A key advantage of using fluorescence to study synaptic ultrastructure is that multiple protein species can be labeled and monitored concurrently find more (Micheva and Smith, 2007), including in live neurons. We expect rapid advances in this arena, with high-content studies of synaptic molecular organization leveraging new labeling strategies and chemical biology methods. There has also been dramatic progress in nonlinear optical microscopy. Today, neuroscientists widely appreciate the phenomenon of two-photon excitation, but two-photon effects were once considered esoteric aspects of optical physics. It was the development of solid-state ultrafast lasers, chiefly the development of titanium sapphire laser technology,

that propelled the two-photon microscope innovated by Webb and Denk to become broadly usable by biologists and to permeate neuroscience. We expect continued improvement of not just the hardware elements that comprise

the two-photon microscope, but also general optical strategies for laser scanning, for scanless approaches to laser illumination, and for other new approaches for imaging faster and deeper into tissue (Kobat et al., 2011, Oron et al., 2005, Quirin et al., 2013 and Schrödel et al., 2013). Beyond the current depths presently attainable by two-photon LY294002 research buy microscopy, nonlinear optical microscopy modalities relying on multiphoton excitation and long-wavelength, ultrashort-pulsed lasers promise to reveal fundamental features of nervous tissue

(Farrar et al., 2011, Horton et al., 2013, Kobat et al., 2011 and Mahou et al., 2012). Due to reduced light scattering at longer wavelengths, three-photon excitation with an illumination wavelength of 1.7 μm has been demonstrated in proof-of-concept studies to reach into even the hippocampus in a live mouse (Horton et al., 2013). However, much work remains to make this a practical technique for day-to-day experimental studies of nervous system structure. others For deep studies of nervous system dynamics, further development of red fluorescent sensors of neural activity will be required, which presently lag behind green fluorescent sensors such as the highly successful GCaMP6 Ca2+ sensors. There are also crucial issues of optical aberrations to consider when imaging 1 mm or more into dense brain tissue. Adaptive optical methods may help, offering the possibility of correcting aberrations online, and have already made inroads into neuroscience (particularly for ophthalmic imaging of the retina and visualization of single human photoreceptor cells) (Godara et al., 2010 and Hunter et al., 2010). Adaptive optics have also shown utility for two-photon microscopy by improving the resolution of two-photon imaging deep in tissue (Ji et al., 2012). When combined with long-wavelength laser illumination, such as for three-photon excitation, adaptive optics may become even more important.

The first response to mechanical stimulus was not affected, but the magnitude of the Tyrosine Kinase Inhibitor Library second response was reduced (Kindt et al., 2007). A role for the TRPV channel subunit OSM-9 is evident from the finding that osm-9 mutant OLQ neurons lack mechanically-evoked calcium transients ( Chatzigeorgiou et al., 2010). Because MRCs have yet to be measured in this mechanoreceptor neuron, it is not known whether loss of TRPA-1 or OSM-9 affect MRCs or the events that follow their activation. These examples in C. elegans nematodes establish the rule that mechanoreceptor neurons commonly express multiple

DEG/ENaC and TRP channel proteins and that these channels operate together to enable proper sensory function. The ability to directly measure

MRCs in vivo has revealed that both DEG/ENaC and TRP channels can form MeT channels. Evidence from the ASH and PVD nociceptors suggests that some TRP channels are essential for posttransduction events needed for sensory signaling. These case studies provide evidence for the idea that TRP channels can be crucial elements in both sensory transduction and in post-transduction signaling. They also illustrate the powerful insights available when detailed physiological analysis of identified mechanoreceptor neurons is merged with genetic dissection. It is rare for deletion of a single DEG/ENaC gene to induce strong behavioral defects in C. elegans. Indeed, there is only one such DEG/ENaC gene known so far: mec-4. By contrast, deleting the DEG/ENaC genes mec-10, deg-1, unc-8, and unc-105 fails to produce clear behavioral phenotypes, although gain-of-function alleles significantly disrupt several behaviors. INCB024360 clinical trial Though only a subset of the DEG/ENaC genes have been studied in this way, these findings suggest there is considerable redundancy in C. elegans mechanosensation. The case of mec-4 and mec-10 illustrate this idea clearly: both genes are coexpressed in the TRNs and encode pore-forming subunits of the MeT channel required for gentle touch sensation ( O’Hagan et al.,

2005). Whereas deleting mec-4 eliminates mechanoreceptor currents and behavioral responses to touch, deleting mec-10 produces a mild defect in touch sensation and has little effect on Rolziracetam mechanoreceptor currents ( Arnadóttir et al., 2011). The peripheral nervous system of Drosophila larvae has three main types of neurons ( Bodmer et al., 1987, Bodmer and Jan, 1987 and Ghysen et al., 1986). External sensory and chordotonal neurons have a single sensory dendrite and innervate specific mechanosensory organs. In contrast, multidendritic neurons have a variable number of fine dendritic processes that lie beneath the epidermis and do not innervate a specific structure. Different subclasses of these neurons provide information about touch and body position as well as function as nociceptors ( Hughes and Thomas, 2007, Song et al., 2007 and Zhong et al., 2010).

, 2000) and assigned to each putative inhibitory synaptic location identified by the collision-detection algorithm. The GABAA reversal was −80 mV. External GSK1210151A mw input is mediated by distributing additional excitatory and inhibitory synapses randomly (uniform distribution) across all cells and activating them independently with a temporally modulated frequency. External synapses accounted

for approximately 5% of the total number of synapses. To measure spiking synchrony, we calculated the mean of the normalized joint peristimulus time (PST) histogram at a lag of 0 ms, i.e., the mean cross-covariance of PST histograms of cell pairs, normalized by the product of their SD. To generate the histograms, we used a bin width of 1 ms. As the covariance would be affected by the change in firing rates between simulated UP and DOWN, we limited the analysis to spikes elicited during UP. To remove synchrony from the simulation (uncorrelated case), we first generated artificial spike trains by moving all spikes of the control case

to times randomly chosen between 0 and 4,000 ms. This generated independent stationary Poisson spike trains with the same number SCH727965 price of spikes as in the control case. This spike train was then used to drive synapses in a simulation. The external input was also present but with a constant rate equal to the mean of the rate in the control case. To increase synchrony (supersynchronized case), we moved all spike times of the control case to the nearest multiple of 5 ms. External input in this case was identical to the control case. The extracellular contribution of transmembrane currents of all neural compartments (approx. 410 compartments per cell, >5,000,000 in total) was calculated via the line source approximation, LSA (Holt and Koch, 1999). Briefly, assuming a purely homogeneous and resistive (3.5 Ω m) extracellular medium, Laplace’s equation applies ∇2Ve = 0. At the boundaries, (1/ρ)Ve = Jm with ρ being the resistivity and Jm the transmembrane current density. LSA assumes each cylindrical compartment of the spatially discretized neuron as a line (a cylinder of infinitesimally small diameter) with a constant current density along the line. The Ve

contributed by Resminostat current I  j of each neural compartment j   evenly distributed over the line segment of length Δs  j and the overall extracellular voltage Ve(r→,t) becomes Ve(r→,t)=∑j=1NρIj(t)4πΔsjloghj2+rj2−hjlj2+rj2−lj,with rj being the radial distance from line segment, hj the longitudinal distance from the end of the line segment, and lj = Δsj+hj the distance from the start of the line segment. The LSA was found to be accurate, except at very small distances (a few micrometers) from the cable. Calculation of Ve using the LSA took place on a separate computer cluster (SGI) and took approx. 1 hr. The CSD was estimated as the negative second spatial derivative along the depth axis. We also calculated the CSD via iCSD (Łęski et al., 2011), and the outcome remained very similar.

001 for WT). Exploration of two novel objects is not different between genotypes during acclimation (not shown). MTEP-treated APP/PS1 mice recover a novel object preference (Figure 7B; p < 0.001 for both WT and APP/PS1 with MTEP). A separate cohort of APP/PS1 was tested in the Morris water

maze. Without treatment, the APP/PS1 mice show greater latencies to locate a hidden platform relative to WT across learning trials (Figure 7C; RM-ANOVA, p < 0.001), and spend less time in the target quadrant during a probe trial for memory selleck compound 24 hr later (Figure 7D; ANOVA p < 0.001). In contrast, MTEP-treated APP/PS1 mice are indistinguishable from untreated WT or MTEP-treated WT mice in learning and memory (Figures 7C and 7D), but are different from untreated APP/PS1 (Figures 7C and 7D; p < 0.001). There is a significant interaction of genotype and drug (two-way RM-ANOVA in Figure 7C for APP/PS1 × MTEP interaction,

p < 0.001; two-way ANOVA in Figure 7D, p < 0.001). We also administered MTEP to 3XTg mice expressing mutant APP, PS1, and Tau (Oddo et al., 2003). this website At 8–9 months, these mice perform normally in the Morris water maze (not shown), but are impaired in novel object recognition (Figure 7E). After randomization to MTEP or vehicle, the 3XTg mice were assessed for novel object recognition (Figure 7G). MTEP-treated 3XTg mice show a novel object preference (p < 0.01), but vehicle-treated mice do not. Thus, MTEP reverses memory deficits in two transgenic AD mice. We considered whether improved memory with MTEP is correlated with a reversal of synaptic loss. A separate cohort of WT and APP/PS1

transgenic mice at 10 months age were treated for 10 days with MTEP, 15 mg/kg two many times a day. As expected, control APP/PS1 mice exhibit a 25%–30% decrease in area occupied by presynaptic synaptophysin and postsynaptic PSD-95 immunoreactivity in the dentate gyrus (Figures 8A–8C). The loss of stained synaptic area was fully rescued by a 10-day course of MTEP (Figures 8D and 8E). For WT mice, MTEP did not alter synaptic density. We also assessed synaptic density ultrastructurally, identifying synaptic profiles by the presence of a postsynaptic density and presynaptic vesicles (Figure 8F). Synapse density in transgenic dentate gyrus increased by 20% with MTEP treatment (Figure 8G). This study delineates a direct role for mGluR5 in Aβo-related pathophysiology. Of transmembrane PSD proteins, only mGluR5 supports coupling of Aβo-PrPC to Fyn activation. Intracellular calcium and protein translation are also linked to Aβo-PrPC engagement via mGluR5. An mGluR5 dependence of signaling is observed for TBS-soluble extracts of AD brain as well as synthetic Aβo, emphasizing the disease relevance. A coreceptor role for mGluR5 is required for dendritic spine loss and transgenic memory impairment. Together, these findings delineate mGluR5 activation as a critical step in Aβo signal transduction with potential for therapeutic intervention.

We also confirmed latency measure stability over time (Figures S3A–S3F). We thank David Euston and Masami Tatsuno for insightful comments on the manuscript and Kenneth D. Harris for supporting recordings from awake animals. We also thank Zak Stinson, Simone Cherry-Delisle, Adam Neumann, Montserrat Villanueva Borbolla, and Hiroe Yamazaki for help with experiments. This work was supported by NSERC (to A.L., A.J.G., B.L.M., and B.K.), AIHS (to A.L., A.J.G., and B.L.M.), and HSRF NF-101773

(P.B.). P.B is a Bolyai fellow. “
“A remarkable feature of sensory perception is the ability to evaluate external stimuli according to momentary demands. This context dependence of sensory perception is reflected in cortical representations of sensory stimuli, which are modulated by behavioral and cognitive states (Gazzaley and Nobre, 2012, Moran and Desimone, 1985, Nicolelis and Fanselow, 2002, UMI-77 Niell and Stryker, click here 2010 and Reynolds and Chelazzi, 2004). While multiple mechanisms probably contribute to context-dependent sensory processing, long-range corticocortical pathways may be particularly important. A prominent feature of sensory cortex is the convergence of feedforward and corticocortical feedback pathways at each stage of sensory processing

(Felleman and Van Essen, 1991). While some have hypothesized that feedback pathways provide important internal and contextual cues that influence sensory perception (Cauller and Kulics, 1991, Engel et al., 2001 and Lamme and Roelfsema, 2000), we know very little about how feedback inputs influence their target regions. In addition to sensory representations, the rhythmic fluctuations of cortical

circuits also exhibit dramatic context-dependent changes. Whereas low-frequency, high-amplitude Linifanib (ABT-869) electroencephalogram/local field potential (EEG/LFP) fluctuations correlate with inattentiveness and immobility, low-amplitude, high-frequency EEG/LFP fluctuations, particularly in the gamma band, correlate with arousal, attention, and behavior (Berger, 1929, Buzsaki, 2006, Fries et al., 2001, Moruzzi and Magoun, 1949 and Poulet and Petersen, 2008). Traditionally, neocortical state changes have been attributed to ascending neuromodulatory systems (Buzsaki et al., 1988, Dringenberg and Vanderwolf, 1997, Jones, 2003, Lee and Dan, 2012, Metherate et al., 1992 and Steriade et al., 1993b). However, considering the relatively slow time course and spatially distributed targets of neuromodulatory systems, it is unclear whether these pathways have permissive or instructive roles in moment-to-moment changes of network states. A recent study demonstrated strong thalamic contributions to cortical state (Poulet et al., 2012), suggesting that glutamatergic inputs may also contribute. Corticocortical feedback projections are well positioned to mediate rapid and specific changes in network dynamics, and yet direct evidence for their roles in modulating network states has not been reported.

We took interest in the regulation of TREK1 by Gi-coupled GPCRs, since several transmitter-gated versions of these are found in the hippocampus (Padgett and Slesinger, 2010). Postsynaptically,

hippocampal GABAB receptors can inhibit calcium channels (Mintz and Bean, 1993), but they are primarily known to enhance the potassium channels that underlie the slow inhibitory postsynaptic potential (IPSP). The slow IPSP is known to involve G protein-coupled inwardly rectifying potassium (Kir3) channels (Lüscher et al., 1997). Baclofen is generally used to study the GABAB response (Dutar and Nicoll, 1988). Using baclofen, Koyrakh and colleagues showed evidences for an additional unidentified GABAB channel target (Koyrakh et al., 2005). Our PCS approach enables us to identify this channel as TREK1. As selleck kinase inhibitor with Kir3 channels, TREK1 is also postsynaptic (Sandoz et al., 2008), where it is complexed with the postsynaptic machinery via interaction with AKAP150 (Sandoz et al., 2006). This is the second case

where a 2P potassium channel has been implicated in GABAergic signaling, since TREK2 appears to mediate a different and much slower IPSP in entorhinal cortex (Deng et al., 2009). These findings suggest that 2P potassium channels may have a broad role in synaptic signaling in the brain. It breaks with the traditional Selleckchem Small molecule library notions that Kir3 channels are the sole targets of postsynaptic GABAB receptors and that 2P-potassium channels serve simply as leak channels in the hippocampus. Our PCS approach offers an affordable and powerful strategy for identifying the molecular basis of unknown ionic currents and for obtaining a pharmacological foothold in multisubunit signaling proteins. Cysteine mutations were introduced into mTREK1 cDNA in the pIRES2EGFP

expression vector using the QuickChange mutagenesis kit (Agilent). The PCR protocol used was 1 cycle (95°, 30 s), 16 cycles (95°, 30 s; 55°, 1 min; 68°, 12 min). TREK1-PCS has been made by PCR and introduced in pIRES2EGFP expression vector. HEK293 Cells were transiently cotransfected using Lipofectamine 2000 (Invitrogen) with TREK1 mutants or TREK1-PCS. For Levetiracetam coexpression, TREK1 or TREK1-PCS are cotransfected with a ratio of 1:3 to 1:5 with 1.6 μg of DNA total per 18-mm-diameter coverslip. Hippocampal neurons were transfected using the calcium phosphate method. Each 12 mm coverslip received 1.1 μg of TREK1-PCS DNA and 0.2 μg of Tomato DNA. HEK293 cells were maintained in DMEM with 5% FBS on poly-L-lysine-coated glass coverslips. Dissociated hippocampal neurons were obtained from postnatal rats (P0-1) and plated at 75,000 cells/coverslip on poly-L-lysine-coated glass coverslips (12 mM). Neurons were maintained in media containing MEM supplemented with 5% fetal bovine serum, B27 (Invitrogen), and GlutaMAX (Invitrogen). HEK293 cell electrophysiology was performed 24–72 hr after transfection solution containing (in mM): 145 mM NaCl, 4 mM KCl, 1 mM MgCl2, 2 mM CaCl2, and 10 mM HEPES.

Socio-economic status was assessed at T1 using a 5 point scale consisting of five variables: educational

level (father/mother), occupation (father/mother), and family income. The internal consistency of this measure is satisfactory (Cronbach’s alpha 0.84; Veenstra et al., 2006). Parental psychopathology (i.e. depression, anxiety, substance abuse, antisocial behaviour, and psychosis) was measured by means of the Brief TRAILS Family History Interview (Ormel et al., 2005), administered at T1. Each syndrome was introduced by a vignette describing its main symptoms and followed by a series of questions to assess lifetime occurrence, professional treatment, and medication use. The scores for substance abuse and antisocial behaviour were used to construct a familial vulnerability index for externalizing selleck chemicals llc disorder. The scores for depression and anxiety disorder were used to construct an index for internalizing disorder. The construction of a familial vulnerability index was based on Kendler et al. (2003), who performed multivariate twin modelling to investigate shared genetic FK228 risk factors for psychiatric and substance use disorders. More information on the construction of familial vulnerability within TRAILS is described elsewhere (Veenstra et al., 2005). For

both internalizing and externalizing disorder, parents were assigned to one of the following categories: (0) = (probably) not; (1) = (probably) yes, (2) = yes and treatment/medication (substance abuse, depression, and anxiety) or picked up by police (antisocial behaviour). In order

to assess alcohol and tobacco use, participants filled out a questionnaire Histone demethylase at both T2 and T3 on the frequency of use in the past month. For tobacco use reported frequency was recoded into non-weekly (0) versus weekly (1), and for alcohol use, the reported frequency was recoded into non-monthly (0) versus monthly use (1). These categories were similar to those used in other studies investigating cannabis use and mental health in young adolescents (e.g. Monshouwer et al., 2006). It was first examined whether non-responders differed from responders on SES (by means of t-test) and gender (by means of Pearson χ2-test). Next, it was examined whether, among the responders, there were differences between cannabis users and non-users with respect to SES, familial vulnerability for internalizing and externalizing behaviour, use of alcohol and tobacco and gender (using Pearson Chi-square analysis for alcohol, tobacco use and gender and t-tests or GLM univariate analysis of variance for SES and familial vulnerability). These analyses were performed in order to determine which variables should be included in the main analyses as covariates.

, 2011b, Lubin et al., 2008, Ma et al., 2009 and Miller and Sweatt, 2007). The near-simultaneous discoveries of a hydroxylated Fulvestrant datasheet form of

5mC (5hmC) (Kriaucionis and Heintz, 2009) and the Ten-eleven translocation (Tet) family of enzymes required for its conversion (Tahiliani et al., 2009) has now offered insight into how these changes in DNA methylation might occur. Specifically, all three Tets (TET1–TET3) have been shown to catalyze the conversion of 5mC to 5hmC as well as its further oxidation into 5-formylcytosine (5fC) and 5-carboxylcytosine (5caC), respectively (He et al., 2011, Ito et al., 2010 and Ito et al., 2011). These modified bases may then function as DNA demethylation intermediates subject to deamination, glycosylase-dependent excision, and repair resulting in a reversion back to unmodified cytosine (Bhutani et al., 2011 and Branco et al., 2012). However, it has now become apparent that 5hmC is not merely a DNA demethylation intermediate but BMN 673 research buy also functions as a stable epigenetic mark enriched within gene bodies, promoters, and transcription factor binding sites, where it may influence gene expression

(Hahn et al., 2013, Mellén et al., 2012 and Szulwach et al., 2011). In the adult brain, alterations in global DNA methylation patterns in response to neuronal activity (Guo et al., 2011a and Miller-Delaney et al., 2012) are at least partially mediated by TET1, which is both necessary and sufficient for demethylation of the fibroblast growth factor 1 (Fgf1) and the brain-derived neurotrophic factor (Bdnf) promoters in response to electroconvulsive shock ( Guo et al., 2011b). Complementary studies have shown that Bdnf is

critical for memory formation ( Bekinschtein et al., 2008 and Mizuno et al., 2000), and its promoter region undergoes rapid demethylation after associative learning in a fear conditioning paradigm in rodents ( Lubin et al., 2008), suggesting the possibility that Tet1 may contribute to memory formation. However, too at present, the role of Tet-mediated regulation of 5hmC and subsequent active DNA demethylation in relation to the expression of neuronal plasticity genes and memory has not been extensively explored, although Zhang et al. recently reported that Tet1 deletion in a knockout mouse model resulted in altered neurogenesis and a deficit in spatial memory in the Morris water maze ( Zhang et al., 2013). In this study, we sought to investigate the role of TET1 enzymatic activity in memory formation, through its ability to promote demethylation and, therefore, gene expression. We found that endogenous TET1 is expressed in neurons throughout the hippocampus and that its transcript levels are regulated by neuronal activity.

Epha3/4pMNΔflox embryos displayed severely defective formation of epaxial sensory pathways (compare Figures 3A–3C and 3D–3F, see also Figures S3G and S3J). At the same time, hypaxial sensory pathways remained unaffected in these mutants ( Figures S3I and 3L). Both the frequency and pattern of these epaxial selleck screening library sensory projection defects were virtually indistinguishable from those observed in Epha3/4null embryos ( Figure 3G). Because in Epha3/4null embryos no alterations in DRG sensory neuron numbers were detected we could rule out that the selective epaxial projection defects in these mutants were caused by loss of a subset of sensory neurons ( Figures

S3N). Moreover, in both Epha3/4pMNΔflox and Epha3/4null embryos the absence of epaxial sensory projections was further accompanied by a consistent increase in diameter of the hypaxial nerves ( Figure 3H and Figures 3I–3L). This suggested that sensory projections that failed to extend epaxially instead grew hypaxially in these mutants ( Figures 3M–3N). We next tested whether these sensory projection defects were accompanied BIBW2992 order by similar defects in epaxial motor projections. Neither Epha3/4pMNΔflox nor Epha3/4null embryos showed absence of epaxial motor projections, thus ruling out that the failure to form epaxial sensory projections was due to the loss of epaxial motor projections (compare Figures 3J and 3L; see also Figures S3H and S3K). We next asked whether loss of epaxial sensory

projections in these mutants could have been caused by hypaxial

misrouting of epaxial motor axons. We tested this by retrogradely tracing of hypaxially projecting motor neurons by focal injection of fluorescent cholera toxin B (CtxB) into hypaxial intercostal nerves. In both control and Epha3/4null animals this effectively labeled hypaxial motor neurons residing within the lateral division of the medial motor column (MMCl) ( Figures S3O–S3Q and S3R–S3T). At the same time, neither in control nor in Epha3/4null embryos were epaxial motor neurons in the medial MMC (MMCm) labeled by hypaxial CtxB injection ( Figures S3O–S3U). Thus, removal of motor axonal EphA3/4 selectively disrupts epaxial sensory projections, without resulting in the hypaxial misrouting of epaxial motor axons ( Figures 3M–3N). In addition to the sensory projection Oxymatrine defects, both Epha3/4null and Epha3/4pMNΔflox mutants display misrouting of epaxial motor axons into DRGs due to loss of repulsive EphA3/4 signaling in motor growth cones (data not shown) ( Gallarda et al., 2008). We therefore asked whether the requirements of EphA3/4 for determining epaxial sensory projections could be uncoupled from their actions in repelling motor growth cones from DRGs. To address this, we tested how sensory projections would develop upon eliminating EphA3/4 repulsive intracellular signaling, while retaining the ability of motor axonal EphAs to engage their putative interaction partners on sensory axons.

Viewing Adriamycin cost a natural movie evokes local brain responses that show synchrony across subjects in a large expanse of cortex, including visual areas in the occipital, ventral temporal, and lateral temporal cortices ( Hasson et al., 2004, Bartels and Zeki, 2004 and Sabuncu et al., 2010). In contrast to earlier univariate analyses of local synchrony, we took a multivariate approach to analyze the time-varying patterns of response evoked by this rich, dynamic stimulus. We reasoned that in the brains of two individuals viewing the same dynamic visual stimulus, such as a full-length

action movie, the trajectories of VT response-pattern vectors over time reflect similar visual information, but the coordinate systems for their respective representational spaces, in which each dimension is one voxel, are poorly aligned. Hyperalignment uses Procrustean transformations ( Schönemann, 1966) iteratively over pairs of subjects to derive a group coordinate system

ABT-199 solubility dmso in which subjects’ vector trajectories are in optimal alignment. The Procrustean transformation is an orthogonal transformation (rotations and reflections) that minimizes the Euclidean distance between two sets of paired vectors. After hyperalignment, we reduced the dimensionality of the common space by performing a principal components analysis (PCA) and determined the subspace that is sufficient to capture the full range of response-pattern distinctions. We tested the validity of the common mafosfamide model by performing between-subject MVP classification of responses to a wide range of visual stimuli—time segments from the movie and still images of seven categories of faces and objects and six animal species. For between-subject classification (BSC), the response vectors for one subject were classified using a classifier model based on other subjects’ response vectors. We compared BSC performance for response vectors that had been transformed into the common model space to BSC for data that were aligned across subjects based on anatomy and to within-subject classification (WSC), in which the response vectors for a subject were

classified using an individually tailored classifier model based on response vectors from the same subject. Results showed that BSC accuracies for response-pattern vectors in common model space were markedly higher than BSC accuracies for anatomically aligned response-pattern vectors and equivalent to WSC accuracies. More than 20 dimensions were needed to achieve this level of accuracy. Here we present a common model space with 35 dimensions. Thus, the representational space in VT cortex can be modeled with response-tuning functions that are common across subjects. These response-tuning functions are associated with cortical topographies that serve as basis patterns for modeling patterns of response to stimuli and can be examined in each individual’s VT cortex.