitor and Ward, 2003), the molecular interaction between these two systems opens up exciting avenues

for understanding the biology and pathophysiology of affect and mood. In addition, since both FGFR1 and 5HT1A receptors are known to be present on neural stem cells, their interplay in modulating neurogenesis, e.g., upon antidepressant treatment or with environmental complexity or exercise, is of great interest. These two examples of interaction with G-Protein coupled receptors greatly expand the range of potential influence of the FGF system on neuronal signaling and the control of growth and differentiation. Such interactions might exist in other brain regions, and possibly with other G protein-coupled receptors, and couple the FGF control of neuroplasticity more directly to the actions of specific neurotransmitters. The body of work summarized

here underscores the surprising role of the FGF family not only in controlling neural development and neuroplasticity, but also in modulating many facets of emotional and motivated behavior. Equally notable is the ABT-199 datasheet fact that this modulation occurs

in multiple time domains, with early effects lasting into adult life, but also with evidence for “on-line” control of signaling and behavioral responsiveness during adulthood. mafosfamide It should be mentioned that other growth factors, such as BDNF and IGF-1, have similar neuromodulatory effects as FGF2. For example, both molecules promote neurogenesis and act as antidepressants (Anderson et al., 2002; Hoshaw et al., 2005; Schmidt and Duman, 2010). BDNF is also upregulated following antidepressant drug treatment and has long-lasting effects on hippocampal function (Monteggia et al., 2004; Nibuya et al., 1995). However, FGF2 has effects on glial cells, specifically astrocytes, which have not been shown for BDNF or IGF-1 (Numakawa et al., 2011). One of these functions includes upregulating microRNAs, where BDNF and IGF-1 failed to do so. Given that depression may be related to a perturbation in glia, this may represent a significant difference between growth factor families (Bernard et al., 2011; Choudary et al., 2005). Finally, FGF receptors can interact with other neurotransmitters, and this has the potential for FGF ligands to have multiple and rapid cellular and behavioral effects. The FGF family appears to reside at the interface of genetic, developmental, environmental, and experiential regulation of mood, affect, and addiction.

Single-subject beta maps were generated for each of five stimulus conditions, which were then used to assess between-group differences

in function using analyses of covariance (ANCOVAs). Participant group (i.e., tinnitus patients versus controls) and mean hearing loss (mHL) were entered as a between-subject factor and covariate, respectively. Single-voxel thresholds were chosen (p < 0.001); maps were then corrected for cluster volume at p(corr) < 0.05 using Montecarlo simulations (a means of estimating the rate of false positive voxels; Forman et al., 1995). Single-voxel thresholds were reduced to p(uncorr) < 0.01, k > 108 mm3 in masked analyses (below). Single-voxel GLM analyses assessed anatomical differences between www.selleckchem.com/screening-libraries.html tinnitus patients and controls, with compensation for unequal variance between groups in SPM8. t tests were performed across groups, and both age and total gray or white matter Doxorubicin clinical trial volume were entered as confound covariates. A single-voxel (i.e., voxel-wise) threshold was chosen of t > 4.65, p < 0.0001; cluster volume was greater than 80 mm3. Single-voxel thresholds were reduced to p < 0.01 in masked analyses. All single-voxel VBM analyses were performed in the same resolution as the tissue probability maps used for segmentation (2 × 2 × 2 mm3). A mask of the auditory system was created for both functional

Suplatast tosilate and anatomical analyses. Auditory cortex was defined by selecting those functional voxels in superior temporal cortex that

survived a sounds > silence contrast with a single-voxel threshold of t > 2.58, p(uncorr) < 0.01, k > 4 (group data). The MGN were defined using the WFU Pick Atlas ( Lancaster et al., 2000 and Maldjian et al., 2003), dilated by 1 mm, and then flipped to create a symmetrical mask in both hemispheres. Additional masks were created using significant clusters from both functional and anatomical analyses. Masks were transferred between programs via image files (ANALYZE format), which were then adjusted to the appropriate format in BrainVoyager or SPM. Coordinate conversions between Talairach and MNI spaces were done using a well-accepted nonlinear transform (http://imaging.mrc-cbu.cam.ac.uk/imaging/MniTalairach). Pairwise correlations between mean fMRI signal or VBM values were performed for ROIs exhibiting significant between-group differences using the statistical tests described above. Cook’s d tests were used to assess the influence of potential outliers on the resulting correlation statistics. Data points from a single participant, Patient #7, had Cook’s d values close to 1.0 (a commonly used benchmark for identifying potential outliers) for four out of six pairwise tests (Table S3). Therefore, we computed correlations both with and without this subject included.

Such “myelinated nociceptors”

conduct in the Aβ range and respond to mechanical stimuli well into the nociceptive range, with a graded NU7441 research buy fashion and adaptive properties that resemble SAII units (Burgess and Perl, 1967, McIlwrath et al., 2007 and Woodbury and Koerber, 2003). Under normal conditions, myelinated nociceptors are also sensitive to innocuous mechanical stimuli, with von Frey thresholds as low as 0.07 mN. Some myelinated nociceptors also respond to noxious heat but are otherwise physiologically indistinguishable from their heat-insensitive counterparts (Treede et al., 1998). Because of their wide dynamic range, myelinated nociceptors are likely to serve both LTMR and nociceptive functions. Myelinated nociceptors can be found both in glabrous and hairy skin, although their anatomical morphologies NSC 683864 remain unknown. Proper identification and differentiation of Aβ-LTMRs versus Aβ-nociceptors will be critical to our understanding of pain states such as allodynia and hyperalgesia. Indeed, it has been suggested that tactile

allodynia after peripheral nerve injury is due to impulses carried along residual A fibers in the presence of dorsal horn sensitization (Campbell et al., 1988, LaMotte and Kapadia, 1993 and Woolf et al., 1992). However, it is possible that myelinated nociceptors mediate certain aspects of tactile allodynia, as they are quite sensitive to mechanical stimuli and are known to innervate lamina in the dorsal horn normally associated with nociception (Woodbury et al., 2008). Furthermore, decreases of mechanical thresholds in myelinated nociceptors after peripheral injury, as is the case with other nociceptors, may also contribute to pain states such as allodynia (Andrew and Greenspan, 1999 and Jankowski

et al., 2009). The anatomical substrate of our tactile perceptions lies in the intricate innervation patterns of physiologically distinct LTMRs and HTMRs and their respective end organs located in the skin. Each unique form, be it a rigid set of LTMR palisades surrounding hair follicles or a free nerve ending associated with keratinocytes, represents a distinct sensory unit that is uniquely tuned to a particular feature of our tactile world. Most of what we know of touch perception comes from studies whatever on glabrous skin of the primate hand or the rodent paw. Here, conceptual leaps in the interpretation of sensory neuron form and function have distilled the essence of touch perception into four main anatomical and physiological “channels,” which transduce mechanical signals into neural codes of rapidly adapting and slowly adapting impulses. Although there is no doubt that tactile information travels along these four channels, at least peripherally, the recently revealed patterns of hairy skin innervation urge us to consider a much more integrative view of touch perception.

5 μM tetrodotoxin. All drugs were obtained from Sigma or Tocris (UK). Chemicals were applied extracellularly by bath superfusion. Living neurons expressing eGFP were visualized in brain slices using an Olympus BX50WI upright microscope equipped with oblique illumination optics, a mercury lamp, and eGFP excitation and emission filters. Somatic recordings were carried out at 37°C using an EPC 10 patch-clamp

amplifier (HEKA Elektronik, Germany). Patch pipettes were made from borosilicate glass, and their tip-resistances ranged from 3 to 8 MΩ (3–5 MΩ with high-Cl and 5–8 MΩ with low-Cl pipette solution). Slices were placed in a submerged-type chamber (volume ∼2 ml, solution flow rate 2.5 ml/min) and anchored with a nylon string grid. Only cells with access resistances between 10 and 25 MΩ were accepted selleck products for analysis. In experiments where change in membrane potential was quantified, all cells were initially held at the same potential to facilitate see more comparison (−50 mV when depolarization was quantified, and

−40 mV when hyperpolarization was quantified), by applying a fixed holding current throughout experiment. Data were sampled and filtered using Pulse and Patchmaster software (HEKA Elektronik, Germany). Current-voltage (I-V) relationships were obtained by performing voltage-clamp ramps from −20 to −130 mV at a rate of 0.1 mV/ms ramp, which is sufficiently slow to allow leak-like K+ currents to reach steady state at each potential (Meuth et al., 2003). In cell-attached the mode (Figure 1H), the patch pipette was filled with ACSF and action potential frequency was measured in voltage-clamp at a command potential under which the holding current is 0 pA (Perkins, 2006). Breaks in some current-clamp traces correspond to moments when the recording

was paused (e.g., to take voltage-clamp measurements or inject cell with current for measurement of input resistance). In some current-clamp experiments (e.g., Figures 1D and 4A) the cells were periodically injected with hyperpolarizing current pulses to monitor membrane resistance. Statistical analyses were performed using Origin (Microcal, Northampton, MA) and Microsoft Excel (Microsoft, Redmond, WA) software. Averaged data are presented as mean ± SEM. Statistical significance was tested using the Student’s t test unless indicated otherwise. The following modified Hill equation was fitted to the data in Figures 1G and 3C: V=Rmax[AA]hEC50h+[AA]hwhere Rmax is the maximal change in membrane potential, EC50 is the concentration that gives half-maximal response, and h is the Hill coefficient. The fit shown in Figure 1G was obtained with Rmax = 20.4 mV, h = 1.79 and EC50 = 438.2 μM. The fit shown in Figure 3C was obtained with Rmax = 950.6 pA, h = 2.39, and EC50 = 3.19 mM. Subjects were 14-week-old C57BL/6 male mice (Charles River). The mice were maintained on a standard 12 hr light-dark cycle (lights on at 0700 hr).

, 2005 and Martinez-Trujillo and Treue, 2004). Neurons in area V4, for example, show enhanced responses to stimuli within LY294002 purchase their receptive fields (RFs) during visual search when they contain a color or shape feature that is shared with the searched-for target (Chelazzi et al., 2001), even when the animal is planning an eye movement (and, thus, directing spatial attention) to another stimulus

in the search array (Bichot et al., 2005). Thus, feature-selective attentional enhancement appears to occur in parallel across the visual field representations of extrastriate visual areas and presumably helps guide the eyes to searched-for targets. Although extrastriate neuronal responses are modulated by feature attention, to our knowledge, the source of the top-down feedback that biases responses in favor of the attended feature is unknown. During spatial attention, there is evidence that the response enhancement with attention observed in extrastriate visual areas results from top-down feedback from areas such as the frontal eye field (FEF) and lateral intraparietal area (LIP) (Desimone and Duncan, 1995, Gregoriou et al., 2009, Kastner and Ungerleider, 2000 and Serences and Boynton, 2007). Electrical stimulation of the FEF causes enhancement BMS-354825 chemical structure of V4 responses and activation

of the cortex measured by fMRI, similar to what is found during spatial attention (Ekstrom et al., 2008 and Moore and Armstrong, 2003), and neurons Metalloexopeptidase in the FEF and V4 synchronize their activity with each other in the gamma frequency range during spatial attention (Gregoriou et al., 2009). However, to our knowledge, whether these areas play the similar role during feature-based attention is still unknown. Like neurons in area V4, neurons in the FEF and LIP also show enhanced responses

to targets (or distracters that share features with the targets) compared to dissimilar distracters in their RFs, even when these stimuli are not selected for the next saccade during visual search (Bichot and Schall, 1999 and Ipata et al., 2009). This suggests that the responses of FEF and LIP neurons to stimuli in their RFs are influenced by the target features in parallel across the visual field, independently of spatial attention. However, the target stimuli used in these studies were fixed, at least within the same session, raising the possibility that the parallel effects of target features on responses arose from learning effects rather than flexible feature attention mechanisms. Learning effects on target responses have been found in prior studies in the FEF (Bichot et al., 1996). Indeed, one recent study of FEF neurons with a target that changed from trial to trial during visual search found that cells exhibited a serial shift of spatial attention effects from one stimulus to another in the search array, rather than parallel, feature attention effects (Buschman and Miller, 2009).

In spite of the fact that our data was taken from brain slices, where many connections have been sectioned, we detect many instances where the connectivity is so dense that it approaches sampling every potential presynaptic input (Figure 8F). Since we randomly chose PCs Docetaxel concentration and also found this dense innervation in pairs or triplets of simultaneously recorded PCs, we interpret our results as indicating that, in principle, every interneuron might be connected to each local. It is unclear

at this point if these results apply only to this population of interneurons in the frontal cortex or whether this very dense connectivity only exists for inhibitory interneurons or is a general feature of cortical connectivity. In any case, future studies need to be performed with other neuronal cell types to address whether our findings are generally applicable. By densely innervating all local PCs, the subpopulation of interneurons that we have studied would affect their function in a global, nonselective manner. This occurs at all developmental stages tested. Although there have been several hypotheses that

have suggested that inhibitory interneurons are involved in crafting specific responses in PCs, such as the generation of specific receptive fields (Runyan et al., 2010), our results are more in line of the idea that inhibition this website serves instead to locally control PCs, perhaps helping stabilize the transfer function of the

circuit, but without a computational function “per se.” This would imply that they themselves are not involved in the generation of specific receptive until field properties (Kerlin et al., 2010). Indeed, the different developmental origin of interneurons, late invaders of cortical territories (Xu et al., 2004) resonates well with an unspecific role, whereby they could arrive late and extend a “blanket of inhibition” throughout the circuit. In finishing, one could reconsider the definition of cortical modules. While there is ample evidence for repetitive features in cortical design at the macroscopic level (“macrocolumns”; Grinvald et al., 1988 and Hubel and Wiesel, 1977), the physical existence of cortical “minicolumns” (Mountcastle, 1982) has been doubted due to the lack of strong anatomical evidence (Crick and Asanuma, 1986). Our results provide a different viewpoint from which to consider cortical modularity. If this type of dense, unspecific connectivity pattern applies to other populations of cortical neurons, neighboring neurons would have overlapping but not identical connectivity patterns. In this scenario, there would be no modules in the cortical microcircuit, but instead each individual neuron defines its own circuit, based on its own distinct input and output innervation.

5% (±7.9 SEM; n = 3 independent experiments) of control values within 48 hr of E10.5 tamoxifen administration, and to 9.8% (±7.0; n = 3) and 1.6% (±0.6; n = 3) by 72 hr and 96 hr, respectively. Immunohistochemistry showed no obvious

loss of Pax6 protein from the cortex 48 hr after tamoxifen administration ( Figures S2B and S2F), presumably due to residual protein perdurance. Within 72 hr of tamoxifen administration, however, Pax6 protein was removed from most cells in Emx1’s cortical expression domain ( Figures S2C, S2G, S2E, and S2I). We compared the numbers of YFP-positive cells in S phase in rostral, central, and caudal areas of the cortex (high, medium, and low Pax6-expressing, respectively; Figures 2D–2E″, 2J, 2K, selleck 2N, and 2O) in iKO and control embryos. Most cortical cells were YFP labeled in these embryos (Figures 2D’ and 2E’), and the proportions that were not ranged from 5% to 15% in both iKO and control cortices. Cells in S phase were identified by a 1 hr pulse of BrdU. The average numbers of YFP-positive cells that were in S phase at different times after tamoxifen administration at E10.5 (iKOE10.5tamox) or E13.5 (iKOE13.5tamox) are shown in Figures 2J, 2K, 2N, and 2O. In E13.5 iKOE10.5tamox embryos, i.e., shortly after loss of almost all Pax6 protein, increases in the numbers of cells in S phase occurred specifically in the rostral cortex (Figure 2J), indicating rapid onset of

overproliferation confined to this region. Two days later, see more however, in E15.5 iKOE10.5tamox embryos, significant increases in the number of cells in S phase were found in all parts of the cortex (Figure 2K). Similarly, between E15.5 and E16.5 in iKOE13.5tamox embryos, significant increases in the number

of cells in S phase occurred in all cortical areas (Figures 2N and 2O). In a second set of experiments, we estimated (as in Figures 1D–1F) values for mean Tc and Ts in iKOE9.5tamox embryos at E14.5 (Figures CYTH4 2R–2U; Figures S2D and S2H). The mean Tc varied significantly with genotype and cortical area (two-way ANOVA). It was reduced significantly in all cortical areas in iKOs (Figures 2R–2U). In controls, the mean Tc was slightly lower in the caudal cortex than in the rostral and central-lateral cortex (p < 0.0003 and < 0.015, respectively; Sidak’s multiple-comparisons test). The mean Ts did not show differences with genotype or cortical area. These results indicate that loss of Pax6 causes shortening of the cell cycle across all cortical areas by E14.5. Given that Tcs are shortened after loss of Pax6, either in specific cortical regions or across the entire cortex, depending on age, we predicted that these changes should correlate with an increased incidence of cells in M phase (identified by their expression of phosphorylated histone 3 [PH3]). This proved to be true (Figures 2F–2I, 2L, 2M, 2P, and 2Q; Figure S3). Interestingly, the positions of these additional M phase cells were abnormal.

Based upon the strong genetic interactions we observe between p190 and Sema-1a, and also the increased defasciculation phenotypes in p190 knockdown embryos, we propose that p190 negatively

regulates Sema-1a repulsive signaling. In addition, the antagonistic genetic interactions we observe between p190 and pbl suggest that they compete to control Sema-1a reverse signaling. This competition could serve to rapidly amplify or inhibit Sema-1a-mediated signaling. Interestingly, we also observed synergistic interactions between p190 and pbl, suggesting employment of a cyclic mode of action for Rho GTPase activation and inactivation in axon guidance ( Luo, 2000). These distinct and cooperative functions may contribute to selective activation of Sema-1a repulsive signaling at different choice points. Taken together, our results support a model whereby Pbl and p190 together act to

integrate target recognition and repulsive BMS 387032 signaling resulting from reverse Sema-1a signal transduction events ( Figure 8). Sema-1a was initially identified as an axonal repellent that functions as a ligand for PlexA ( Yu et al., 1998; Winberg et al., ZD1839 solubility dmso 1998). This Sema-1a ligand function is strongly supported by genetic analyses that define roles for Sema-1a-PlexA forward signaling in PNS motor axon pathfinding ( Winberg et al., 1998; this present study). However, differences in Sema-1a and PlexA null mutant phenotypes, and also the lack of genetic interactions between these mutants with respect to CNS defects, suggest that Sema-1a plays a unique role independent of PlexA in CNS axon guidance ( Figures S8B–S8E). Here, we provide cellular and genetic evidence that Sema-1a forward signaling is largely responsible for Sema-1a-mediated CNS axon guidance, whereas both forward and reverse

signaling are required for Sema-1a-mediated PNS motor axon pathfinding. In addition, Sema-1a reverse signaling is dependent upon opposing Pbl and p190 functions ( Figure 8). Sema-1a is highly expressed on embryonic motor and CNS axons and plays an important role in both CNS and PNS axon guidance (Yu et al., 1998). The neuronal requirement for Sema-1a in these guidance events fits well with our finding that the Sema-1a receptor function required for PNS axon guidance is controlled by neuronal Pbl below and p190. Our genetic interaction analyses, however, suggest that PlexA does not function as a major Sema-1a ligand in both the embryonic PNS and the CNS, consistent with previous observations in the olfactory system (Sweeney et al., 2011), but, rather, cooperates with Sema-1a reverse signaling to mediate repulsion (Figure 8). Given that plexins harbor a GAP activity directed toward Ras GTPases (Oinuma et al., 2004; Yang and Terman, 2012), Sema-1a reverse signaling and the receptor function of PlexA likely converge on Rho and Ras GTPases, respectively, and these two small GTPases likely collaborate to control axonal defasciculation.

Hippocampal activation, which was bilateral in both awake monkeys, was absent in two of the anesthetized monkeys and unilaterally preserved in one animal. The functional

activation in the amygdalae and hippocampus is suppressed under anesthesia or at the least severely reduced. That these areas are activated only in awake animals suggests they are involved in awake processing of faces or their properties. Figure 6 shows the mean responses of the face-selective areas to faces and to the other categories in awake and anesthetized animals. Overall response amplitudes were lower in anesthetized than awake monkeys. The reduction of the amplitude of the BOLD signal was expected given the effects of anesthesia on the vascular system. CDK inhibitor While the face-selective areas in the middle STS showed significant responses to the other object categories (t test, p < 0.05), the ventral areas, for

instance near the AMTS, were more selective to faces, given that the responses to objects were often not significantly different from zero in these areas. These results suggest that the ventral pathway is more selective for faces than the STS patches. In this study, we took advantage of the increased sensitivity of high-field (7T) SE fMRI to study face processing in the temporal lobe of awake and in the entire brain of anesthetized monkeys. First, we confirmed the face-selective activation found in earlier monkey fMRI studies, but in addition, we found and report a number of face-selective areas in the ventral and medial temporal lobe that have not been described before, such as ventral V4, see more ventral TE, TG, hippocampus, entorhinal science cortex, and parahippocampal cortex (area TF). Some of the more posterior areas may be homologous with human occipitotemporal face areas. We also scanned awake

and anesthetized animals by using the same protocol and observed that MTL activation that was present under passive viewing was mostly preserved under anesthesia (except in the hippocampus), suggesting that processes related to memory, like familiarity or recollection, are not necessarily required for functional activation in the MTL. In agreement with previous studies of face-selective activation in macaques we found extensive face-selective activation in STS, with the largest and most reproducible face-selective patches located in the middle STS, which responded to other categories as well (Pinsk et al., 2005 and Tsao et al., 2003). Activation in or near the AMTS was also found in all animals and was highly specific to faces. Selectivity of AMTS areas for faces was also identified in earlier fMRI studies (Logothetis et al., 1999 and Tsao et al., 2003) although not in all, most likely because of signal loss in the temporal lobe. Additional face-selective areas were found in area TG and ventral TE but these results were less reproducible across animals.