Efficient

Lam1 coating was obtained as the Lam1-coated beads clumped together and formed aggregates, which was not seen for BSA or uncoated beads, and confirmed by strong Lam1 staining by immunofluorescence. Bead implantations were performed by mounting 24 hpf embryos in 2%–4% methylcellulose (Sigma), containing 0.4 mg/ml MS222 (Sigma) as anesthetic. Beads were suspended in the methylcellulose, sucked into a sharp glass capillary connected to a mineral-oil filled Hamilton syringe, and injected into the retina of Akt inhibitor the embryo. Embryos were then transferred to clean Petri dishes containing embryo medium and penicillin/streptomycin/fungicide to recover. The polychromatic red dye showed extremely bright fluorescence, and the signal bleedthrough into the green channel was strong enough for bead visualization in most experiments. When imaging in red channel was also necessary, beads were photobleached by being placed on the windowsill for 2–4 weeks. Dissociated retinal cell culture was performed as previously described (Zolessi et al., 2006). For the creation of a substrate with Laminin islands, coverslips were coated

overnight with Venetoclax poly-L-lysine (Sigma, 10 μg/ml), and then sprayed with an atomizer creating a fine mist of Lam1 (Sigma, 20 μg/ml) mixed with Texas-red-conjugated Dextran (D-1863, Invitrogen) in order to stain the Laminin deposits. Imaging of live and fixed embryos was performed as described previously (Poggi et al., 2005), using a Perkin Elmer Spinning Disk UltraVIEW ERS, Olympus IX81 Inverted microscope and 60× (1.2 NA) water immersion objective, and a motorized XY stage (H117,

Prior) to allow for simultaneous imaging of multiple embryos. A confocal laser scanning microscope (Leica) and 63× (1.2 NA) water immersion objective (Leica) were also used for experiments shown in 3A–3C and 6B. Optical sections at 0.75–1 μm separation were taken to cover the majority of the retina (between 40 and 100 μm) at the relevant time intervals. Whole-mount immunostaining was performed using standard methods, using rabbit polyclonal anti-Lam1 (L9393, Sigma, 1:100) and anti-rabbit Alexa-594 (Invitrogen, 1:1000). Confocal data was analyzed using Volocity (Improvision). Deconvolution was generally performed on Interleukin-11 receptor data acquired by spinning disk confocal microscopy using the Iterative Restoration tool at 25 iterations and 99.99% confidence levels. Unless otherwise stated, the confocal z-slices were cropped to a rectangular region containing the cells of interest in XYZ and reconstructed using 3D Opacity. Brightness, contrast, and gamma were adjusted for maximal visibility of cellular morphology and fluorescent signal using Volocity, Photoshop (Adobe), and ImageJ (NIH), and the RFP channel was converted to magenta using the channels tool in ImageJ. Pseudocoloring and cell tracing was done in Photoshop, and the outline of the cell was determined by comparing it to the original confocal z-slices.

, 2011, Frank, 2006 and Wiecki and Frank, 2013) have proposed that projections from dACC to STN specify the threshold for evidence accumulation JAK inhibitor before initiating a motor or cognitive response and that efferents from STN implement

this threshold. To test this, Cavanagh and colleagues (2011) used scalp and intracortical EEG to measure dACC and STN activity in patients with Parkinson’s disease while undergoing deep brain stimulation (DBS) to the STN. On each trial, patients chose between pairs of stimuli that they had learned to associate with either similar or different rewards (high- and low-conflict trials, respectively). Activity in both dACC and STN tracked the level of decision conflict for a given choice. Furthermore, greater dACC activity associated with high conflict trials predicted slower more accurate responses (reflecting a higher threshold). In contrast, when DBS was applied to STN (interfering with its function), responses on these trials became more impulsive

and error prone (reflecting lower decision thresholds), and the relationship between dACC activity and slower responding was lost. Taken together, these findings provide support for the role of dACC in specifying adaptive adjustments in threshold that are then implemented by STN. Similarly, Aston-Jones and Cohen (2005) have proposed that dACC is involved http://www.selleckchem.com/products/Nutlin-3.html in monitoring behavioral outcomes and deciding when it is appropriate to explore versus exploit, and that this is conveyed to LC which implements the decision by means of its broad modulatory projections to the thalamus and neocortex. This division of labor is consistent with strong anatomic connections from dACC to LC and is also supported by imaging studies implicating dACC in the decision to explore, as well as recent behavioral and psychophysiological studies suggesting a role for LC in mediating these decisions by regulating the balance

between exploration and exploitation (Gilzenrat et al., 2010, Jepma and Nieuwenhuis, Phosphatidylethanolamine N-methyltransferase 2011, Murphy et al., 2011 and Nieuwenhuis et al., 2005a). The projections of dACC to subcortical modulatory structures, together with its efferents to other cortical areas, puts the dACC in a position to specify control signals of a variety of types, and over a variety of domains of processing, from signals required to regulate specific tasks (e.g., in lPFC) to broader, modulatory ones needed to influence a wide range of tasks (e.g., in STN and LC). This centralized responsibility for specifying such a wide range of control signals may also explain why dACC appears to be so consistently associated with cognitive control, and more so than other candidate structures like lPFC (e.g., Danielmeier et al., 2011 and Dosenbach et al., 2006). Insofar as most of those other structures are responsible for regulation, they are dedicated either to the support of specific tasks or to specific modulatory forms of control.

, 2002). Furthermore, nonconscious stimulus processing was absent in human MTL and macaque STS/IT cortex, where none of the modulated cells consistently fired more during the perceptual suppression of a preferred stimulus. These findings led to the hypothesis that perceptually modulated activity in early, extrastriate cortical areas reflects competitive interactions mediating image segmentation, figure-ground segregation, and perceptual grouping, mechanisms that give rise to perceptual organization and, therefore, subjective visual perception (Blake and Logothetis, 2002 and Logothetis, 1998). In contrast, perceptually modulated

activity in the temporal lobe represents a final stage of cortical processing, beyond the resolution of ambiguities in the sensory environment, where neural activity explicitly represents the content of visual consciousness (Blake and Logothetis, PR171 2002 and Logothetis, 1998). However, the temporal cortex is not the final endpoint of the ventral visual processing stream. The STS/IT cortex is reciprocally connected to visual areas of the lateral prefrontal cortex (LPFC) (Barbas, 1988, Borra et al., 2010, Webster et al., 1994 and Yeterian et al., 2012) where neuronal activity, including single units, is also known to respond selectively to faces and

complex visual objects similar to the perceptually modulated cells found in the INCB018424 concentration STS/IT cortex (Pigarev et al., 1979, Ó Scalaidhe et al., 1997, Ó Scalaidhe et al., 1999 and Tsao et al., 2008). Thus, an intriguing question is whether such feature-selective neuronal activity in the LPFC correlates with phenomenal perception under conditions introducing perceptual ambiguity. Previous studies provided strong evidence supporting a role for the LPFC in spontaneously induced perceptual alternations. For example, patients with widespread STK38 prefrontal

cortex lesions show abnormal perceptual transitions during bistable perception (Meenan and Miller, 1994, Ricci and Blundo, 1990 and Windmann et al., 2006; but see Valle-Inclán and Gallego, 2006). In addition, human functional magnetic resonance imaging (fMRI) studies repeatedly revealed an increase in the blood-oxygenation-level-dependent (BOLD) signal of the inferior prefrontal gyrus during endogenously triggered perceptual alternations compared to purely sensory stimulus transitions (Lumer et al., 1998, Sterzer and Kleinschmidt, 2007 and Zaretskaya et al., 2010). More recently, a direct neuronal correlate of perceptual transitions was identified in the firing rate modulation of neurons in the macaque frontal eye fields, which predicted perceptual alternations during the bistable paradigm of motion-induced blindness (Libedinsky and Livingstone, 2011).

It provides the primary excitatory stimulus to the auditory nerve during this period, and this is responsible for the downstream survival and maturation of auditory neurons. Retina. An extensive literature pertains to P2X receptor expression in the retina, both in neurons and in supporting cells ( Housley et al., 2009). The speculation around their roles in normal function or disease currently lacks insight at the cellular level, but this is clearly an area worth Raf inhibitor exploring. Recently, it has been proposed that central P2X4 receptors are involved in neuropathic pain (Trang and Salter, 2012). The key evidence here is that removal of P2X4 receptors strikingly prevents the development of mechanical allodynia following

peripheral nerve injury (Tsuda et al., 2003, Epacadostat mw 2009; Ulmann et al., 2008). Peripheral nerve injury is followed by activation of spinal microglia. It is suggested that ATP acting on P2X4 receptors drives the release of BDNF from spinal microglia, and that this in turn is critical for the rewiring that

underlies the perception of mild tactile stimuli as noxious. The neuronal subtypes and specific microcircuitry involved remain to be elucidated. P2X7 receptors can also fashion the behavioral responses to painful stimuli and, in sharp contrast to the situation with P2X4 receptors, there is now a wealth of pharmacological antagonists to be used as experimental tools, some Casein kinase 1 of which are in clinical trials (Gum et al., 2012; Jarvis, 2010). The predominant expression of P2X7 receptors in the nervous system is on microglia, astrocytes, and oligodendrocytes. However, some re-interpretation of experiments which used knock-out mice may be

required. The mice produced by Pfizer (Masin et al., 2012) continue to express two shortened, alternatively spliced, forms of the P2X7 receptor (P2X7 13B and P2X7 13C). The mice produced by Glaxo have a functional P2X7 splice variant (P2X7(k)) that continues to be expressed in these mice: the corresponding protein is widely expressed, but it has a different N-terminus and TM1 (Nicke et al., 2009). It forms receptors which are more sensitive to ATP and which undergo a more rapid increase in permeability to organic cations (a measure of pore dilation). The development of mechanical hypersensitivity in models of neuropathic pain is absent in the Glaxo P2X7 deletion mouse (Chessell et al., 2005), and a similar phenotype is observed in mice lacking both isoforms of IL-1β (Honore et al., 2006b). Mechanical hypersensitivity is also prevented by intrathecal P2X7 antagonist A-438079 (Kobayashi et al., 2011) and Brilliant Blue G (He et al., 2012) or systemic administration of P2X7 receptor antagonists (Honore et al., 2006a). These effects in pain models appear to require release of IL-1β, given that A-438079 blocks not only the ATP evoked release of IL-1β but also the release evoked by LPS (Clark et al., 2010).

We repeated these experiments, however, and observed the same speeding as we found in the conditional GluA1 KO neurons ( Figure S3). We have no clear explanation for the difference, although Andrásfalvy et al. (2003) did report faster deactivation in outside-out patches from the germline KO mouse. Long-term potentiation (LTP), which is widely held as the cellular basis for learning and memory, is also found to be severely reduced in hippocampal neurons from GluA1 KO mice ( Zamanillo et al., 1999). We, therefore, examined LTP in neurons lacking CNIH-2/-3. If GluA1-containing

AMPARs are removed from synapses in the absence of CNIH-2/-3, LTP should be compromised. Indeed, when compared to uninfected neurons, LTP was markedly reduced in Cnih2/3fl/fl neurons infected with CRE ( Figure 2K). Thus, knocking out CNIH-2/-3 appeared to phenocopy knocking out GluA1 in three key parameters. Previous studies in HEK cells ( Kato et al., 2010a) suggested that JQ1 in vivo the absence Compound C in vivo of CNIH proteins in neurons should result in AMPAR resensitization and alterations in cyclothiazide potentiation of kainate-induced currents.

However, neither of these effects was observed ( Figures S3C and S3D). We next directly tested whether the effects of deleting CNIH-2/-3 are specifically related to the regulation of GluA1. To this end, we compared the effects of CNIH-2 knockdown (KD) on AMPAR-eEPSCs in GluA1 and GluA2 KO mice. The shRNA we generated was highly effective in knocking down CNIH-2 protein levels (Figure S4A) and in wild-type neurons produced a phenotype identical

to knocking out CNIH-2 (Figures 1A, 1B, 3A, and 3B). The KD of CNIH-2 in neurons from GluA2 KO mice, which primarily express GluA1 homomers, also resulted in a selective but more pronounced reduction in the AMPAR-eEPSC compared to wild-type mice (Figures 3C, 3D, 3G, and 3H). In striking contrast, CNIH-2 KD in slices from GluA1 KO mice had no effect on AMPAR-eEPSCs (Figure 3E), AMPAR mEPSC kinetics (Figure S4B), or NMDAR eEPSCs (Figure 3F), demonstrating that CNIH-2 effects on synaptic AMPARs require GluA1. The eEPSC results are summarized in Figures 3G and 3H. Additionally, residual GluA2A3 receptors in GluA1 KO neurons were found to have a IKA/IGlu ratio of ∼0.5, suggesting that all available TARP PR-171 clinical trial binding sites on these receptors are occupied (Figure S4C). Although it is well established that CNIH-2 binds to AMPARs (Kato et al., 2010a; Schwenk et al., 2009; Shi et al., 2010), the relative binding to GluA subunits has not been reported. Because CNIH-2 KD has a profound and selective effect on GluA1-containing AMPARs, we compared GluA1 and GluA2 binding to CNIH-2. We first immunoprecipitated GluA2 from wild-type hippocampal lysates using two different antibodies (anti-GluA2 or anti-GluA2/3). We found that CNIH-2 coimmunoprecipitated with GluA2 from wild-type hippocampal lysates, as expected (Figure 3I).

An important caveat in the study of ICMs by EEG or MEG is that, due to their limited spatial resolution, these methods are prone to signal mixing artifacts, which are especially severe for estimates of brain interactions (Nolte et al., 2004 and Stam et al., 2007a). Through volume spread, any active source contributes, in weighted manner, to the signals at all sensors (Figure 2A). This can give rise to spurious signal correlations and, thus, distort connectivity measures. Several methods have been suggested to address this problem, which selleck screening library are based on the notion that volume spread contributes to apparent coupling with negligible

delay, whereas true neuronal communication also occurs at other delays. One possibility is to analyze the imaginary part of coherence, which, if significant, cannot be explained by volume spread (Nolte et al., 2004). Subsequent studies have introduced related measures such as the phase lag index (Stam et al., 2007a). Another approach that has www.selleckchem.com/products/gdc-0068.html recently been introduced has used phase orthogonalization of oscillatory signals from different sources before analyzing power envelope correlations (Figure 2B) (Hipp et al., 2012). This is equivalent to removing, after Fourier transformation, those components that have the same phase for the two signals. This method is insensitive to trivial correlations arising from two sensors seeing the identical signal component and enables the

selective study of true neuronal interactions from MEG or EEG recordings (Figures 2D and 2E) (Hipp et al., 2012 and Brookes et al., 2012). It should be noted, however, that this comes at the cost of also discarding true zero-phase synchrony, which is known from microelectrode recordings to be abundant in the brain (Singer, 1999 and Engel et al., 2001). For studying ICMs, it is also highly interesting to quantify functional relationships between waves too of different frequencies (Jensen and Colgin, 2007 and Palva and Palva, 2011). Measures such as n:m phase locking for n≠m, phase-amplitude coupling, or amplitude-amplitude coupling

can reveal nonlinear coupling across different frequencies, which is also less susceptible to volume spread artifacts. Functional connectivity, in whatever form, can in principle be estimated between all pairs of voxels specified on a grid or surface. It is essentially impossible to visualize such a connectivity matrix in its complete form and hence approaches using graph-theoretical measures (Bullmore and Sporns, 2009) have become popular to characterize ICMs with a small set of parameters for each voxel. Beyond data compression, this representation may indicate general properties of brain connections having, for instance, small world topology, in which there are many local but few remote connections, such that the neural nodes are generally connected by short paths (Bullmore and Sporns, 2012). Correlation patterns in ongoing activity were first described in animal studies.

Compared to the double-transgenic mice expressing ADAM10-WT Raf targets (Tg2576/WT), the decrease of mature APP and increase of APP-CTFα were significantly reduced in 3-month-old brains expressing either Q170H (Tg2576/Q170H) or R181G (Tg2576/R181G) ADAM10 mutations (Figures

2A and 2B). Moreover, the levels of sAPPβ and APP-CTFβ were elevated by both LOAD mutations in comparison to Tg2576/WT mice. Quantitative analysis of brain sAPPα and sAPPβ by ELISA revealed similar patterns as compared to the results from western blots (Figure 2C). The ratios of both APP-CTFα:APP-CTFβ and sAPPα:sAPPβ indicate that both the LOAD mutations shifted more than 50% of the APP processing from the α-secretase BKM120 cost to β-secretase pathway. While the ratio of α- versus β-cleavage was still higher in Tg2576/Q170H and Tg2576/R181G mice than Tg2576, the DN mutation modestly shifted APP processing toward β-cleavage. However, the increase in β-secretase cleavage of APP by mutant ADAM10 expression was not caused by altered BACE1 expression (Figure 2A). Notably, as observed in the ADAM10 single-transgenic mice, no differences were found in sAPPα levels among Tg2576/WT, Tg2576/Q170H, and Tg2576/R181G double-transgenic mice (Figures 2A and 2B). Instead, C-terminal

truncated sAPP were detected more abundantly in mice expressing the WT form (Figures 2A, S3B, and S3C). Given the robust increase of APP-CTFα and concurrent decrease of APP-CTFβ by ADAM10-WT expression, the

C terminus truncated sAPP are probably generated from sAPPα. Next, we examined Aβ levels in the Tg2576/ADAM10 double-transgenic mice. In the brains of 3-month-old Tg2576/WT mice, both TBS-soluble Aβ40 and Aβ42 levels were reduced ∼35% compared to Tg2576 control (Figure 3A). However, the ADAM10-mediated decrease in Aβ40 and Aβ42 was significantly attenuated in both Tg2576/Q170H and Tg2576/R181G mice. Tg2576/DN mice exhibited higher Aβ levels than Tg2576 alone, which indicates decreased nonamyloidogenic processing of APP in the presence of the DN form. In 3-month-old brains, TBS-insoluble Aβ was barely detectable in Tg2576 or Tg2576/ADAM10 mice (data not shown). As the deposition of Adenylyl cyclase insoluble Aβ occurs at 7–8 months in the brains of Tg2576 mice (Kawarabayashi et al., 2001), the total Aβ levels at 12 months were hundreds-fold higher than those at 3 months (Figure 3B). Correspondingly, in 12-month-old mice, the reduction of Aβ levels in Tg2576/WT was dramatically amplified in both TBS-soluble (>90%) and insoluble (>99%) Aβ fractions (Figure 3B). Compared to the Tg2576/WT, there was much less of a decrease in Aβ levels in Tg2576/Q170H mice. However, Tg2576/Q170H mice also showed a robust decrease in brain Aβ levels as compared to Tg2576. This decrease was most likely due to partial, but not complete, loss of α-secretase activity by the LOAD mutation.

There were three main sub-themes that were

brought up by participants around CM pertaining to the relative importance of effectiveness of CM as an intervention. These were as follows (see Table 2.4 for examples): 1. A pragmatic approach that linked in with the discussions around having CM as part of a ‘tool kit’ of interventions. This could be summarised as ‘if it works, use it.’ This stance was primarily taken by the more experienced clinicians. The aims of this study were to explore systematically the attitudes, concerns and opinions Rigosertib of staff and service users about the use of CM in publicly funded substance misuse services and to identify the key areas that may be influential in terms of implementation and outcome. Below we summarise the findings and examine specifically what this study adds to the literature in terms of: 1. How CM may fit within the context of substance misuse programmes.

The causes of addictions are well recognised to be a complex interaction of biological, social and psychological factors and from a health perspective can be considered within a chronic disease model (McLellan et al., 2000), requiring a collaborative approach between professional and patient if long-term, sustained positive outcomes are to be achieved. Many substance misuse services in developed countries work within a multi-disciplinary, community treatment model. Consequently, the way that new interventions buy AG-014699 are viewed by clinicians (in their role as individual citizen as well as practitioner), and the collective philosophy of a treatment service will have a substantial impact on the effectiveness and cost-effectiveness of their implementation and uptake (Benishek et al., 2010, Cameron and Ritter, 2007, Kirby et al., 2006 and McGovern et al., 2004). This study highlighted the issues most consistently discussed about the use of CM by service users (both current and past) and health professionals. The 15 different themes are concerns that will need to be considered FMO4 in any evaluation of effectiveness of CM implementation

within different clinical settings, and across different health care systems. Whilst the evidence base from randomised controlled trials (RCTs) for the role of CM in substance misuse programmes is compelling (Dutra et al., 2008, NIHCE, 2007 and Pilling et al., 2007) the uptake into clinical practice has been less good (Kirby et al., 2006 and Petry, 2006). The results of this study suggest that the overall aims of a treatment programme (e.g., whether the aim is for harm minimisation or abstinence) may be a significant factor in how a single intervention is viewed and the likelihood of its implementation. The methodology of an RCT, even of a complex intervention, specifically attempts to insulate the intervention under examination, from such contextual factors.

Each of the proteins in this putative pathway, CTGF, TFGβ2, and its receptors TGFβRI and TGFβRII, were expressed in the glomerular layer. TFGβ2 was secreted by GFAP-positive astrocytes, while its receptors—TGFβRI and TGFβRII—were expressed in a subpopulation of newly born GAD-positive periglomerular neurons. CB-839 solubility dmso In vivo evidence for CTGF/TGFβ2 interaction was provided by knocking down TGFβRI selectively in postnatally born neuroblasts via viral injection.

TGFβRI knockdown led to an increase in the number of neurons located in the glomerular layer, indicating a reduction in apoptosis. Furthermore, the effect of knocking down CTGF in OB, shown in the initial experiments to effect cell survival, Y-27632 could be abrogated by the simultaneous knockdown of TGFβRI receptor in the target neuroblasts. Together, these data indicated that CTGF acts in a complex with TGFβ2 to activate a TGFβ signaling pathway in postnatally born periglomerular cells that leads to activation of apoptosis in these cells (Figure 1). Knockdown of CTGF led to an increased number of periglomerular cells. Did this affect olfactory information processing at the level of OB circuitry and electrophysiology? In the CTGF knockdown OB, the frequency but not the amplitude of spontaneous inhibitory postsynaptic currents (sIPSC) increased in both prenatally and postnatally generated populations of periglomerular interneurons.

The frequency and the amplitude of spontaneous excitatory postsynaptic current (sEPSC) in these cells, however, did not change significantly. Therefore, the sEPSC:sIPSC

(excitation:inhibition ratio) decreased in postnatally and prenatally born CTGF-knockdown periglomerular cells. These results indicated that CTGF expression level impacts local circuit activity and the presence of an increased number of periglomerular neurons resulted in stronger inhibition on the mitral cells. Do the alterations in the number of inhibitory cells have a consequence in mouse olfactory behavior? To understand its role, odorant detection, discrimination, and long-term memory were examined in mice that were subject to CTGF knockdown in the olfactory bulb. Compared to control mice, CTGF knockdown mice displayed Dichloromethane dehalogenase a decrease in odorant detection threshold, i.e., the CTGF knockdown mice were more sensitive to odors than control mice. In the odorant discrimination test, CTGF knockdown mice performed better than control mice. The only test in which CTGF knockdown and control mice performed equally was the long-term memory test using suprathreshold odorant stimuli. The mammalian olfactory bulb is subject to dynamic and variable changes throughout adult life. New OSNs are continually reinnervating the OB as a result of normal turnover of these cells and traumatic or pathogenic lesions in the sensory epithelium. Furthermore, the odor environment is constantly changing in intensity and quality.

Naselaris et al. (2009) used a model similar to the one described for the Kay et al. (2008) study to attempt to reconstruct images from brain activation. They found that the reconstructions http://www.selleckchem.com/products/abt-199.html provided by the basic model were not better than chance with regard to their accuracy. However, by using a database of six million randomly selected natural images as priors, it was possible to create image reconstructions that had structural accuracy substantially better than chance. Furthermore, using a hybrid model that also included semantic labels for the images, the reconstructions also had

a high degree of semantic accuracy. Another study by Pereira et al. (2011) used a similar approach to generate concrete words from brain activation, using a “topic model” trained on corpus of text from Wikipedia. These studies highlight the utility of model-based decoding, which provides much more powerful decoding abilities via the use of computational models that better characterize mental processes along with statistical information mined from large online databases. The foregoing examples of successful decoding are impressive, but each is focused on decoding between different stimuli (images or concrete words) for which the relevant representations are located within a circumscribed set of brain areas at a relatively small spatial scale (e.g., Cilengitide cell line cortical columns). In

these cases, decoding likely relies upon the relative activity of specific subpopulations of neurons within those relevant cortical regions or the

fine-grained vascular architecture in those Peroxiredoxin 1 regions (see Kriegeskorte et al., 2010 for further discussion of this issue). In many cases, however, the goal of reverse inference is to identify what mental processes are engaged against a much larger set of possibilities. We refer to this here as “large-scale” decoding, in which “scale” refers to both the spatial scale of the relevant neural systems and the breadth of the possible mental states being decoded. Such large-scale decoding is challenging because it requires training data acquired across a much larger set of possible mental states. At the same time, it is more likely to rely upon distributions of activation across many regions across the brain and thus has a greater likelihood of generalizing across individuals compared to the decoding of specific stimuli, which is more likely to rely upon idiosyncratic features of individual brains. Although most previous decoding studies have examined generalization within the same individuals, a number of previous studies has shown that it is possible to generalize across individuals (Davatzikos et al., 2005, Mourão-Miranda et al., 2005 and Shinkareva et al., 2008). In an attempt to test the large-scale decoding concept, we (Poldrack et al.